class TestNSGA2(OptTest):
name = "quadratic"
optName = "NSGA2"
def objfunc(self, xdict):
x = xdict["x"]
y = xdict["y"]
funcs = {}
funcs["obj1"] = (x - 0.0) ** 2 + (y - 0.0) ** 2
funcs["obj2"] = (x - 1.0) ** 2 + (y - 1.0) ** 2
fail = False
return funcs, fail
def setup_optProb(self):
# Instantiate Optimization Problem
self.optProb = Optimization("quadratic", self.objfunc)
self.optProb.addVar("x", value=0, lower=-600, upper=600)
self.optProb.addVar("y", value=0, lower=-600, upper=600)
self.optProb.addObj("obj1")
self.optProb.addObj("obj2")
def test_opt(self):
self.setup_optProb()
# 300 generations will find x=(0,0), 200 or less will find x=(1,1)
optOptions = {"maxGen": 200}
sol = self.optimize(optOptions=optOptions)
tol = 1e-2
assert_allclose(sol.variables["x"][0].value, 1.0, atol=tol, rtol=tol)
assert_allclose(sol.variables["y"][0].value, 1.0, atol=tol, rtol=tol)
def test_autorefine(self):
# Optimization Object
optProb = Optimization("TP109 Constraint Problem", objfunc)
# Design Variables (Removed infinite bounds for ALPSO)
lower = [0.0, 0.0, -0.55, -0.55, 196, 196, 196, -400, -400]
upper = [2000, 2000, 0.55, 0.55, 252, 252, 252, 800, 800]
value = [0, 0, 0, 0, 0, 0, 0, 0, 0]
optProb.addVarGroup("xvars", 9, lower=lower, upper=upper, value=value)
# Constraints
lower = [0, 0, 0, 0, 0, 0, 0, 0]
upper = [None, None, 0, 0, 0, 0, 0, 0]
if not USE_LINEAR:
lower.extend([0, 0])
upper.extend([None, None])
optProb.addConGroup("con", len(lower), lower=lower, upper=upper)
# And the 2 linear constriants
if USE_LINEAR:
jac = np.zeros((1, 9))
jac[0, 3] = 1.0
jac[0, 2] = -1.0
optProb.addConGroup("lin_con",
1,
lower=-0.55,
upper=0.55,
wrt=["xvars"],
jac={"xvars": jac},
linear=True)
# Objective
optProb.addObj("obj")
# Check optimization problem:
# optProb.printSparsity()
# Global Optimizer: ALPSO
try:
opt1 = OPT("ALPSO")
except Error:
raise unittest.SkipTest("Optimizer not available:", "ALPSO")
# Get first Solution
sol1 = opt1(optProb)
# Now run the previous solution with SNOPT
try:
opt2 = OPT("SNOPT")
except Error:
raise unittest.SkipTest("Optimizer not available:", "SNOPT")
sol2 = opt2(sol1)
# Check Solution
assert_allclose(sol2.objectives["obj"].value,
0.536206927538e04,
atol=1e-2,
rtol=1e-2)
def test_sens(self):
termcomp = TerminateComp(max_sens=3)
optProb = Optimization("Paraboloid", termcomp.objfunc)
optProb.addVarGroup("x", 1, type="c", lower=-50.0, upper=50.0, value=0.0)
optProb.addVarGroup("y", 1, type="c", lower=-50.0, upper=50.0, value=0.0)
optProb.finalizeDesignVariables()
optProb.addObj("obj")
optProb.addConGroup("con", 1, lower=-15.0, upper=-15.0, wrt=["x", "y"], linear=True, jac=con_jac)
test_name = "SNOPT_user_termination_sens"
optOptions = {
"Print file": "{}.out".format(test_name),
"Summary file": "{}_summary.out".format(test_name),
}
try:
opt = SNOPT(options=optOptions)
except Error:
raise unittest.SkipTest("Optimizer not available: SNOPT")
sol = opt(optProb, sens=termcomp.sens)
self.assertEqual(termcomp.sens_count, 4)
# Exit code for user requested termination.
self.assertEqual(sol.optInform["value"], 71)
def test_opt(self):
# Optimization Object
optProb = Optimization('Paraboloid', objfunc)
# Design Variables
optProb.addVarGroup('x', 1, type='c', lower=-50.0, upper=50.0, value=0.0)
optProb.addVarGroup('y', 1, type='c', lower=-50.0, upper=50.0, value=0.0)
optProb.finalizeDesignVariables()
# Objective
optProb.addObj('obj')
# Equality Constraint
optProb.addConGroup('con', 1, lower=-15.0, upper=-15.0, wrt=['x', 'y'], linear=True, jac=con_jac)
# Check optimization problem:
print(optProb)
# Optimizer
try:
opt = SNOPT(optOptions = {'Major feasibility tolerance' : 1e-1})
except:
raise unittest.SkipTest('Optimizer not available: SNOPT')
sol = opt(optProb, sens=sens)
# Check Solution 7.166667, -7.833334
self.assertAlmostEqual(sol.variables['x'][0].value, 7.166667, places=6)
self.assertAlmostEqual(sol.variables['y'][0].value, -7.833333, places=6)
def setup_optProb(self, nObj=1, nDV=[4], nCon=[2], xScale=[1.0], objScale=[1.0], conScale=[1.0], offset=[0.0]):
"""
This function sets up a general optimization problem, with arbitrary
DVs, constraints and objectives.
Arbitrary scaling for the various parameters can also be specified.
"""
self.nObj = nObj
self.nDV = nDV
self.nCon = nCon
self.xScale = xScale
self.objScale = objScale
self.conScale = conScale
self.offset = offset
# Optimization Object
self.optProb = Optimization("Configurable Test Problem", self.objfunc)
self.x0 = {}
# Design Variables
for iDV in range(len(nDV)):
n = nDV[iDV]
lower = np.random.uniform(-5, 2, n)
upper = np.random.uniform(5, 20, n)
x0 = np.random.uniform(lower, upper)
dvName = "x{}".format(iDV)
self.x0[dvName] = x0
self.optProb.addVarGroup(
dvName,
n,
lower=lower,
upper=upper,
value=x0,
scale=xScale[iDV],
offset=offset[iDV],
)
# Constraints
for iCon in range(len(nCon)):
nc = nCon[iCon]
lower = np.random.uniform(-5, 2, nc)
upper = np.random.uniform(5, 6, nc)
self.optProb.addConGroup(
"con_{}".format(iCon),
nc,
lower=lower,
upper=upper,
scale=conScale[iCon],
)
# Objective
for iObj in range(nObj):
self.optProb.addObj("obj_{}".format(iObj), scale=objScale[iObj])
# Finalize
self.optProb.printSparsity()
# run optimization
# we don't care about outputs, but this performs optimizer-specific re-ordering
# of constraints so we need this to test mappings
opt = OPT("slsqp", options={"IFILE": "optProb_SLSQP.out"})
opt(self.optProb, "FD")
def test_autorefine(self):
# Optimization Object
optProb = Optimization('TP109 Constraint Problem', objfunc)
# Design Variables (Removed infinite bounds for ALPSO)
lower = [0.0, 0.0, -0.55, -0.55, 196, 196, 196, -400, -400]
upper = [2000, 2000, 0.55, 0.55, 252, 252, 252, 800, 800]
value = [0, 0, 0, 0, 0, 0, 0, 0, 0]
optProb.addVarGroup('xvars', 9, lower=lower, upper=upper, value=value)
# Constraints
lower = [0, 0, 0, 0, 0, 0, 0, 0]
upper = [None, None, 0, 0, 0, 0, 0, 0]
if not USE_LINEAR:
lower.extend([0, 0])
upper.extend([None, None])
optProb.addConGroup('con', len(lower), lower=lower, upper=upper)
# And the 2 linear constriants
if USE_LINEAR:
jac = numpy.zeros((1, 9))
jac[0, 3] = 1.0
jac[0, 2] = -1.0
optProb.addConGroup('lin_con',
1,
lower=-.55,
upper=0.55,
wrt=['xvars'],
jac={'xvars': jac},
linear=True)
# Objective
optProb.addObj('obj')
# Check optimization problem:
#optProb.printSparsity()
# Global Optimizer: ALPSO
try:
opt1 = OPT('ALPSO')
except:
raise unittest.SkipTest('Optimizer not available:', 'ALPSO')
# Get first Solution
sol1 = opt1(optProb)
# Now run the previous solution with SNOPT
try:
opt2 = OPT('SNOPT')
except:
raise unittest.SkipTest('Optimizer not available:', 'SNOPT')
sol2 = opt2(sol1)
# Check Solution
self.assertAlmostEqual(sol2.objectives['obj'].value,
0.536206927538e+04,
places=2)
def setup_optProb(self):
# Instantiate Optimization Problem
self.optProb = Optimization("quadratic", self.objfunc)
self.optProb.addVar("x", value=0, lower=-600, upper=600)
self.optProb.addVar("y", value=0, lower=-600, upper=600)
self.optProb.addObj("obj1")
self.optProb.addObj("obj2")
def test_opt(self):
# Optimization Object
optProb = Optimization("Paraboloid", objfunc)
# Design Variables
optProb.addVarGroup("x",
1,
varType="c",
lower=-50.0,
upper=50.0,
value=0.0)
optProb.addVarGroup("y",
1,
varType="c",
lower=-50.0,
upper=50.0,
value=0.0)
# Objective
optProb.addObj("obj")
# Equality Constraint
optProb.addConGroup("con",
1,
lower=-15.0,
upper=-15.0,
wrt=["x", "y"],
linear=True,
jac=con_jac)
# Check optimization problem:
print(optProb)
test_name = "bugfix_SNOPT_test_opt"
optOptions = {
"Major feasibility tolerance": 1e-1,
"Print file": "{}.out".format(test_name),
"Summary file": "{}_summary.out".format(test_name),
}
# Optimizer
try:
opt = SNOPT(options=optOptions)
except Error:
raise unittest.SkipTest("Optimizer not available: SNOPT")
sol = opt(optProb, sens=sens)
# Check Solution 7.166667, -7.833334
tol = 1e-6
assert_allclose(sol.variables["x"][0].value,
7.166667,
atol=tol,
rtol=tol)
assert_allclose(sol.variables["y"][0].value,
-7.833333,
atol=tol,
rtol=tol)
def __call__(self, optimizer, options=None):
""" Run optimization """
system = self._system
variables = self._variables
opt_prob = OptProblem('Optimization', self.obj_func)
for dv_name in variables['dv'].keys():
dv_id = variables['dv'][dv_name]['ID']
value = variables['dv'][dv_name]['value']
lower = variables['dv'][dv_name]['lower']
upper = variables['dv'][dv_name]['upper']
size = system(dv_id).size
opt_prob.addVarGroup(dv_name, size, value=value,
lower=lower, upper=upper)
opt_prob.finalizeDesignVariables()
for func_name in variables['func'].keys():
lower = variables['func'][func_name]['lower']
upper = variables['func'][func_name]['upper']
if lower is None and upper is None:
opt_prob.addObj(func_name)
else:
opt_prob.addCon(func_name, lower=lower, upper=upper)
if options is None:
options = {}
opt = Optimizer(optimizer, options=options)
sol = opt(opt_prob, sens=self.sens_func)
print sol
def __call__(self, optimizer, options=None):
""" Run optimization """
system = self._system
variables = self._variables
opt_prob = OptProblem('Optimization', self.obj_func)
for dv_name in variables['dv'].keys():
dv_id = variables['dv'][dv_name]['ID']
value = variables['dv'][dv_name]['value']
lower = variables['dv'][dv_name]['lower']
upper = variables['dv'][dv_name]['upper']
size = system(dv_id).size
opt_prob.addVarGroup(dv_name,
size,
value=value,
lower=lower,
upper=upper)
opt_prob.finalizeDesignVariables()
for func_name in variables['func'].keys():
lower = variables['func'][func_name]['lower']
upper = variables['func'][func_name]['upper']
if lower is None and upper is None:
opt_prob.addObj(func_name)
else:
opt_prob.addCon(func_name, lower=lower, upper=upper)
if options is None:
options = {}
opt = Optimizer(optimizer, options=options)
sol = opt(opt_prob, sens=self.sens_func)
print sol
def optimize(self, optName, tol, optOptions={}):
# Optimization Object
optProb = Optimization("TP109 Constraint Problem", objfunc)
# Design Variables
lower = [0.0, 0.0, -0.55, -0.55, 196, 196, 196, -400, -400]
upper = [None, None, 0.55, 0.55, 252, 252, 252, 800, 800]
value = [0, 0, 0, 0, 0, 0, 0, 0, 0]
optProb.addVarGroup("xvars", 9, lower=lower, upper=upper, value=value)
# Constraints
lower = [0, 0, 0, 0, 0, 0, 0, 0]
upper = [None, None, 0, 0, 0, 0, 0, 0]
if not USE_LINEAR:
lower.extend([0, 0])
upper.extend([None, None])
optProb.addConGroup("con", len(lower), lower=lower, upper=upper)
# And the 2 linear constriants
if USE_LINEAR:
jac = np.zeros((1, 9))
jac[0, 3] = 1.0
jac[0, 2] = -1.0
optProb.addConGroup("lin_con",
1,
lower=-0.55,
upper=0.55,
wrt=["xvars"],
jac={"xvars": jac},
linear=True)
# Objective
optProb.addObj("obj")
# Check optimization problem:
# optProb.printSparsity()
# Optimizer
try:
opt = OPT(optName, options=optOptions)
except Error:
raise unittest.SkipTest("Optimizer not available:", optName)
# Solution
sol = opt(optProb, sens="CS")
# Check Solution
assert_allclose(sol.objectives["obj"].value,
0.536206927538e04,
atol=tol,
rtol=tol)
def setup_optProb(self, xScale=1.0, objScale=1.0, conScale=1.0, offset=0.0):
# Optimization Object
self.optProb = Optimization("HS071 Constraint Problem", self.objfunc, sens=self.sens)
# Design Variables
x0 = [1.0, 5.0, 5.0, 1.0]
self.optProb.addVarGroup("xvars", 4, lower=1, upper=5, value=x0, scale=xScale, offset=offset)
# Constraints
self.optProb.addConGroup("con", 2, lower=[25, 40], upper=[None, 40], scale=conScale)
# Objective
self.optProb.addObj("obj", scale=objScale)
def setup_optProb(self):
# Optimization Object
self.optProb = Optimization("Rosenbrock Problem", self.objfunc)
np.random.seed(10)
value = np.random.normal(size=self.N)
lower = np.ones(self.N) * -50
upper = np.ones(self.N) * 50
self.optProb.addVarGroup("xvars", self.N, lower=lower, upper=upper, value=value)
# Objective
self.optProb.addObj("obj")
def optimize(self, optName, optOptions={}, places=2):
# Optimization Object
optProb = Optimization('TP109 Constraint Problem', objfunc)
# Design Variables
lower = [0.0, 0.0, -0.55, -0.55, 196, 196, 196, -400, -400]
upper = [None, None, 0.55, 0.55, 252, 252, 252, 800, 800]
value = [0, 0, 0, 0, 0, 0, 0, 0, 0]
optProb.addVarGroup('xvars', 9, lower=lower, upper=upper, value=value)
# Constraints
lower = [0, 0, 0, 0, 0, 0, 0, 0]
upper = [None, None, 0, 0, 0, 0, 0, 0]
if not USE_LINEAR:
lower.extend([0, 0])
upper.extend([None, None])
optProb.addConGroup('con', len(lower), lower=lower, upper=upper)
# And the 2 linear constriants
if USE_LINEAR:
jac = numpy.zeros((1, 9))
jac[0, 3] = 1.0
jac[0, 2] = -1.0
optProb.addConGroup('lin_con',
1,
lower=-.55,
upper=0.55,
wrt=['xvars'],
jac={'xvars': jac},
linear=True)
# Objective
optProb.addObj('obj')
# Check optimization problem:
#optProb.printSparsity()
# Optimizer
try:
opt = OPT(optName, options=optOptions)
except:
raise unittest.SkipTest('Optimizer not available:', optName)
# Solution
sol = opt(optProb, sens='CS')
# Check Solution
self.assertAlmostEqual(sol.objectives['obj'].value,
0.536206927538e+04,
places=places)
def optimize(self, optName, tol, optOptions={}, storeHistory=False, hotStart=None):
self.nf = 0 # number of function evaluations
self.ng = 0 # number of gradient evaluations
# Optimization Object
optProb = Optimization("HS15 Constraint Problem", self.objfunc)
# Design Variables
lower = [-5.0, -5.0]
upper = [0.5, 5.0]
value = [-2, 1.0]
optProb.addVarGroup("xvars", 2, lower=lower, upper=upper, value=value)
# Constraints
lower = [1.0, 0.0]
upper = [None, None]
optProb.addConGroup("con", 2, lower=lower, upper=upper)
# Objective
optProb.addObj("obj")
# Check optimization problem:
print(optProb)
# Optimizer
try:
opt = OPT(optName, options=optOptions)
except Error:
raise unittest.SkipTest("Optimizer not available:", optName)
# Solution
if storeHistory is not None:
if storeHistory is True:
self.histFileName = "%s_hs015_Hist.hst" % (optName.lower())
elif isinstance(storeHistory, str):
self.histFileName = storeHistory
else:
self.histFileName = None
sol = opt(optProb, sens=self.sens, storeHistory=self.histFileName, hotStart=hotStart)
# Test printing solution to screen
print(sol)
# Check Solution
self.fStar1 = 306.5
self.fStar2 = 360.379767
self.xStar1 = (0.5, 2.0)
self.xStar2 = (-0.79212322, -1.26242985)
dv = sol.getDVs()
sol_xvars = [sol.variables["xvars"][i].value for i in range(2)]
assert_allclose(sol_xvars, dv["xvars"], atol=tol, rtol=tol)
# we check either optimum via try/except
try:
assert_allclose(sol.objectives["obj"].value, self.fStar1, atol=tol, rtol=tol)
assert_allclose(dv["xvars"], self.xStar1, atol=tol, rtol=tol)
except AssertionError:
assert_allclose(sol.objectives["obj"].value, self.fStar2, atol=tol, rtol=tol)
assert_allclose(dv["xvars"], self.xStar2, atol=tol, rtol=tol)
def optimize(self, optName, optOptions={}, storeHistory=False, places=5):
# Optimization Object
optProb = Optimization('HS071 Constraint Problem', objfunc)
# Design Variables
x0 = [1.0, 5.0, 5.0, 1.0]
optProb.addVarGroup('xvars', 4, lower=1, upper=5, value=x0)
# Constraints
optProb.addConGroup('con', 2, lower=[25, 40], upper=[None, 40])
# Objective
optProb.addObj('obj')
# Optimizer
try:
opt = OPT(optName, options=optOptions)
except:
raise unittest.SkipTest('Optimizer not available:', optName)
sol = opt(optProb, sens=sens)
# Check Solution
self.assertAlmostEqual(sol.objectives['obj'].value, 17.0140172, places=places)
self.assertAlmostEqual(sol.variables['xvars'][0].value, 1.0, places=places)
self.assertAlmostEqual(sol.variables['xvars'][1].value, 4.743, places=places)
self.assertAlmostEqual(sol.variables['xvars'][2].value, 3.82115, places=places)
self.assertAlmostEqual(sol.variables['xvars'][3].value, 1.37941, places=places)
if hasattr(sol, 'lambdaStar'):
self.assertAlmostEqual(sol.lambdaStar['con'][0], 0.55229366, places=places)
self.assertAlmostEqual(sol.lambdaStar['con'][1], -0.16146857, places=places)
def test_opt_bug1(self):
# Due to a new feature, there is a TypeError when you optimize a model without a constraint.
optProb = Optimization("Paraboloid", objfunc_no_con)
# Design Variables
optProb.addVarGroup("x",
1,
varType="c",
lower=-50.0,
upper=50.0,
value=0.0)
optProb.addVarGroup("y",
1,
varType="c",
lower=-50.0,
upper=50.0,
value=0.0)
# Objective
optProb.addObj("obj")
test_name = "bugfix_SNOPT_bug1"
optOptions = {
"Major feasibility tolerance": 1e-1,
"Print file": "{}.out".format(test_name),
"Summary file": "{}_summary.out".format(test_name),
}
# Optimizer
try:
opt = SNOPT(options=optOptions)
except Error:
raise unittest.SkipTest("Optimizer not available: SNOPT")
opt(optProb, sens=sens)
def optimize(self, optName, optOptions={}, storeHistory=False, places=5):
# Optimization Object
optProb = Optimization('HS15 Constraint Problem', self.objfunc)
# Design Variables
lower = [-5.0, -5.0]
upper = [0.5, 5.0]
value = [-2, 1.0]
optProb.addVarGroup('xvars', 2, lower=lower, upper=upper, value=value)
# Constraints
lower = [1.0, 0.0]
upper = [None, None]
optProb.addConGroup('con', 2, lower=lower, upper=upper)
# Objective
optProb.addObj('obj')
# Check optimization problem:
# print(optProb)
# Optimizer
try:
opt = OPT(optName, options=optOptions)
except:
raise unittest.SkipTest('Optimizer not available:', optName)
# Solution
if storeHistory:
histFileName = '%s_hs015_Hist.hst' % (optName.lower())
else:
histFileName = None
sol = opt(optProb, sens=self.sens, storeHistory=histFileName)
# Test printing solution to screen
print(sol)
# Check Solution
fobj = sol.objectives['obj'].value
diff = np.min(np.abs([fobj - 306.5, fobj - 360.379767]))
self.assertAlmostEqual(diff, 0.0, places=places)
xstar1 = (0.5, 2.0)
xstar2 = (-0.79212322, -1.26242985)
x1 = sol.variables['xvars'][0].value
x2 = sol.variables['xvars'][1].value
dv = sol.getDVs()
self.assertAlmostEqual(x1, dv['xvars'][0], places=10)
self.assertAlmostEqual(x2, dv['xvars'][1], places=10)
diff = np.min(np.abs([xstar1[0] - x1, xstar2[0] - x1]))
self.assertAlmostEqual(diff, 0.0, places=places)
diff = np.min(np.abs([xstar1[1] - x2, xstar2[1] - x2]))
self.assertAlmostEqual(diff, 0.0, places=places)
def setup_optProb(self, sparse=True):
# set N
if sparse:
self.N = 10000
else:
self.N = 500
# Optimization Object
self.optProb = Optimization("large and sparse",
self.objfunc,
sens=self.sens)
# Design Variables
self.optProb.addVar("x", lower=-100, upper=150, value=0)
self.optProb.addVarGroup("y",
self.N,
lower=-10 - np.arange(self.N),
upper=np.arange(self.N),
value=0)
self.optProb.addVarGroup("z",
2 * self.N,
upper=np.arange(2 * self.N),
lower=-100 - np.arange(2 * self.N),
value=0)
# Constraints
self.optProb.addCon("con1", upper=100, wrt=["x"])
self.optProb.addCon("con2", upper=100)
self.optProb.addCon("con3", lower=4, wrt=["x", "z"])
xJac = np.ones((self.N, 1))
if sparse:
yJac = scipy.sparse.spdiags(np.ones(self.N), 0, self.N, self.N)
else:
yJac = np.eye(self.N)
self.optProb.addConGroup(
"lincon",
self.N,
lower=2 - 3 * np.arange(self.N),
linear=True,
wrt=["x", "y"],
jac={
"x": xJac,
"y": yJac
},
)
self.optProb.addObj("obj")
def test_obj(self):
termcomp = TerminateComp(max_obj=2)
optProb = Optimization("Paraboloid", termcomp.objfunc)
optProb.addVarGroup("x",
1,
varType="c",
lower=-50.0,
upper=50.0,
value=0.0)
optProb.addVarGroup("y",
1,
varType="c",
lower=-50.0,
upper=50.0,
value=0.0)
optProb.addObj("obj")
optProb.addConGroup("con",
1,
lower=-15.0,
upper=-15.0,
wrt=["x", "y"],
linear=True,
jac=con_jac)
test_name = "SNOPT_user_termination_obj"
optOptions = {
"Print file": f"{test_name}.out",
"Summary file": f"{test_name}_summary.out",
}
try:
opt = SNOPT(options=optOptions)
except Error as e:
if "There was an error importing" in e.message:
raise unittest.SkipTest("Optimizer not available: SNOPT")
raise e
sol = opt(optProb, sens=termcomp.sens)
self.assertEqual(termcomp.obj_count, 3)
# Exit code for user requested termination.
self.assertEqual(sol.optInform["value"], 71)
def test_opt_bug_print_2con(self):
# Optimization Object
optProb = Optimization("Paraboloid", objfunc_2con)
# Design Variables
optProb.addVarGroup("x", 1, varType="c", lower=-50.0, upper=50.0, value=0.0)
optProb.addVarGroup("y", 1, varType="c", lower=-50.0, upper=50.0, value=0.0)
# Objective
optProb.addObj("obj")
con_jac2 = {}
con_jac2["x"] = -np.ones((2, 1))
con_jac2["y"] = np.ones((2, 1))
con_jac3 = {}
con_jac3["x"] = -np.ones((3, 1))
con_jac3["y"] = np.ones((3, 1))
# Equality Constraint
optProb.addConGroup("con", 2, lower=-15.0, upper=-15.0, wrt=["x", "y"], linear=True, jac=con_jac2)
optProb.addConGroup("con2", 3, lower=-15.0, upper=-15.0, wrt=["x", "y"], linear=True, jac=con_jac3)
# Check optimization problem:
print(optProb)
test_name = "bugfix_SNOPT_bug_print_2con"
optOptions = {
"Major feasibility tolerance": 1e-1,
"Print file": f"{test_name}.out",
"Summary file": f"{test_name}_summary.out",
}
# Optimizer
try:
opt = SNOPT(options=optOptions)
except Error as e:
if "There was an error importing" in e.message:
raise unittest.SkipTest("Optimizer not available: SNOPT")
raise e
sol = opt(optProb, sens=sens)
print(sol)
def pyopt_truss(truss, optimizer='snopt', options={}):
'''
Take the given problem and optimize it with the given optimizer
from the pyOptSparse library of optimizers.
'''
# Import the optimization problem
from pyoptsparse import Optimization, OPT
class pyOptWrapper:
def __init__(self, truss):
self.truss = truss
def objcon(self, x):
fail, obj, con = self.truss.evalObjCon(x['x'])
funcs = {'objective': obj, 'con': con}
return funcs, fail
def gobjcon(self, x, funcs):
g = np.zeros(x['x'].shape)
A = np.zeros((1, x['x'].shape[0]))
fail = self.truss.evalObjConGradient(x['x'], g, A)
sens = {'objective': {'x': g}, 'con': {'x': A}}
return sens, fail
# Set the design variables
wrap = pyOptWrapper(truss)
prob = Optimization('Truss', wrap.objcon)
# Determine the initial variable values and their lower/upper
# bounds in the design problem
n = len(truss.conn)
x0 = np.zeros(n)
lower = np.zeros(n)
upper = np.zeros(n)
truss.getVarsAndBounds(x0, lower, upper)
# Set the variable bounds and initial values
prob.addVarGroup('x', n, value=x0, lower=lower, upper=upper)
# Set the constraints
prob.addConGroup('con', 1, lower=0.0, upper=0.0)
# Add the objective
prob.addObj('objective')
# Optimize the problem
try:
opt = OPT(optimizer, options=options)
sol = opt(prob, sens=wrap.gobjcon)
except:
opt = None
sol = None
return opt, prob, sol
def setup_optProb(self):
# Optimization Object
self.optProb = Optimization("HS15 Constraint Problem", self.objfunc)
# Design Variables
lower = [-5.0, -5.0]
upper = [0.5, 5.0]
value = [-2, 1.0]
self.optProb.addVarGroup("xvars",
2,
lower=lower,
upper=upper,
value=value)
# Constraints
lower = [1.0, 0.0]
upper = [None, None]
self.optProb.addConGroup("con", 2, lower=lower, upper=upper)
# Objective
self.optProb.addObj("obj")
def test_opt(self):
# Instantiate Optimization Problem
optProb = Optimization("Rosenbrock function", objfunc)
optProb.addVar("x", "c", value=0, lower=-600, upper=600)
optProb.addVar("y", "c", value=0, lower=-600, upper=600)
optProb.addObj("obj1")
optProb.addObj("obj2")
# 300 generations will find x=(0,0), 200 or less will find x=(1,1)
options = {"maxGen": 200}
# Optimizer
try:
opt = NSGA2(options=options)
except Error:
raise unittest.SkipTest("Optimizer not available:", "NSGA2")
sol = opt(optProb)
# Check Solution
tol = 1e-2
assert_allclose(sol.variables["x"][0].value, 1.0, atol=tol, rtol=tol)
assert_allclose(sol.variables["y"][0].value, 1.0, atol=tol, rtol=tol)
def setup_optProb(self):
# Optimization Object
self.optProb = Optimization("TP109 Constraint Problem", self.objfunc)
# Design Variables
lower = [0.0, 0.0, -0.55, -0.55, 196, 196, 196, -400, -400]
upper = [None, None, 0.55, 0.55, 252, 252, 252, 800, 800]
value = [0, 0, 0, 0, 0, 0, 0, 0, 0]
self.optProb.addVarGroup("xvars",
9,
lower=lower,
upper=upper,
value=value)
# Constraints
lower = [0, 0, 0, 0, 0, 0, 0, 0]
upper = [None, None, 0, 0, 0, 0, 0, 0]
if not USE_LINEAR:
lower.extend([0, 0])
upper.extend([None, None])
self.optProb.addConGroup("con", len(lower), lower=lower, upper=upper)
# And the 2 linear constriants
if USE_LINEAR:
jac = np.zeros((1, 9))
jac[0, 3] = 1.0
jac[0, 2] = -1.0
self.optProb.addConGroup("lin_con",
1,
lower=-0.55,
upper=0.55,
wrt=["xvars"],
jac={"xvars": jac},
linear=True)
# Objective
self.optProb.addObj("obj")
def setup_optProb(self, sparse=True):
# set N
if sparse:
self.N = 10000
else:
self.N = 500
# Optimization Object
self.optProb = Optimization("large and sparse", self.objfunc, sens=self.sens)
# Design Variables
self.optProb.addVar("x", lower=-100, upper=150, value=0)
self.optProb.addVarGroup("y", self.N, lower=-10 - np.arange(self.N), upper=np.arange(self.N), value=0)
self.optProb.addVarGroup(
"z", 2 * self.N, upper=np.arange(2 * self.N), lower=-100 - np.arange(2 * self.N), value=0
)
# Constraints
self.optProb.addCon("con1", upper=100, wrt=["x"])
self.optProb.addCon("con2", upper=100)
self.optProb.addCon("con3", lower=4, wrt=["x", "z"])
xJac = np.ones((self.N, 1))
if sparse:
rows_cols = np.array([i for i in range(0, self.N)]).astype(int)
yJac = {"coo": [rows_cols, rows_cols, np.ones(self.N)], "shape": [self.N, self.N]}
else:
yJac = np.eye(self.N)
self.optProb.addConGroup(
"lincon",
self.N,
lower=2 - 3 * np.arange(self.N),
linear=True,
wrt=["x", "y"],
jac={"x": xJac, "y": yJac},
)
self.optProb.addObj("obj")
def optimize(self, x0, alg='IPOPT', options={}):
opt = {}
opt.update(options)
def objfun(xdict):
V = xdict['V']
b = xdict['b']
c = xdict['c']
al = xdict['al']
A, fail = self.ll(V, b, c, al)
funcs= {
'obj':10000. if fail else self.DoverL(V, b, c, al, A)
}
return funcs, fail
optProb = Optimization('llOpt', objfun)
ub = self.get_vars(self.bounds.ub, dic=True)
lb = self.get_vars(self.bounds.lb, dic=True)
x0 = self.get_vars(x0, dic=True)
optProb.addVar('V', upper=ub['V'], lower=lb['V'], value=x0['V'])
optProb.addVar('b', upper=ub['b'], lower=lb['b'], value=x0['b'])
optProb.addVarGroup('c', self.N_th, upper=ub['c'], lower=lb['c'], value=x0['c'])
optProb.addVarGroup('al', self.N_th, upper=ub['al'], lower=lb['al'], value=x0['al'])
optProb.addObj('obj')
if alg== "IPOPT":
opt = OPT(alg, options=options)
sol = opt(optProb, sens='FD')
else:
raise NotImplementedError(f"No routine for algorithm {alg}")
D = dict(
al = [a.value for a in sol.variables['al']],
c = [a.value for a in sol.variables['c']],
b = sol.variables['b'][0].value,
V = sol.variables['V'][0].value
)
x = self.set_vars(D)[0]
return x, sol
def optimize(self, optName, tol, optOptions={}, storeHistory=False, hotStart=None):
self.nf = 0 # number of function evaluations
self.ng = 0 # number of gradient evaluations
# Optimization Object
optProb = Optimization("Rosenbrock Problem", self.objfunc)
n = 4 # Number of design variables
np.random.seed(10)
value = np.random.normal(size=n)
lower = np.ones(n) * -50
upper = np.ones(n) * 50
optProb.addVarGroup("xvars", n, lower=lower, upper=upper, value=value)
# Objective
optProb.addObj("obj")
# Check optimization problem:
print(optProb)
# Optimizer
try:
opt = OPT(optName, options=optOptions)
except Error:
raise unittest.SkipTest("Optimizer not available:", optName)
# Solution
if storeHistory is not None:
if storeHistory is True:
self.histFileName = "%s_Rsbrk_Hist.hst" % (optName.lower())
elif isinstance(storeHistory, str):
self.histFileName = storeHistory
else:
self.histFileName = None
sol = opt(optProb, sens=self.sens, storeHistory=self.histFileName, hotStart=hotStart)
# Test printing solution to screen
print(sol)
# Check Solution
self.fStar1 = 0.0
self.xStar1 = np.ones(n)
dv = sol.getDVs()
sol_xvars = [sol.variables["xvars"][i].value for i in range(n)]
assert_allclose(sol_xvars, dv["xvars"], atol=tol, rtol=tol)
assert_allclose(dv["xvars"], self.xStar1, atol=tol, rtol=tol)
if optName == "SNOPT" and opt.version != "7.7.7":
assert_allclose(sol.objectives["obj"].value, self.fStar1, atol=tol, rtol=tol)
else:
assert_allclose(sol.fStar, self.fStar1, atol=tol, rtol=tol)
def test_opt_bug1(self):
# Due to a new feature, there is a TypeError when you optimize a model without a constraint.
optProb = Optimization('Paraboloid', objfunc_no_con)
# Design Variables
optProb.addVarGroup('x', 1, type='c', lower=-50.0, upper=50.0, value=0.0)
optProb.addVarGroup('y', 1, type='c', lower=-50.0, upper=50.0, value=0.0)
optProb.finalizeDesignVariables()
# Objective
optProb.addObj('obj')
# Optimizer
try:
opt = SNOPT(optOptions = {'Major feasibility tolerance' : 1e-1})
except:
raise unittest.SkipTest('Optimizer not available: SNOPT')
sol = opt(optProb, sens=sens)
def optimize(self, optName, optOptions={}, storeHistory=False):
# Optimization Object
optProb = Optimization('HS15 Constraint Problem', self.objfunc)
# Design Variables
lower = [-5, -5]
upper = [0.5, 5]
value = [-2, 1]
optProb.addVarGroup('xvars', 2, lower=lower, upper=upper, value=value)
# Constraints
lower = [1, 0]
upper = [None, None]
optProb.addConGroup('con', 2, lower=lower, upper=upper)
# Objective
optProb.addObj('obj')
# Check optimization problem:
# print(optProb)
# Optimizer
try:
opt = OPT(optName, options=optOptions)
except:
raise unittest.SkipTest('Optimizer not available:', optName)
# Solution
if storeHistory:
histFileName = '%s_hs015_Hist.hst' % (optName.lower())
else:
histFileName = None
sol = opt(optProb, sens=self.sens, storeHistory=histFileName)
# Check Solution
self.assertAlmostEqual(sol.objectives['obj'].value, 306.5)
self.assertAlmostEqual(sol.variables['xvars'][0].value, 0.5)
self.assertAlmostEqual(sol.variables['xvars'][1].value, 2.0)
def setUp(self):
# construct MP
self.MP = multiPointSparse(gcomm)
for setName in SET_NAMES:
comm_size = COMM_SIZES[setName]
self.MP.addProcessorSet(setName,
nMembers=len(comm_size),
memberSizes=comm_size)
self.comm, self.setComm, self.setFlags, self.groupFlags, self.ptID = self.MP.createCommunicators(
)
for setName in SET_NAMES:
self.MP.addProcSetObjFunc(setName, SET_FUNC_HANDLES[setName][0])
self.MP.addProcSetSensFunc(setName, SET_FUNC_HANDLES[setName][1])
# construct optProb
optProb = Optimization("multipoint test", self.MP.obj)
for dv in DVS:
optProb.addVar(dv)
optProb.addObj("total_drag")
self.MP.setObjCon(objCon)
self.MP.setOptProb(optProb)
def __call__(self, optimizer, options=None):
""" Run optimization """
system = self._system
variables = self._variables
opt_prob = OptProblem('Optimization', self.obj_func)
for dv_name in variables['dv'].keys():
dv = variables['dv'][dv_name]
dv_id = dv['ID']
value = dv['value']
lower = dv['lower']
upper = dv['upper']
size = system.vec['u'](dv_id).shape[0]
opt_prob.addVarGroup(dv_name, size, value=value,
lower=lower, upper=upper)
opt_prob.finalizeDesignVariables()
for func_name in variables['func'].keys():
func = variables['func'][func_name]
func_id = func['ID']
lower = func['lower']
upper = func['upper']
linear = func['linear']
get_jacs = func['get_jacs']
size = system.vec['u'](func_id).shape[0]
if lower is None and upper is None:
opt_prob.addObj(func_name)
else:
if func['get_jacs'] is None:
opt_prob.addConGroup(func_name, size,
lower=lower, upper=upper)
else:
jacs_var = get_jacs()
dv_names = []
jacs = {}
for dv_var in jacs_var:
dv_id = self._system.get_id(dv_var)
dv_name = self._get_name(dv_id)
dv_names.append(dv_name)
jacs[dv_name] = jacs_var[dv_var]
opt_prob.addConGroup(func_name, size,
wrt=dv_names,
jac=jacs, linear=linear,
lower=lower, upper=upper)
if options is None:
options = {}
opt = Optimizer(optimizer, options=options)
opt.setOption('Iterations limit', int(1e6))
#opt.setOption('Verify level', 3)
sol = opt(opt_prob, sens=self.sens_func, storeHistory='hist.hst')
print sol
def fit(s,t,length,plot,comp,read_data,opt_print):
global xd
global pos1d
global pos2d
global pos3d
global pos4d
global pos5d
global pos6d
global pos7d
global pos8d
global pos9d
global pos10d
global pos11d
global pos12d
global pos13d
global pos14d
global pos15d
global pos16d
global pos17d
global pos18d
global pos19d
global pos20d
global pos21d
global pos22d
global pos23d
global pos24d
global pos25d
global pos26d
global pos27d
global pos28d
global pos29d
global pos30d
global velo1d
global velo2d
global velo3d
global velo4d
global velo5d
global velo6d
global velo7d
global velo8d
global velo9d
global velo10d
global velo11d
global velo12d
global velo13d
global velo14d
global velo15d
global velo16d
global velo17d
global velo18d
global velo19d
global velo20d
global velo21d
global velo22d
global velo23d
global velo24d
global velo25d
global velo26d
global velo27d
global velo28d
global velo29d
global velo30d
t2 = t+'.0'
wfit = s+'_'+t2
wfit2 = s+'_'+t2
wfit3 = s+'_'+t2
wfit4 = s+'_'+t2
wfit5 = s+'_'+t2
wfit6 = s+'_'+t2
length2 = length
length3 = length
length4 = length
length5 = length
length6 = length
wind = 15.
wind2 = 14.
wind3 = 12.
wind4 = 16.
rad = 3.
dia = rad*2.
tsr = float(wfit[3]+'.'+wfit[4]+wfit[5])
rot = tsr*wind/rad
rot2 = tsr*wind2/rad
rot3 = tsr*wind3/rad
rot4 = tsr*wind4/rad
rot5 = 17.
rot6 = 18.
wind5 = rot5*rad/tsr
wind6 = rot6*rad/tsr
if comp == 'mac':
# fdata = '/Users/ning1/Documents/Flow Lab/STAR-CCM+/NACA0021/MoveForward/test.csv'
fdata = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/Velocity Sections/'+wfit+'.csv'
fdata2 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel14/Velocity/'+wfit2+'.csv'
fdata3 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel12/Velocity/'+wfit3+'.csv'
fdata4 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel16/Velocity/'+wfit4+'.csv'
fdata5 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot17/Velocity/'+wfit5+'.csv'
fdata6 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot18/Velocity/'+wfit6+'.csv'
elif comp == 'fsl':
fdata = '/fslhome/ebtingey/compute/moveForward/Velocity/'+wfit+'.csv'
fdata2 = '/fslhome/ebtingey/compute/moveForward/vel14/Velocity/'+wfit2+'.csv'
fdata3 = '/fslhome/ebtingey/compute/moveForward/vel12/Velocity/'+wfit3+'.csv'
fdata4 = '/fslhome/ebtingey/compute/moveForward/vel16/Velocity/'+wfit4+'.csv'
fdata5 = '/fslhome/ebtingey/compute/moveForward/rot17/Velocity/'+wfit5+'.csv'
fdata6 = '/fslhome/ebtingey/compute/moveForward/rot18/Velocity/'+wfit6+'.csv'
elif comp == 'win':
fdata = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//Velocity Sections//'+wfit+'.csv'
fdata2 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel14//Velocity//'+wfit2+'.csv'
fdata3 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel12//Velocity//'+wfit3+'.csv'
fdata4 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel16//Velocity//'+wfit4+'.csv'
fdata5 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//rot17//Velocity//'+wfit5+'.csv'
fdata6 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//rot18//Velocity//'+wfit6+'.csv'
if read_data ==1:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata]),dia,np.array([wind]),opt_print)
if read_data ==2:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata,fdata2]),dia,np.array([wind,wind2]),opt_print)
if read_data ==3:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata,fdata2,fdata3]),dia,np.array([wind,wind2,wind3]),opt_print)
if read_data ==4:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata,fdata2,fdata3,fdata4]),dia,np.array([wind,wind2,wind3,wind4]),opt_print)
if read_data ==5:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata,fdata2,fdata3,fdata4,fdata5]),dia,np.array([wind,wind2,wind3,wind4,wind5]),opt_print)
if read_data ==6:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata,fdata2,fdata3,fdata4,fdata5,fdata6]),dia,np.array([wind,wind2,wind3,wind4,wind5,wind6]),opt_print)
start = length/30.
xd = np.linspace(start,length,30)/dia
## Optimization
optProb = Optimization('VAWTWake_Velo', obj_func)
optProb.addObj('obj')
param0 = np.array([2.91638655e-04, -1.70286993e-03 , 2.38051673e-02 , 7.65610623e-01,6.40509205e-02 , 6.99046413e-01, 7.83484187e-01 , 4.55408268e-01, 1.18716383e-01 , 2.05484572e+01 , -2.67741935e+00 , 4.43022575e+01,-2.10925147e+00 , 3.30400554e+01])
# param0 = np.array([-0.000168794,0.005413905,-0.151372907,0.194333896,-29.52514866,-5.555943879,0.350540531,106.116331,0.000304188,17.4218187,30.06169144,47.3929253,32.97190468,33.4188042])
# param0 = np.array([-0.430957043,0.202977737,0.20361798,-0.818862214,-1.568705068,0.753568492,0.255557818,0.162385094,3.7137047,19.67995783,31.32756058,77.24259087,13.72492175,48.22628446])
# param0 = np.array([-0.0002347,0.0094285,-0.2262108,0.0847296,0.7634520,5.0331194,0.3964091,6.0135974,0.0052853,19.9466507,-2.0322239,47.3381111,-1.1426171,27.5907093])
param_l = np.array([-1.,-1,-1.,-1.,-1,-1.,0.,0.,0.,None,0.,None,0.])
param_u = np.array([1.,1.,1.,1.,1.,1.,None,None,None,0.,None,0.,None])
param_l = np.array([None,None,None,0.,0.,0.,0.,0.,0.,0.,None,0.,None,0.])
param_u = np.array([None,None,None,None,None,None,None,None,None,None,0.,None,0.,None])
nparam = np.size(param0)
optProb.addVarGroup('param', nparam, 'c', lower=param_l, upper=param_u, value=param0)
opt = SNOPT()
opt.setOption('Scale option',2)
if comp == 'mac':
opt.setOption('Print file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_summary'+s+'_'+t+'.out')
elif comp == 'fsl':
opt.setOption('Print file','/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_summary'+s+'_'+t+'.out')
elif comp == 'win':
opt.setOption('Print file','C://Users//TingeyPC//Documents//FLOW Lab//VAWTWakeModel//wake_model//data//optVel//SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','C://Users//TingeyPC//Documents//FLOW Lab//VAWTWakeModel//wake_model//data//OptVel//SNOPT_summary'+s+'_'+t+'.out')
res = opt(optProb, sens=None)
if opt_print == True:
print res
pow = res.fStar
paramf = res.xStar['param']
if opt_print == True:
print paramf[0]
print paramf[1]
print paramf[2]
print paramf[3]
print paramf[4]
print paramf[5]
print paramf[6]
print paramf[7]
print paramf[8]
print paramf[9]
print paramf[10]
print paramf[11]
print paramf[12]
print paramf[13]
men = np.array([paramf[0],paramf[1],paramf[2]])
spr = np.array([paramf[3],paramf[4],paramf[5],paramf[6]])
scl = np.array([paramf[7],paramf[8],paramf[9]])
rat = np.array([paramf[10],paramf[11]])
tns = np.array([paramf[12],paramf[13]])
paper = False
if plot == True:
if paper == True:
for i in range(30):
name = str(i+1)
ind = str(i)
plt.figure(1)
ax1 = plt.subplot(5,6,i+1)
color = 'bo'
color2 = 'r-'
fs = 15
lab = 'CFD'
lab2 = 'Trend'
tex = '$x/D$ = '+str("{0:.2f}".format(x[i]/dia))
exec('xfit = np.linspace(min(pos'+name+'/dia)-1.,max(pos'+name+'/dia)+1.,500)')
if i == 5:
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color,label=lab)')
men_v,spr_v,scl_v,rat_v,tns_v = paramfit(xd[i],men,spr,scl,rat,tns)
plt.plot(veldist(xfit,men_v,spr_v,scl_v,rat_v,tns_v),xfit,'r-',linewidth=2,label=lab2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
plt.legend(loc="upper left", bbox_to_anchor=(1,1),fontsize=fs)
else:
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color)')
men_v,spr_v,scl_v,rat_v,tns_v = paramfit(xd[i],men,spr,scl,rat,tns)
plt.plot(veldist(xfit,men_v,spr_v,scl_v,rat_v,tns_v),xfit,'r-',linewidth=2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
plt.text(0.3,0.8,tex,fontsize=fs)
if i <= 23:
plt.setp(ax1.get_xticklabels(), visible=False)
else:
plt.xlabel('$y/D$',fontsize=fs)
plt.xticks(fontsize=fs)
if i == 0 or i == 6 or i == 12 or i == 18 or i ==24:
plt.ylabel(r'$u/U_\infty$',fontsize=fs)
plt.yticks(fontsize=fs)
else:
plt.setp(ax1.get_yticklabels(), visible=False)
elif paper == False:
for i in range(30):
name = str(i+1)
plt.figure(1)
plt.subplot(5,6,i+1)
color = 'bo'
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color)')
men_v,spr_v,scl_v,rat_v,tns_v = paramfit(xd[i],men,spr,scl,rat,tns)
plt.plot(veldist(xfit,men_v,spr_v,scl_v,rat_v,tns_v),xfit,'r-',linewidth=2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
# plt.legend(loc=1)
plt.xlabel('Normalized Velocity')
plt.ylabel('$y/D$')
return men,spr,scl,rat,tns
def fit(s,t,length,plot,comp,read_data,opt_print):
global posdn
global poslt
global velod
t2 = t+'.0'
wfit = s+'_'+t2
wfit2 = s+'_'+t2
wfit3 = s+'_'+t2
wfit4 = s+'_'+t2
wfit5 = s+'_'+t2
wfit6 = s+'_'+t2
length2 = length
length3 = length
length4 = length
length5 = length
length6 = length
wind = 15.
wind2 = 14.
wind3 = 12.
wind4 = 16.
rad = 3.
dia = rad*2.
tsr = float(wfit[3]+'.'+wfit[4]+wfit[5])
rot = tsr*wind/rad
rot2 = tsr*wind2/rad
rot3 = tsr*wind3/rad
rot4 = tsr*wind4/rad
rot5 = 17.
rot6 = 18.
wind5 = rot5*rad/tsr
wind6 = rot6*rad/tsr
if comp == 'mac':
# fdata = '/Users/ning1/Documents/Flow Lab/STAR-CCM+/NACA0021/MoveForward/test.csv'
fdata = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/Velocity Sections/'+wfit+'.csv'
fdata2 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel14/Velocity/'+wfit2+'.csv'
fdata3 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel12/Velocity/'+wfit3+'.csv'
fdata4 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel16/Velocity/'+wfit4+'.csv'
fdata5 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot17/Velocity/'+wfit5+'.csv'
fdata6 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot18/Velocity/'+wfit6+'.csv'
elif comp == 'fsl':
fdata = '/fslhome/ebtingey/compute/moveForward/Velocity/'+wfit+'.csv'
fdata2 = '/fslhome/ebtingey/compute/moveForward/vel14/Velocity/'+wfit2+'.csv'
fdata3 = '/fslhome/ebtingey/compute/moveForward/vel12/Velocity/'+wfit3+'.csv'
fdata4 = '/fslhome/ebtingey/compute/moveForward/vel16/Velocity/'+wfit4+'.csv'
fdata5 = '/fslhome/ebtingey/compute/moveForward/rot17/Velocity/'+wfit5+'.csv'
fdata6 = '/fslhome/ebtingey/compute/moveForward/rot18/Velocity/'+wfit6+'.csv'
elif comp == 'win':
fdata = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//Velocity Sections//'+wfit+'.csv'
fdata2 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel14//Velocity//'+wfit2+'.csv'
fdata3 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel12//Velocity//'+wfit3+'.csv'
fdata4 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel16//Velocity//'+wfit4+'.csv'
fdata5 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//rot17//Velocity//'+wfit5+'.csv'
fdata6 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//rot18//Velocity//'+wfit6+'.csv'
if read_data ==1:
posdn,poslt,velod = starccm_read(np.array([fdata]),dia,np.array([wind]),length,opt_print)
if read_data ==2:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2]),dia,np.array([wind,wind2]),length,opt_print)
if read_data ==3:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3]),dia,np.array([wind,wind2,wind3]),length,opt_print)
if read_data ==4:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3,fdata4]),dia,np.array([wind,wind2,wind3,wind4]),length,opt_print)
if read_data ==5:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3,fdata4,fdata5]),dia,np.array([wind,wind2,wind3,wind4,wind5]),length,opt_print)
if read_data ==6:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3,fdata4,fdata5,fdata6]),dia,np.array([wind,wind2,wind3,wind4,wind5,wind6]),length,opt_print)
if plot == True:
if read_data ==1:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata]),dia,np.array([wind]),opt_print)
if read_data ==2:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2]),dia,np.array([wind,wind2]),opt_print)
if read_data ==3:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3]),dia,np.array([wind,wind2,wind3]),opt_print)
if read_data ==4:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3,fdata4]),dia,np.array([wind,wind2,wind3,wind4]),opt_print)
if read_data ==5:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3,fdata4,fdata5]),dia,np.array([wind,wind2,wind3,wind4,wind5]),opt_print)
if read_data ==6:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3,fdata4,fdata5,fdata6]),dia,np.array([wind,wind2,wind3,wind4,wind5,wind6]),opt_print)
start = length/30.
xd = np.linspace(start,length,30)/dia
## Optimization
optProb = Optimization('VAWTWake_Velo', obj_func)
optProb.addObj('obj')
men0 = 0.
sdv10 = 0.5
sdv20 = 0.1
sdv30 = 10.
sdv40 = 0.5
rat0 = 10.
wdt0 = 10.
spr10 = 0.5
spr20 = 0.1
spr30 = 20.
spr40 = 1.
scl10 = 0.5
scl20 = 0.1
scl30 = 40.
# men0 = 0.107980482
# sdv10 = 5.09E-01
# sdv20 = 0.056288195
# sdv30 = 50
# sdv40 = 0.5
# rat0 = 13.19127977
# wdt0 = 14.20436344
# spr10 = 1
# spr20 = 0.010825
# spr30 = 132.4282087
# spr40 = 1
# scl10 = 0.365635251
# scl20 = 0.082475724
# scl30 = 37.61946447
men0 = -0.0138439642406
sdv10 = 0.0
sdv20 = 0.17803796067
sdv30 = 9.69044107271
sdv40 = 0.50982003115
rat0 = 0.0
wdt0 = 10.0
spr10 = 0.998862596849
spr20 = 1.47011550439e-05
spr30 = 22.5386579407
spr40 = 1.0
scl10 = 0.380051328623
scl20 = 0.134712758388
scl30 = 45.7788575653
men0 = -0.0384248691061
sdv10 = 0.0
sdv20 = 0.17803796067
sdv30 = 9.69044107271
sdv40 = 0.81447171018
rat0 = 0.0
wdt0 = 10.0
spr10 = 0.0232236137751
spr20 = 0.0
spr30 = 12.6315271162
spr40 = 9.75322269238
scl10 = 0.357152390403
scl20 = 0.135756021609
scl30 = 33.8432403717
param0 = np.array([men0,sdv10,sdv20,sdv30,sdv40,rat0,wdt0,spr10,spr20,spr30,spr40,scl10,scl20,scl30])
param_l = np.array([None,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.])
param_u = np.array([None,10.,1.,50.,None,None,None,1.,1.,50.,None,1.,1.,None])
# param_l = np.array([None,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.])
# param_u = np.array([None,10.,1.,None,None,None,None,None,None,None,None,1.,1.,None])
# param_l = np.array([None,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.])
# param_u = np.array([None,None,None,None,None,None,None,None,None,None,None,1.,1.,None])
nparam = np.size(param0)
optProb.addVarGroup('param', nparam, 'c', lower=param_l, upper=param_u, value=param0)
opt = SNOPT()
opt.setOption('Scale option',2)
if comp == 'mac':
opt.setOption('Print file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_summary'+s+'_'+t+'.out')
elif comp == 'fsl':
opt.setOption('Print file','/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_summary'+s+'_'+t+'.out')
elif comp == 'win':
opt.setOption('Print file','C://Users//TingeyPC//Documents//FLOW Lab//VAWTWakeModel//wake_model//data//optVel//SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','C://Users//TingeyPC//Documents//FLOW Lab//VAWTWakeModel//wake_model//data//OptVel//SNOPT_summary'+s+'_'+t+'.out')
res = opt(optProb, sens=None)
if opt_print == True:
print res
pow = res.fStar
paramf = res.xStar['param']
if opt_print == True:
print paramf[0]
print paramf[1]
print paramf[2]
print paramf[3]
print paramf[4]
print paramf[5]
print paramf[6]
print paramf[7]
print paramf[8]
print paramf[9]
print paramf[10]
print paramf[11]
print paramf[12]
print paramf[13]
men = paramf[0]
sdv1 = paramf[1]
sdv2 = paramf[2]
sdv3 = paramf[3]
sdv4 = paramf[4]
rat = paramf[5]
wdt = paramf[6]
spr1 = paramf[7]
spr2 = paramf[8]
spr3 = paramf[9]
spr4 = paramf[10]
scl1 = paramf[11]
scl2 = paramf[12]
scl3 = paramf[13]
paper = False
if plot == True:
if paper == True:
for i in range(30):
name = str(i+1)
ind = str(i)
plt.figure(1)
ax1 = plt.subplot(5,6,i+1)
color = 'bo'
color2 = 'r-'
fs = 15
lab = 'CFD'
lab2 = 'Trend'
tex = '$x/D$ = '+str("{0:.2f}".format(x[i]/dia))
exec('xfit = np.linspace(min(pos'+name+'/dia)-1.,max(pos'+name+'/dia)+1.,500)')
if i == 5:
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color,label=lab)')
men_v,spr_v,scl_v,rat_v,spr_v = paramfit(xd[i],men,spr,scl,rat,spr)
plt.plot(veldist(xfit,men_v,spr_v,scl_v,rat_v,spr_v),xfit,'r-',linewidth=2,label=lab2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
plt.legend(loc="upper left", bbox_to_anchor=(1,1),fontsize=fs)
else:
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color)')
men_v,spr_v,scl_v,rat_v,spr_v = paramfit(xd[i],men,spr,scl,rat,spr)
plt.plot(veldist(xfit,men_v,spr_v,scl_v,rat_v,spr_v),xfit,'r-',linewidth=2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
plt.text(0.3,0.8,tex,fontsize=fs)
if i <= 23:
plt.setp(ax1.get_xticklabels(), visible=False)
else:
plt.xlabel('$y/D$',fontsize=fs)
plt.xticks(fontsize=fs)
if i == 0 or i == 6 or i == 12 or i == 18 or i ==24:
plt.ylabel(r'$u/U_\infty$',fontsize=fs)
plt.yticks(fontsize=fs)
else:
plt.setp(ax1.get_yticklabels(), visible=False)
elif paper == False:
for i in range(30):
name = str(i+1)
plt.figure(1)
plt.subplot(5,6,i+1)
color = 'bo'
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color)')
plt.plot(veldist(xd[i],xfit,men,sdv1,sdv2,sdv3,sdv4,rat,wdt,spr1,spr2,spr3,spr4,scl1,scl2,scl3),xfit,'r-',linewidth=2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
# plt.legend(loc=1)
plt.xlabel('Normalized Velocity')
plt.ylabel('$y/D$')
return men,sdv1,sdv2,sdv3,sdv4,rat,wdt,spr1,spr2,spr3,spr4,scl1,scl2,scl3
def fit(s, t, length, plot, comp, read_data, opt_print):
global posdn
global poslt
global velod
t2 = t + ".0"
wfit = s + "_" + t2
wfit2 = s + "_" + t2
wfit3 = s + "_" + t2
wfit4 = s + "_" + t2
wfit5 = s + "_" + t2
wfit6 = s + "_" + t2
length2 = length
length3 = length
length4 = length
length5 = length
length6 = length
wind = 15.0
wind2 = 14.0
wind3 = 12.0
wind4 = 16.0
rad = 3.0
dia = rad * 2.0
tsr = float(wfit[3] + "." + wfit[4] + wfit[5])
rot = tsr * wind / rad
rot2 = tsr * wind2 / rad
rot3 = tsr * wind3 / rad
rot4 = tsr * wind4 / rad
rot5 = 17.0
rot6 = 18.0
wind5 = rot5 * rad / tsr
wind6 = rot6 * rad / tsr
if comp == "mac":
# fdata = '/Users/ning1/Documents/Flow Lab/STAR-CCM+/NACA0021/MoveForward/test.csv'
fdata = "/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/Velocity Sections/" + wfit + ".csv"
fdata2 = (
"/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel14/Velocity/"
+ wfit2
+ ".csv"
)
fdata3 = (
"/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel12/Velocity/"
+ wfit3
+ ".csv"
)
fdata4 = (
"/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel16/Velocity/"
+ wfit4
+ ".csv"
)
fdata5 = (
"/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot17/Velocity/"
+ wfit5
+ ".csv"
)
fdata6 = (
"/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot18/Velocity/"
+ wfit6
+ ".csv"
)
elif comp == "fsl":
fdata = "/fslhome/ebtingey/compute/moveForward/Velocity/" + wfit + ".csv"
fdata2 = "/fslhome/ebtingey/compute/moveForward/vel14/Velocity/" + wfit2 + ".csv"
fdata3 = "/fslhome/ebtingey/compute/moveForward/vel12/Velocity/" + wfit3 + ".csv"
fdata4 = "/fslhome/ebtingey/compute/moveForward/vel16/Velocity/" + wfit4 + ".csv"
fdata5 = "/fslhome/ebtingey/compute/moveForward/rot17/Velocity/" + wfit5 + ".csv"
fdata6 = "/fslhome/ebtingey/compute/moveForward/rot18/Velocity/" + wfit6 + ".csv"
elif comp == "win":
fdata = (
"C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//Velocity Sections//"
+ wfit
+ ".csv"
)
fdata2 = (
"C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel14//Velocity//"
+ wfit2
+ ".csv"
)
fdata3 = (
"C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel12//Velocity//"
+ wfit3
+ ".csv"
)
fdata4 = (
"C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel16//Velocity//"
+ wfit4
+ ".csv"
)
fdata5 = (
"C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//rot17//Velocity//"
+ wfit5
+ ".csv"
)
fdata6 = (
"C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//rot18//Velocity//"
+ wfit6
+ ".csv"
)
if read_data == 1:
posdn, poslt, velod = starccm_read(np.array([fdata]), dia, np.array([wind]), length, opt_print)
if read_data == 2:
posdn, poslt, velod = starccm_read(np.array([fdata, fdata2]), dia, np.array([wind, wind2]), length, opt_print)
if read_data == 3:
posdn, poslt, velod = starccm_read(
np.array([fdata, fdata2, fdata3]), dia, np.array([wind, wind2, wind3]), length, opt_print
)
if read_data == 4:
posdn, poslt, velod = starccm_read(
np.array([fdata, fdata2, fdata3, fdata4]), dia, np.array([wind, wind2, wind3, wind4]), length, opt_print
)
if read_data == 5:
posdn, poslt, velod = starccm_read(
np.array([fdata, fdata2, fdata3, fdata4, fdata5]),
dia,
np.array([wind, wind2, wind3, wind4, wind5]),
length,
opt_print,
)
if read_data == 6:
posdn, poslt, velod = starccm_read(
np.array([fdata, fdata2, fdata3, fdata4, fdata5, fdata6]),
dia,
np.array([wind, wind2, wind3, wind4, wind5, wind6]),
length,
opt_print,
)
if plot == True:
if read_data == 1:
pos1d, pos2d, pos3d, pos4d, pos5d, pos6d, pos7d, pos8d, pos9d, pos10d, pos11d, pos12d, pos13d, pos14d, pos15d, pos16d, pos17d, pos18d, pos19d, pos20d, pos21d, pos22d, pos23d, pos24d, pos25d, pos26d, pos27d, pos28d, pos29d, pos30d, velo1d, velo2d, velo3d, velo4d, velo5d, velo6d, velo7d, velo8d, velo9d, velo10d, velo11d, velo12d, velo13d, velo14d, velo15d, velo16d, velo17d, velo18d, velo19d, velo20d, velo21d, velo22d, velo23d, velo24d, velo25d, velo26d, velo27d, velo28d, velo29d, velo30d = starccm_read2(
np.array([fdata]), dia, np.array([wind]), opt_print
)
if read_data == 2:
pos1d, pos2d, pos3d, pos4d, pos5d, pos6d, pos7d, pos8d, pos9d, pos10d, pos11d, pos12d, pos13d, pos14d, pos15d, pos16d, pos17d, pos18d, pos19d, pos20d, pos21d, pos22d, pos23d, pos24d, pos25d, pos26d, pos27d, pos28d, pos29d, pos30d, velo1d, velo2d, velo3d, velo4d, velo5d, velo6d, velo7d, velo8d, velo9d, velo10d, velo11d, velo12d, velo13d, velo14d, velo15d, velo16d, velo17d, velo18d, velo19d, velo20d, velo21d, velo22d, velo23d, velo24d, velo25d, velo26d, velo27d, velo28d, velo29d, velo30d = starccm_read2(
np.array([fdata, fdata2]), dia, np.array([wind, wind2]), opt_print
)
if read_data == 3:
pos1d, pos2d, pos3d, pos4d, pos5d, pos6d, pos7d, pos8d, pos9d, pos10d, pos11d, pos12d, pos13d, pos14d, pos15d, pos16d, pos17d, pos18d, pos19d, pos20d, pos21d, pos22d, pos23d, pos24d, pos25d, pos26d, pos27d, pos28d, pos29d, pos30d, velo1d, velo2d, velo3d, velo4d, velo5d, velo6d, velo7d, velo8d, velo9d, velo10d, velo11d, velo12d, velo13d, velo14d, velo15d, velo16d, velo17d, velo18d, velo19d, velo20d, velo21d, velo22d, velo23d, velo24d, velo25d, velo26d, velo27d, velo28d, velo29d, velo30d = starccm_read2(
np.array([fdata, fdata2, fdata3]), dia, np.array([wind, wind2, wind3]), opt_print
)
if read_data == 4:
pos1d, pos2d, pos3d, pos4d, pos5d, pos6d, pos7d, pos8d, pos9d, pos10d, pos11d, pos12d, pos13d, pos14d, pos15d, pos16d, pos17d, pos18d, pos19d, pos20d, pos21d, pos22d, pos23d, pos24d, pos25d, pos26d, pos27d, pos28d, pos29d, pos30d, velo1d, velo2d, velo3d, velo4d, velo5d, velo6d, velo7d, velo8d, velo9d, velo10d, velo11d, velo12d, velo13d, velo14d, velo15d, velo16d, velo17d, velo18d, velo19d, velo20d, velo21d, velo22d, velo23d, velo24d, velo25d, velo26d, velo27d, velo28d, velo29d, velo30d = starccm_read2(
np.array([fdata, fdata2, fdata3, fdata4]), dia, np.array([wind, wind2, wind3, wind4]), opt_print
)
if read_data == 5:
pos1d, pos2d, pos3d, pos4d, pos5d, pos6d, pos7d, pos8d, pos9d, pos10d, pos11d, pos12d, pos13d, pos14d, pos15d, pos16d, pos17d, pos18d, pos19d, pos20d, pos21d, pos22d, pos23d, pos24d, pos25d, pos26d, pos27d, pos28d, pos29d, pos30d, velo1d, velo2d, velo3d, velo4d, velo5d, velo6d, velo7d, velo8d, velo9d, velo10d, velo11d, velo12d, velo13d, velo14d, velo15d, velo16d, velo17d, velo18d, velo19d, velo20d, velo21d, velo22d, velo23d, velo24d, velo25d, velo26d, velo27d, velo28d, velo29d, velo30d = starccm_read2(
np.array([fdata, fdata2, fdata3, fdata4, fdata5]),
dia,
np.array([wind, wind2, wind3, wind4, wind5]),
opt_print,
)
if read_data == 6:
pos1d, pos2d, pos3d, pos4d, pos5d, pos6d, pos7d, pos8d, pos9d, pos10d, pos11d, pos12d, pos13d, pos14d, pos15d, pos16d, pos17d, pos18d, pos19d, pos20d, pos21d, pos22d, pos23d, pos24d, pos25d, pos26d, pos27d, pos28d, pos29d, pos30d, velo1d, velo2d, velo3d, velo4d, velo5d, velo6d, velo7d, velo8d, velo9d, velo10d, velo11d, velo12d, velo13d, velo14d, velo15d, velo16d, velo17d, velo18d, velo19d, velo20d, velo21d, velo22d, velo23d, velo24d, velo25d, velo26d, velo27d, velo28d, velo29d, velo30d = starccm_read2(
np.array([fdata, fdata2, fdata3, fdata4, fdata5, fdata6]),
dia,
np.array([wind, wind2, wind3, wind4, wind5, wind6]),
opt_print,
)
start = length / 30.0
xd = np.linspace(start, length, 30) / dia
## Optimization
optProb = Optimization("VAWTWake_Velo", obj_func)
optProb.addObj("obj")
spr10 = 10.0
pow10 = 10.0
pow20 = 0.5
pow30 = 1.0
spr20 = 2.0
skw0 = 0.0
scl10 = 0.5
scl20 = 0.1
scl30 = 10.0
spr10 = 10.0
pow10 = 5.0
pow20 = 0.5
pow30 = 1.0
spr20 = 2.0
skw0 = 0.0
scl10 = 0.5
scl20 = 0.1
scl30 = 20.0
spr10 = 213.8593169
pow10 = 10.39210953
pow20 = 2.086951239
pow30 = 0.035659319
spr20 = 0.007589688
skw0 = 10.63462155
scl10 = 0.537566448
scl20 = 0.041077603
scl30 = 56.74689143
spr10 = 100.0
pow10 = 10.0
pow20 = 0.5
pow30 = 0.0 # 1.0
spr20 = 20.0
skw0 = 0.0
scl10 = 0.5
scl20 = 0.1
scl30 = 10.0
param0 = np.array([spr10, pow10, pow20, pow30, spr20, skw0, scl10, scl20, scl30])
param_l = np.array([0.0, 0.0, 0.0, 0.0, 0.0, None, 0.0, 0.0, 0.0])
param_u = np.array([None, None, None, None, None, None, 1.0, 1.0, None])
nparam = np.size(param0)
optProb.addVarGroup("param", nparam, "c", lower=param_l, upper=param_u, value=param0)
opt = SNOPT()
opt.setOption("Scale option", 2)
if comp == "mac":
opt.setOption(
"Print file",
"/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_print" + s + "_" + t + ".out",
)
opt.setOption(
"Summary file",
"/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_summary" + s + "_" + t + ".out",
)
elif comp == "fsl":
opt.setOption("Print file", "/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_print" + s + "_" + t + ".out")
opt.setOption(
"Summary file", "/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_summary" + s + "_" + t + ".out"
)
elif comp == "win":
opt.setOption(
"Print file",
"C://Users//TingeyPC//Documents//FLOW Lab//VAWTWakeModel//wake_model//data//optVel//SNOPT_print"
+ s
+ "_"
+ t
+ ".out",
)
opt.setOption(
"Summary file",
"C://Users//TingeyPC//Documents//FLOW Lab//VAWTWakeModel//wake_model//data//OptVel//SNOPT_summary"
+ s
+ "_"
+ t
+ ".out",
)
res = opt(optProb, sens=None)
if opt_print == True:
print res
pow = res.fStar
paramf = res.xStar["param"]
if opt_print == True:
print paramf[0]
print paramf[1]
print paramf[2]
print paramf[3]
print paramf[4]
print paramf[5]
print paramf[6]
print paramf[7]
print paramf[8]
spr1 = paramf[0]
pow1 = paramf[1]
pow2 = paramf[2]
pow3 = paramf[3]
spr2 = paramf[4]
skw = paramf[5]
scl1 = paramf[6]
scl2 = paramf[7]
scl3 = paramf[8]
paper = False
if plot == True:
if paper == True:
for i in range(30):
name = str(i + 1)
ind = str(i)
plt.figure(1)
ax1 = plt.subplot(5, 6, i + 1)
color = "bo"
color2 = "r-"
fs = 15
lab = "CFD"
lab2 = "Trend"
tex = "$x/D$ = " + str("{0:.2f}".format(x[i] / dia))
exec ("xfit = np.linspace(min(pos" + name + "/dia)-1.,max(pos" + name + "/dia)+1.,500)")
if i == 5:
exec ("xfit = np.linspace(min(pos" + name + "d)-1.,max(pos" + name + "d)+1.,500)")
exec ("plt.plot(velo" + name + "d,pos" + name + "d,color,label=lab)")
skw_v, spr_v, scl_v, rat_v, spr_v = paramfit(xd[i], skw, spr, scl, rat, spr)
plt.plot(veldist(xfit, skw_v, spr_v, scl_v, rat_v, spr_v), xfit, "r-", linewidth=2, label=lab2)
plt.xlim(0.0, 1.5)
# plt.ylim(-4.,4.)
plt.legend(loc="upper left", bbox_to_anchor=(1, 1), fontsize=fs)
else:
exec ("xfit = np.linspace(min(pos" + name + "d)-1.,max(pos" + name + "d)+1.,500)")
exec ("plt.plot(velo" + name + "d,pos" + name + "d,color)")
skw_v, spr_v, scl_v, rat_v, spr_v = paramfit(xd[i], skw, spr, scl, rat, spr)
plt.plot(veldist(xfit, skw_v, spr_v, scl_v, rat_v, spr_v), xfit, "r-", linewidth=2)
plt.xlim(0.0, 1.5)
# plt.ylim(-4.,4.)
plt.text(0.3, 0.8, tex, fontsize=fs)
if i <= 23:
plt.setp(ax1.get_xticklabels(), visible=False)
else:
plt.xlabel("$y/D$", fontsize=fs)
plt.xticks(fontsize=fs)
if i == 0 or i == 6 or i == 12 or i == 18 or i == 24:
plt.ylabel(r"$u/U_\infty$", fontsize=fs)
plt.yticks(fontsize=fs)
else:
plt.setp(ax1.get_yticklabels(), visible=False)
elif paper == False:
for i in range(30):
name = str(i + 1)
plt.figure(1)
plt.subplot(5, 6, i + 1)
color = "bo"
exec ("xfit = np.linspace(min(pos" + name + "d)-1.,max(pos" + name + "d)+1.,500)")
exec ("plt.plot(velo" + name + "d,pos" + name + "d,color)")
plt.plot(
veldist(xd[i], xfit, spr1, pow1, pow2, pow3, spr2, skw, scl1, scl2, scl3), xfit, "r-", linewidth=2
)
plt.xlim(0.0, 1.5)
# plt.ylim(-4.,4.)
# plt.legend(loc=1)
plt.xlabel("Normalized Velocity")
plt.ylabel("$y/D$")
return spr1, pow1, pow2, pow3, spr2, skw, scl1, scl2, scl3
def fit(s,t,length,plot,comp,read_data,opt_print):
global posdntr
global poslttr
global velodtr
t2 = t+'.0'
wfit = s+'_'+t2
wfit2 = s+'_'+t2
wfit3 = s+'_'+t2
wfit4 = s+'_'+t2
wfit5 = s+'_'+t2
wfit6 = s+'_'+t2
length2 = length
length3 = length
length4 = length
length5 = length
length6 = length
wind = 15.
wind2 = 14.
wind3 = 12.
wind4 = 16.
rad = 3.
dia = rad*2.
tsr = float(wfit[3]+'.'+wfit[4]+wfit[5])
rot = tsr*wind/rad
rot2 = tsr*wind2/rad
rot3 = tsr*wind3/rad
rot4 = tsr*wind4/rad
rot5 = 17.
rot6 = 18.
wind5 = rot5*rad/tsr
wind6 = rot6*rad/tsr
if comp == 'mac':
# fdata = '/Users/ning1/Documents/Flow Lab/STAR-CCM+/NACA0021/MoveForward/test.csv'
fdata = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/Velocity Sections/'+wfit+'.csv'
fdata2 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel14/Velocity/'+wfit2+'.csv'
fdata3 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel12/Velocity/'+wfit3+'.csv'
fdata4 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel16/Velocity/'+wfit4+'.csv'
fdata5 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot17/Velocity/'+wfit5+'.csv'
fdata6 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot18/Velocity/'+wfit6+'.csv'
elif comp == 'fsl':
fdata = '/fslhome/ebtingey/compute/moveForward/Velocity/'+wfit+'.csv'
fdata2 = '/fslhome/ebtingey/compute/moveForward/vel14/Velocity/'+wfit2+'.csv'
fdata3 = '/fslhome/ebtingey/compute/moveForward/vel12/Velocity/'+wfit3+'.csv'
fdata4 = '/fslhome/ebtingey/compute/moveForward/vel16/Velocity/'+wfit4+'.csv'
fdata5 = '/fslhome/ebtingey/compute/moveForward/rot17/Velocity/'+wfit5+'.csv'
fdata6 = '/fslhome/ebtingey/compute/moveForward/rot18/Velocity/'+wfit6+'.csv'
if read_data ==1:
posdn,poslt,velod = starccm_read(np.array([fdata]),dia,np.array([wind]),length,opt_print)
if read_data ==2:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2]),dia,np.array([wind,wind2]),length,opt_print)
if read_data ==3:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3]),dia,np.array([wind,wind2,wind3]),length,opt_print)
if read_data ==4:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3,fdata4]),dia,np.array([wind,wind2,wind3,wind4]),length,opt_print)
if read_data ==5:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3,fdata4,fdata5]),dia,np.array([wind,wind2,wind3,wind4,wind5]),length,opt_print)
if read_data ==6:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3,fdata4,fdata5,fdata6]),dia,np.array([wind,wind2,wind3,wind4,wind5,wind6]),length,opt_print)
if plot == True:
if read_data ==1:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata]),dia,np.array([wind]),opt_print)
if read_data ==2:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2]),dia,np.array([wind,wind2]),opt_print)
if read_data ==3:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3]),dia,np.array([wind,wind2,wind3]),opt_print)
if read_data ==4:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3,fdata4]),dia,np.array([wind,wind2,wind3,wind4]),opt_print)
if read_data ==5:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3,fdata4,fdata5]),dia,np.array([wind,wind2,wind3,wind4,wind5]),opt_print)
if read_data ==6:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3,fdata4,fdata5,fdata6]),dia,np.array([wind,wind2,wind3,wind4,wind5,wind6]),opt_print)
start = length/30.
xd = np.linspace(start,length,30)/dia
cvtest = 0.3
posdntr,posdnts,poslttr,posltts,velodtr,velodts = train_test_split(posdn,poslt,velod,test_size=cvtest)
## Optimization
optProb = Optimization('VAWTWake_Velo', obj_func)
optProb.addObj('obj')
men0 = 0.
sdv10 = 0.5
sdv20 = 0.1
sdv30 = 10.
sdv40 = 0.5
rat0 = 10.
spr0 = 10.
bow1 = 0.5
bow2 = 0.1
bow3 = 20.
bow4 = 1.
# bow1 = -1.
# bow2 = 1.
# bow3 = 1.
# bow4 = 1.
param0 = np.array([men0,sdv10,sdv20,sdv30,sdv40,rat0,bow4,spr0,bow1,bow2,bow3,0.5,0.1,20.])
param_l = np.array([None,0.,0.,0.,0.,None,0.,None,0.,0.,0.,0.])
param_u = np.array([None,None,None,None,None,0.,None,0.,None,1.,1.,50.])
param_l = np.array([None,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.])
param_u = np.array([None,10.,1.,50.,None,None,None,None,1.,1.,50.,1.,1.,None])
# param_l = np.array([None,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.])
# param_u = np.array([None,None,None,None,None,None,None,None,None,None,None,1.,1.,None])
# param_l = np.array([None,0.,0.,0.,0.,0.,None,0.,None,None,None,0.,0.,0.])
# param_u = np.array([None,1.,1.,50.,None,None,None,None,None,None,None,1.,1.,50.])
nparam = np.size(param0)
optProb.addVarGroup('param', nparam, 'c', lower=param_l, upper=param_u, value=param0)
opt = SNOPT()
opt.setOption('Scale option',2)
if comp == 'mac':
opt.setOption('Print file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_summary'+s+'_'+t+'.out')
elif comp == 'fsl':
opt.setOption('Print file','/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_summary'+s+'_'+t+'.out')
res = opt(optProb, sens=None)
if opt_print == True:
print res
pow = res.fStar
paramf = res.xStar['param']
if opt_print == True:
print paramf[0]
print paramf[1]
print paramf[2]
print paramf[3]
print paramf[4]
print paramf[5]
print paramf[6]
print paramf[7]
print paramf[8]
print paramf[9]
print paramf[10]
print paramf[11]
print paramf[12]
print paramf[13]
men = paramf[0]
sdv1 = paramf[1]
sdv2 = paramf[2]
sdv3 = paramf[3]
sdv4 = paramf[4]
rat1 = paramf[5]
rat2 = paramf[6]
spr1 = paramf[7]
spr2 = paramf[8]
spr3 = paramf[9]
spr4 = paramf[10]
scl1 = paramf[11]
scl2 = paramf[12]
scl3 = paramf[13]
cv_error = 0.
for i in range(np.size(posdnts)):
if posdnts[i] > 0.58:
vel = veldist(posdnts[i],posltts[i],men,sdv1,sdv2,sdv3,sdv4,rat1,rat2,spr1,spr2,spr3,spr4,scl1,scl2,scl3)
cv_error = cv_error + (vel-velodts[i])**2
# men = np.array([paramf[0],paramf[1],paramf[2]])
# spr = np.array([paramf[3],paramf[4],paramf[5],paramf[6]])
# scl = np.array([paramf[7],paramf[8],paramf[9]])
# rat = np.array([paramf[10],paramf[11]])
# spr = np.array([paramf[12],paramf[13]])
#
paper = False
if plot == True:
if paper == True:
for i in range(30):
name = str(i+1)
ind = str(i)
plt.figure(1)
ax1 = plt.subplot(5,6,i+1)
color = 'bo'
color2 = 'r-'
fs = 15
lab = 'CFD'
lab2 = 'Trend'
tex = '$x/D$ = '+str("{0:.2f}".format(x[i]/dia))
exec('xfit = np.linspace(min(pos'+name+'/dia)-1.,max(pos'+name+'/dia)+1.,500)')
if i == 5:
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color,label=lab)')
men_v,spr_v,scl_v,rat_v,spr_v = paramfit(xd[i],men,spr,scl,rat,spr)
plt.plot(veldist(xfit,men_v,spr_v,scl_v,rat_v,spr_v),xfit,'r-',linewidth=2,label=lab2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
plt.legend(loc="upper left", bbox_to_anchor=(1,1),fontsize=fs)
else:
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color)')
men_v,spr_v,scl_v,rat_v,spr_v = paramfit(xd[i],men,spr,scl,rat,spr)
plt.plot(veldist(xfit,men_v,spr_v,scl_v,rat_v,spr_v),xfit,'r-',linewidth=2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
plt.text(0.3,0.8,tex,fontsize=fs)
if i <= 23:
plt.setp(ax1.get_xticklabels(), visible=False)
else:
plt.xlabel('$y/D$',fontsize=fs)
plt.xticks(fontsize=fs)
if i == 0 or i == 6 or i == 12 or i == 18 or i ==24:
plt.ylabel(r'$u/U_\infty$',fontsize=fs)
plt.yticks(fontsize=fs)
else:
plt.setp(ax1.get_yticklabels(), visible=False)
elif paper == False:
for i in range(30):
name = str(i+1)
plt.figure(1)
plt.subplot(5,6,i+1)
color = 'bo'
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color)')
plt.plot(veldist(xd[i],xfit,men,sdv1,sdv2,sdv3,sdv4,rat1,rat2,spr1,spr2,spr3,spr4,scl1,scl2,scl3),xfit,'r-',linewidth=2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
# plt.legend(loc=1)
plt.xlabel('Normalized Velocity')
plt.ylabel('$y/D$')
return men,sdv1,sdv2,sdv3,sdv4,rat1,rat2,spr1,spr2,spr3,spr4,scl1,scl2,scl3,cv_error
def fit(s,t,length,plot,comp,read_data,opt_print):
global xd
global pos1tr
global pos2tr
global pos3tr
global pos4tr
global pos5tr
global pos6tr
global pos7tr
global pos8tr
global pos9tr
global pos10tr
global pos11tr
global pos12tr
global pos13tr
global pos14tr
global pos15tr
global pos16tr
global pos17tr
global pos18tr
global pos19tr
global pos20tr
global pos21tr
global pos22tr
global pos23tr
global pos24tr
global pos25tr
global pos26tr
global pos27tr
global pos28tr
global pos29tr
global pos30tr
global velo1tr
global velo2tr
global velo3tr
global velo4tr
global velo5tr
global velo6tr
global velo7tr
global velo8tr
global velo9tr
global velo10tr
global velo11tr
global velo12tr
global velo13tr
global velo14tr
global velo15tr
global velo16tr
global velo17tr
global velo18tr
global velo19tr
global velo20tr
global velo21tr
global velo22tr
global velo23tr
global velo24tr
global velo25tr
global velo26tr
global velo27tr
global velo28tr
global velo29tr
global velo30tr
t2 = t+'.0'
wfit = s+'_'+t2
wfit2 = s+'_'+t2
wfit3 = s+'_'+t2
wfit4 = s+'_'+t2
wfit5 = s+'_'+t2
wfit6 = s+'_'+t2
length2 = length
length3 = length
length4 = length
length5 = length
length6 = length
wind = 15.
wind2 = 14.
wind3 = 12.
wind4 = 16.
rad = 3.
dia = rad*2.
tsr = float(wfit[3]+'.'+wfit[4]+wfit[5])
rot = tsr*wind/rad
rot2 = tsr*wind2/rad
rot3 = tsr*wind3/rad
rot4 = tsr*wind4/rad
rot5 = 17.
rot6 = 18.
wind5 = rot5*rad/tsr
wind6 = rot6*rad/tsr
if comp == 'mac':
# fdata = '/Users/ning1/Documents/Flow Lab/STAR-CCM+/NACA0021/MoveForward/test.csv'
fdata = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/Velocity Sections/'+wfit+'.csv'
fdata2 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel14/Velocity/'+wfit2+'.csv'
fdata3 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel12/Velocity/'+wfit3+'.csv'
fdata4 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel16/Velocity/'+wfit4+'.csv'
fdata5 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot17/Velocity/'+wfit5+'.csv'
fdata6 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot18/Velocity/'+wfit6+'.csv'
elif comp == 'fsl':
fdata = '/fslhome/ebtingey/compute/moveForward/Velocity/'+wfit+'.csv'
fdata2 = '/fslhome/ebtingey/compute/moveForward/vel14/Velocity/'+wfit2+'.csv'
fdata3 = '/fslhome/ebtingey/compute/moveForward/vel12/Velocity/'+wfit3+'.csv'
fdata4 = '/fslhome/ebtingey/compute/moveForward/vel16/Velocity/'+wfit4+'.csv'
fdata5 = '/fslhome/ebtingey/compute/moveForward/rot17/Velocity/'+wfit5+'.csv'
fdata6 = '/fslhome/ebtingey/compute/moveForward/rot18/Velocity/'+wfit6+'.csv'
if read_data ==1:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata]),dia,np.array([wind]),opt_print)
if read_data ==2:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata,fdata2]),dia,np.array([wind,wind2]),opt_print)
if read_data ==3:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata,fdata2,fdata3]),dia,np.array([wind,wind2,wind3]),opt_print)
if read_data ==4:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata,fdata2,fdata3,fdata4]),dia,np.array([wind,wind2,wind3,wind4]),opt_print)
if read_data ==5:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata,fdata2,fdata3,fdata4,fdata5]),dia,np.array([wind,wind2,wind3,wind4,wind5]),opt_print)
if read_data ==6:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata,fdata2,fdata3,fdata4,fdata5,fdata6]),dia,np.array([wind,wind2,wind3,wind4,wind5,wind6]),opt_print)
start = length/30.
xd = np.linspace(start,length,30)/dia
cvtest = 0.3
pos1tr,pos1ts,velo1tr,velo1ts = train_test_split(pos1d,velo1d,test_size=cvtest)
pos2tr,pos2ts,velo2tr,velo2ts = train_test_split(pos2d,velo2d,test_size=cvtest)
pos3tr,pos3ts,velo3tr,velo3ts = train_test_split(pos3d,velo3d,test_size=cvtest)
pos4tr,pos4ts,velo4tr,velo4ts = train_test_split(pos4d,velo4d,test_size=cvtest)
pos5tr,pos5ts,velo5tr,velo5ts = train_test_split(pos5d,velo5d,test_size=cvtest)
pos6tr,pos6ts,velo6tr,velo6ts = train_test_split(pos6d,velo6d,test_size=cvtest)
pos7tr,pos7ts,velo7tr,velo7ts = train_test_split(pos7d,velo7d,test_size=cvtest)
pos8tr,pos8ts,velo8tr,velo8ts = train_test_split(pos8d,velo8d,test_size=cvtest)
pos9tr,pos9ts,velo9tr,velo9ts = train_test_split(pos9d,velo9d,test_size=cvtest)
pos10tr,pos10ts,velo10tr,velo10ts = train_test_split(pos10d,velo10d,test_size=cvtest)
pos11tr,pos11ts,velo11tr,velo11ts = train_test_split(pos11d,velo11d,test_size=cvtest)
pos12tr,pos12ts,velo12tr,velo12ts = train_test_split(pos12d,velo12d,test_size=cvtest)
pos13tr,pos13ts,velo13tr,velo13ts = train_test_split(pos13d,velo13d,test_size=cvtest)
pos14tr,pos14ts,velo14tr,velo14ts = train_test_split(pos14d,velo14d,test_size=cvtest)
pos15tr,pos15ts,velo15tr,velo15ts = train_test_split(pos15d,velo15d,test_size=cvtest)
pos16tr,pos16ts,velo16tr,velo16ts = train_test_split(pos16d,velo16d,test_size=cvtest)
pos17tr,pos17ts,velo17tr,velo17ts = train_test_split(pos17d,velo17d,test_size=cvtest)
pos18tr,pos18ts,velo18tr,velo18ts = train_test_split(pos18d,velo18d,test_size=cvtest)
pos19tr,pos19ts,velo19tr,velo19ts = train_test_split(pos19d,velo19d,test_size=cvtest)
pos20tr,pos20ts,velo20tr,velo20ts = train_test_split(pos20d,velo20d,test_size=cvtest)
pos21tr,pos21ts,velo21tr,velo21ts = train_test_split(pos21d,velo21d,test_size=cvtest)
pos22tr,pos22ts,velo22tr,velo22ts = train_test_split(pos22d,velo22d,test_size=cvtest)
pos23tr,pos23ts,velo23tr,velo23ts = train_test_split(pos23d,velo23d,test_size=cvtest)
pos24tr,pos24ts,velo24tr,velo24ts = train_test_split(pos24d,velo24d,test_size=cvtest)
pos25tr,pos25ts,velo25tr,velo25ts = train_test_split(pos25d,velo25d,test_size=cvtest)
pos26tr,pos26ts,velo26tr,velo26ts = train_test_split(pos26d,velo26d,test_size=cvtest)
pos27tr,pos27ts,velo27tr,velo27ts = train_test_split(pos27d,velo27d,test_size=cvtest)
pos28tr,pos28ts,velo28tr,velo28ts = train_test_split(pos28d,velo28d,test_size=cvtest)
pos29tr,pos29ts,velo29tr,velo29ts = train_test_split(pos29d,velo29d,test_size=cvtest)
pos30tr,pos30ts,velo30tr,velo30ts = train_test_split(pos30d,velo30d,test_size=cvtest)
## Optimization
optProb = Optimization('VAWTWake_Velo', obj_func)
optProb.addObj('obj')
param0 = np.array([2.91638655e-04, -1.70286993e-03 , 2.38051673e-02 , -7.65610623e-01,6.40509205e-02 , 6.99046413e-01, 7.83484187e-01 , 4.55408268e-01, 1.18716383e-01 , 2.05484572e+01 , -2.67741935e+00 , 4.43022575e+01,-2.10925147e+00 , 3.30400554e+01])
param_l = np.array([-1.,-1,-1.,-1.,-1,-1.,0.,0.,0.,None,0.,None,0.])
param_u = np.array([1.,1.,1.,1.,1.,1.,None,None,None,0.,None,0.,None])
nparam = np.size(param0)
optProb.addVarGroup('param', nparam, 'c', lower=None, upper=None, value=param0)
opt = SNOPT()
opt.setOption('Scale option',2)
if comp == 'mac':
opt.setOption('Print file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_summary'+s+'_'+t+'.out')
elif comp == 'fsl':
opt.setOption('Print file','/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_summary'+s+'_'+t+'.out')
res = opt(optProb, sens=None)
if opt_print == True:
print res
pow = res.fStar
paramf = res.xStar['param']
if opt_print == True:
print paramf
men = np.array([paramf[0],paramf[1],paramf[2]])
spr = np.array([paramf[3],paramf[4],paramf[5],paramf[6]])
scl = np.array([paramf[7],paramf[8],paramf[9]])
rat = np.array([paramf[10],paramf[11]])
tns = np.array([paramf[12],paramf[13]])
cv_error = 0.
for i in range(30):
name = str(i+1)
ind = str(i)
exec('if xd['+ind+'] > 0.58:\n\tmen_v,spr_v,scl_v,rat_v,tns_v = paramfit(xd['+ind+'],men,spr,scl,rat,tns)\n\tfor j in range(np.size(pos'+name+'ts)):\n\t\tvel = (-scl_v/(spr_v*sqrt(2.*pi))*exp(-(pos'+name+'ts[j]+men_v)**2/(2.*spr_v**2)))*(1./(1. + exp(rat_v*fabs(pos'+name+'ts[j])-tns_v))) + 1.\n\t\tcv_error = cv_error + (vel-velo'+name+'ts[j])**2')
if plot == True:
for i in range(30):
name = str(i+1)
plt.figure(1)
plt.subplot(5,6,i+1)
color = 'bo'
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color)')
men_v,spr_v,scl_v,rat_v,tns_v = paramfit(xd[i],men,spr,scl,rat,tns)
plt.plot(veldist(xfit,men_v,spr_v,scl_v,rat_v,tns_v),xfit,'r-',linewidth=2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
# plt.legend(loc=1)
plt.xlabel('Normalized Velocity')
plt.ylabel('$y/D$')
return men,spr,scl,rat,tns,cv_error
# Creating a power curve for the turbine (tip-speed ratio vs. power coefficient)
powercurve = Akima1DInterpolator(tsr,CP)
# Adjusting geometry for SPL calculations
r_nrel = np.array([2.8667, 5.6000, 8.3333, 11.7500, 15.8500, 19.9500, 24.0500, 28.1500, 32.2500, 36.3500, 40.4500, 44.5500, 48.6500, 52.7500, 56.1667, 58.9000, 61.6333, 63.0]) # radial positions (m)
rad = r_nrel*r_ratio
# Initialize input variables
rotorDiameter = np.ones(nturb)*rotor_diameter
generator_efficiency = np.ones(nturb)*0.944
yaw = np.ones((nwind,nturb))*0.
rpm = np.ones(nwind*nturb)*rpm_max
# Optimization
optProb = Optimization('Wind_Farm_APP', obj_func)
optProb.addObj('obj')
# Design Variables (scaled to 1)
nrpm = nturb*nwind
optProb.addVarGroup('xvars', nturb, 'c', lower=xlow/100., upper=xupp/100., value=turbineX/100.) # x positions
optProb.addVarGroup('yvars', nturb, 'c', lower=ylow/100., upper=yupp/100., value=turbineY/100.) # y positions
optProb.addVarGroup('rpm', nrpm, 'c', lower=rpmlow/10., upper=rpmupp/10., value=rpm/10.) # rpm values
# Constraints (scaled to 1)
num_cons_sep = (nturb-1)*nturb/2
optProb.addConGroup('sep', num_cons_sep, lower=0., upper=None) # separation between turbines
num_cons_spl = nwind*nobs
optProb.addConGroup('SPL', num_cons_spl, lower=0., upper=SPLlim/10.) # SPL limit
opt = SNOPT()
"""
# from pymatbridge import Matlab
# mlab = Matlab('/Applications/MATLAB_R2014b.app/bin/matlab')
# mlab.start()
#
# res = mlab.lsqcurvefit(sheet,param,posdntr,velodtr)
# print res
#
# mlab.stop
if fit_opt == 'snopt':
optProb = Optimization('VAWTWake_Velo', obj_func)
optProb.addObj('obj')
optProb.addVarGroup('param', 90, 'c', lower=None, upper=None, value=param0)
opt = SNOPT()
opt.setOption('Scale option',2)
if comp == 'mac':
opt.setOption('Print file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptSheet/SNOPT_print'+str(argv[1])+str(argv[2])+str(argv[3])+'ORD'+str(argv[4])+str(argv[5])+str(argv[6])+str(argv[7])+str(argv[8])+str(argv[9])+str(argv[10])+str(argv[11])+str(argv[12])+'.out')
opt.setOption('Summary file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptSheet/SNOPT_summary'+str(argv[1])+str(argv[2])+str(argv[3])+'ORD'+str(argv[4])+str(argv[5])+str(argv[6])+str(argv[7])+str(argv[8])+str(argv[9])+str(argv[10])+str(argv[11])+str(argv[12])+'.out')
elif comp == 'fsl':
opt.setOption('Print file','/fslhome/ebtingey/compute/VAWTWakeModel/CrossVal/SNOPT_Files/SNOPT_print'+str(argv[1])+str(argv[2])+str(argv[3])+'ORD'+str(argv[4])+str(argv[5])+str(argv[6])+str(argv[7])+str(argv[8])+str(argv[9])+str(argv[10])+str(argv[11])+str(argv[12])+'.out')
opt.setOption('Summary file','/fslhome/ebtingey/compute/VAWTWakeModel/CrossVal/SNOPT_Files/SNOPT_summary'+str(argv[1])+str(argv[2])+str(argv[3])+'ORD'+str(argv[4])+str(argv[5])+str(argv[6])+str(argv[7])+str(argv[8])+str(argv[9])+str(argv[10])+str(argv[11])+str(argv[12])+'.out')
result = opt(optProb, sens=None)
res = result.xStar['param']
exec('posdn'+name+'tr = posdns'+str(int(scvtr[i]))+'t'+str(int(tcvtr[i])))
exec('poslt'+name+'tr = poslts'+str(int(scvtr[i]))+'t'+str(int(tcvtr[i])))
exec('velod'+name+'tr = velods'+str(int(scvtr[i]))+'t'+str(int(tcvtr[i])))
exec('xt'+name+'tr = xttr[i]')
exec('ys'+name+'tr = ystr[i]')
for i in range(np.size(scvts)):
name = str(i+1)
exec('posdn'+name+'ts = posdns'+str(int(scvts[i]))+'t'+str(int(tcvts[i])))
exec('poslt'+name+'ts = poslts'+str(int(scvts[i]))+'t'+str(int(tcvts[i])))
exec('velod'+name+'ts = velods'+str(int(scvts[i]))+'t'+str(int(tcvts[i])))
exec('xt'+name+'ts = xtts[i]')
exec('ys'+name+'ts = ysts[i]')
# Optimization
optProb = Optimization('VAWTWake_Sheet', obj_func)
optProb.addObj('obj')
if comp == 'mac':
ordt = int(argv[1])
ords = int(argv[2])
elif comp == 'fsl':
ordt = int(argv[1])
ords = int(argv[2])
men0 = np.linspace(0.,0.,3*ords)
sdv10 = np.linspace(0.,0.,ordt*ords)
sdv20 = np.linspace(0.,0.,ordt*ords)
sdv30 = np.linspace(0.,0.,ordt*ords)
sdv40 = np.linspace(0.,0.,ordt*ords)
rat0 = np.linspace(0.,0.,ordt*ords)
def run(self):
"""
Excute pyOptsparse.
Note that pyOpt controls the execution, and the individual optimizers
(e.g., SNOPT) control the iteration.
Returns
-------
boolean
Failure flag; True if failed to converge, False is successful.
"""
problem = self._problem
model = problem.model
relevant = model._relevant
self.pyopt_solution = None
self._total_jac = None
self.iter_count = 0
fwd = problem._mode == 'fwd'
optimizer = self.options['optimizer']
# Only need initial run if we have linear constraints or if we are using an optimizer that
# doesn't perform one initially.
con_meta = self._cons
model_ran = False
if optimizer in run_required or np.any([con['linear'] for con in itervalues(self._cons)]):
with RecordingDebugging(self._get_name(), self.iter_count, self) as rec:
# Initial Run
model.run_solve_nonlinear()
rec.abs = 0.0
rec.rel = 0.0
model_ran = True
self.iter_count += 1
# compute dynamic simul deriv coloring or just sparsity if option is set
if coloring_mod._use_sparsity:
if self.options['dynamic_simul_derivs']:
coloring_mod.dynamic_simul_coloring(self, run_model=not model_ran,
do_sparsity=True)
elif self.options['dynamic_derivs_sparsity']:
coloring_mod.dynamic_sparsity(self)
opt_prob = Optimization(self.options['title'], self._objfunc)
# Add all design variables
param_meta = self._designvars
self._indep_list = indep_list = list(param_meta)
param_vals = self.get_design_var_values()
for name, meta in iteritems(param_meta):
opt_prob.addVarGroup(name, meta['size'], type='c',
value=param_vals[name],
lower=meta['lower'], upper=meta['upper'])
opt_prob.finalizeDesignVariables()
# Add all objectives
objs = self.get_objective_values()
for name in objs:
opt_prob.addObj(name)
self._quantities.append(name)
# Calculate and save derivatives for any linear constraints.
lcons = [key for (key, con) in iteritems(con_meta) if con['linear']]
if len(lcons) > 0:
_lin_jacs = self._compute_totals(of=lcons, wrt=indep_list, return_format='dict')
# convert all of our linear constraint jacs to COO format. Otherwise pyoptsparse will
# do it for us and we'll end up with a fully dense COO matrix and very slow evaluation
# of linear constraints!
to_remove = []
for jacdct in itervalues(_lin_jacs):
for n, subjac in iteritems(jacdct):
if isinstance(subjac, np.ndarray):
# we can safely use coo_matrix to automatically convert the ndarray
# since our linear constraint jacs are constant, so zeros won't become
# nonzero during the optimization.
mat = coo_matrix(subjac)
if mat.row.size > 0:
# convert to 'coo' format here to avoid an emphatic warning
# by pyoptsparse.
jacdct[n] = {'coo': [mat.row, mat.col, mat.data], 'shape': mat.shape}
# Add all equality constraints
for name, meta in iteritems(con_meta):
if meta['equals'] is None:
continue
size = meta['size']
lower = upper = meta['equals']
if fwd:
wrt = [v for v in indep_list if name in relevant[v]]
else:
rels = relevant[name]
wrt = [v for v in indep_list if v in rels]
if meta['linear']:
jac = {w: _lin_jacs[name][w] for w in wrt}
opt_prob.addConGroup(name, size, lower=lower, upper=upper,
linear=True, wrt=wrt, jac=jac)
else:
if name in self._res_jacs:
resjac = self._res_jacs[name]
jac = {n: resjac[n] for n in wrt}
else:
jac = None
opt_prob.addConGroup(name, size, lower=lower, upper=upper, wrt=wrt, jac=jac)
self._quantities.append(name)
# Add all inequality constraints
for name, meta in iteritems(con_meta):
if meta['equals'] is not None:
continue
size = meta['size']
# Bounds - double sided is supported
lower = meta['lower']
upper = meta['upper']
if fwd:
wrt = [v for v in indep_list if name in relevant[v]]
else:
rels = relevant[name]
wrt = [v for v in indep_list if v in rels]
if meta['linear']:
jac = {w: _lin_jacs[name][w] for w in wrt}
opt_prob.addConGroup(name, size, upper=upper, lower=lower,
linear=True, wrt=wrt, jac=jac)
else:
if name in self._res_jacs:
resjac = self._res_jacs[name]
jac = {n: resjac[n] for n in wrt}
else:
jac = None
opt_prob.addConGroup(name, size, upper=upper, lower=lower, wrt=wrt, jac=jac)
self._quantities.append(name)
# Instantiate the requested optimizer
try:
_tmp = __import__('pyoptsparse', globals(), locals(), [optimizer], 0)
opt = getattr(_tmp, optimizer)()
except Exception as err:
# Change whatever pyopt gives us to an ImportError, give it a readable message,
# but raise with the original traceback.
msg = "Optimizer %s is not available in this installation." % optimizer
reraise(ImportError, ImportError(msg), sys.exc_info()[2])
# Set optimization options
for option, value in self.opt_settings.items():
opt.setOption(option, value)
# Execute the optimization problem
if self.options['gradient method'] == 'pyopt_fd':
# Use pyOpt's internal finite difference
# TODO: Need to get this from OpenMDAO
# fd_step = problem.root.deriv_options['step_size']
fd_step = 1e-6
sol = opt(opt_prob, sens='FD', sensStep=fd_step, storeHistory=self.hist_file,
hotStart=self.hotstart_file)
elif self.options['gradient method'] == 'snopt_fd':
if self.options['optimizer'] == 'SNOPT':
# Use SNOPT's internal finite difference
# TODO: Need to get this from OpenMDAO
# fd_step = problem.root.deriv_options['step_size']
fd_step = 1e-6
sol = opt(opt_prob, sens=None, sensStep=fd_step, storeHistory=self.hist_file,
hotStart=self.hotstart_file)
else:
msg = "SNOPT's internal finite difference can only be used with SNOPT"
raise Exception(msg)
else:
# Use OpenMDAO's differentiator for the gradient
sol = opt(opt_prob, sens=self._gradfunc, storeHistory=self.hist_file,
hotStart=self.hotstart_file)
# Print results
if self.options['print_results']:
print(sol)
# Pull optimal parameters back into framework and re-run, so that
# framework is left in the right final state
dv_dict = sol.getDVs()
for name in indep_list:
self.set_design_var(name, dv_dict[name])
with RecordingDebugging(self._get_name(), self.iter_count, self) as rec:
model.run_solve_nonlinear()
rec.abs = 0.0
rec.rel = 0.0
self.iter_count += 1
# Save the most recent solution.
self.pyopt_solution = sol
try:
exit_status = sol.optInform['value']
self.fail = False
# These are various failed statuses.
if exit_status > 2:
self.fail = True
except KeyError:
# optimizers other than pySNOPT may not populate this dict
pass
return self.fail
wake_model=gauss_wrapper, wake_model_options={'nSamples': 0}, datasize=0,
params_IdepVar_func=add_gauss_params_IndepVarComps,
params_IndepVar_args={}))
prob.setup()
prob['model_params:integrate'] = False
prob['model_params:spread_mode'] = 'bastankhah'
prob['model_params:yaw_mode'] = 'bastankhah'
prob['model_params:n_std_dev'] = 4.
# prob['model_params:m'] = 0.33
# prob['model_params:Dw0'] = 1.3
tuning_obj_function(plot=True)
# initialize optimization problem
optProb = Optimization('Tuning Gaussian Model to SOWFA', tuning_obj_function)
optProb.addVarGroup('ke', 1, lower=0.0, upper=1.0, value=0.152, scalar=1)
# optProb.addVarGroup('spread_angle', 1, lower=0.0, upper=30.0, value=3.0, scalar=1)
optProb.addVarGroup('rotation_offset_angle', 1, lower=0.0, upper=5.0, value=1.5, scalar=1)
# optProb.addVarGroup('ky', 1, lower=0.0, upper=20.0, value=0.1, scalar=1E-4)
# optProb.addVarGroup('Dw0', 3, lower=np.zeros(3), upper=np.ones(3)*20., value=np.array([1.3, 1.3, 1.3]))#, scalar=1E-2)
# optProb.addVarGroup('m', 1, lower=0.1, upper=20.0, value=0.33, scalar=1E-3)
# add objective
optProb.addObj('obj', scale=1E-3)
# initialize optimizer
snopt = SNOPT()
# run optimizer
sol = snopt(optProb, sens='FD')
if nRotorPoints > 1:
prob['model_params:RotorPointsY'], prob['model_params:RotorPointsZ'] = sunflower_points(nRotorPoints)
print "setting rotor points"
elif model is 'floris':
prob = Problem(root=OptAEP(nTurbines=nTurbines, nDirections=nDirections, use_rotor_components=False,
wake_model=floris_wrapper,
wake_model_options={'nSamples': 0, 'use_rotor_components': False,
'differentiable': True}, datasize=0,
params_IdepVar_func=add_floris_params_IndepVarComps))
prob.setup()
prob['model_params:useWakeAngle'] = True
# initialize optimization problem
optProb = Optimization('Tuning %s Model to SOWFA' % model, tuning_obj_function)
if model is 'gauss':
# optProb.addVarGroup('ky', 1, lower=0.01, upper=1.0, value=0.022, scalar=1E1)
# optProb.addVarGroup('kz', 1, lower=0.01, upper=1.0, value=0.022, scalar=1E1)
# optProb.addVarGroup('I', 1, lower=0.04, upper=0.5, value=0.06, scalar=1E1)
optProb.addVarGroup('shear_exp', 1, lower=0.01, upper=1.0, value=0.15, scalar=1)
# optProb.addVarGroup('yshift', 1, lower=-126.4, upper=126.4, value=0.0)#, scalar=1E-3)
elif model is 'floris':
# optProb.addVarGroup('pP', 1, lower=0.0, upper=5.0, value=1.5) # , scalar=1E-1)
optProb.addVarGroup('kd', 1, lower=0.0, upper=1.0, value=0.15) # , scalar=1E-1)
optProb.addVarGroup('initialWakeAngle', 1, lower=-4.0, upper=4.0, value=1.5) # , scalar=1E-1)
optProb.addVarGroup('initialWakeDisplacement', 1, lower=-30.0, upper=30.0, value=-4.5) # , scalar=1E-1)
optProb.addVarGroup('bd', 1, lower=-1.0, upper=1.0, value=-0.01) # , scalar=1E-1)
optProb.addVarGroup('ke', 1, lower=0.0, upper=1.0, value=0.065) # , scalar=1E-1)
optProb.addVarGroup('me', 2, lower=np.array([-1.0, 0.0]), upper=np.array([0.0, 0.9]),
def optimize(func, x0, lb, ub, optimizer, A=[], b=[], Aeq=[], beq=[], args=[]):
global fcalls # keep track of function calls myself, seems to be an error in pyopt
fcalls = 1
# evalute initial point to get size information and determine if gradients included
out = func(x0, *args)
if len(out) == 4:
gradients = True
f, c, _, _ = out
else:
gradients = False
f, c = out
nx = len(x0)
nc = len(c)
nlin = len(b)
nleq = len(beq)
if hasattr(f, "__len__"):
nf = len(f) # multiobjective
else:
nf = 1
def objcon(xdict):
global fcalls
fcalls += 1
x = xdict['x']
outputs = {}
if gradients:
f, c, df, dc = func(x, *args)
# these gradients aren't directly used in this function but we will save them for later
outputs['g-obj'] = df
outputs['g-con'] = dc
outputs['g-x'] = x
else:
f, c = func(x, *args)
outputs['con'] = c
if nf == 1:
outputs['obj'] = f
else: # multiobjective
for i in range(nf):
outputs['obj%d' % i] = f[i]
fail = False
return outputs, fail
def grad(xdict, fdict):
# check if this was the x-location we just evaluated from func (should never happen)
if not np.array_equal(xdict['x'], fdict['g-x']):
f, c, df, dc = func(xdict['x'], *args)
global fcalls
fcalls += 1
else:
df = fdict['g-obj']
dc = fdict['g-con']
# populate gradients (the multiobjective optimizers don't use gradients so no change needed here)
gout = {}
gout['obj'] = {}
gout['obj']['x'] = df
gout['con'] = {}
gout['con']['x'] = dc
fail = False
return gout, fail
# setup problem
optProb = Optimization('optimization', objcon)
if nf == 1:
optProb.addObj('obj')
else: # multiobjective
for i in range(nf):
optProb.addObj('obj%d' % i)
optProb.addVarGroup('x', nx, lower=lb, upper=ub, value=x0)
# add nonlinear constraints
if nc > 0:
optProb.addConGroup('con', nc, upper=0.0)
# add linear inequality constraints
if nlin > 0:
optProb.addConGroup('linear-ineq', nlin, upper=b, linear=True, jac={'x': A})
# add linear equality constraints
if nleq > 0:
optProb.addConGroup('linear-ineq', nleq, upper=beq, lower=beq, linear=True, jac={'x': Aeq})
# check if gradients defined
if gradients:
sens = grad
else:
sens = 'FDR' # forward diff with relative step size
with warnings.catch_warnings(): # FIXME: ignore the FutureWarning until fixed
warnings.simplefilter("ignore")
# run optimization
sol = optimizer(optProb, sens=sens)
# save solution
xstar = sol.xStar['x']
fstar = sol.fStar
info = {}
info['fcalls'] = fcalls
info['time'] = sol.optTime
if sol.optInform:
info['code'] = sol.optInform
# FIXME: bug in how output of NLPQLP is returned
if optimizer.name == 'NLPQLP':
xtemp = xstar
xstar = np.zeros(nx)
for i in range(nx):
xstar[i] = xtemp[i, 0]
# FIXME: because of bug exists in all except SNOPT, also none return cstar
# if optimizer.name != 'SNOPT':
if gradients:
fstar, cstar, _, _ = func(xstar, *args)
else:
fstar, cstar = func(xstar, *args)
# FIXME: handle multiobjective NSGA2
if nf > 1 and optimizer.name == 'NSGA-II':
xstar = []
fstar = []
cstar = []
with open('nsga2_final_pop.out') as f:
# skip first two lines
f.readline()
f.readline()
for line in f:
values = line.split()
rank = values[nx + nc + nf + 1]
if rank == "1":
fstar.append(np.array(values[:nf]).astype(np.float))
cstar.append(np.array(values[nf:nf+nc]).astype(np.float))
xstar.append(np.array(values[nf+nc:nf+nc+nx]).astype(np.float))
xstar = np.array(xstar)
fstar = np.array(fstar)
cstar = -np.array(cstar) # negative sign because of nsga definition
if nc > 0:
info['max-c-vio'] = max(np.amax(cstar), 0.0)
return xstar, fstar, info
def __call__(self, optimizer, options=None):
""" Run optimization """
system = self._system
variables = self._variables
opt_prob = OptProblem('Optimization', self.obj_func)
for dv_name in variables['dv'].keys():
dv = variables['dv'][dv_name]
dv_id = dv['ID']
if dv['value'] is not None:
value = dv['value']
else:
value = system.vec['u'](dv_id)
scale = dv['scale']
lower = dv['lower']
upper = dv['upper']
size = system.vec['u'](dv_id).shape[0]
opt_prob.addVarGroup(dv_name, size, value=value, scale=scale,
lower=lower, upper=upper)
opt_prob.finalizeDesignVariables()
for func_name in variables['func'].keys():
func = variables['func'][func_name]
func_id = func['ID']
lower = func['lower']
upper = func['upper']
linear = func['linear']
get_jacs = func['get_jacs']
sys = func['sys']
size = system.vec['u'](func_id).shape[0]
if lower is None and upper is None:
opt_prob.addObj(func_name)
else:
if get_jacs is not None:
jacs_var = get_jacs()
dv_names = []
jacs = {}
for dv_var in jacs_var:
dv_id = self._system.get_id(dv_var)
dv_name = self._get_name(dv_id)
dv_names.append(dv_name)
jacs[dv_name] = jacs_var[dv_var]
opt_prob.addConGroup(func_name, size,
wrt=dv_names,
jac=jacs, linear=linear,
lower=lower, upper=upper)
elif sys is not None:
dv_names = []
for dv_name in variables['dv'].keys():
dv_id = variables['dv'][dv_name]['ID']
if dv_id in sys.vec['u']:
dv_names.append(dv_name)
opt_prob.addConGroup(func_name, size,
wrt=dv_names,
lower=lower, upper=upper)
else:
opt_prob.addConGroup(func_name, size,
lower=lower, upper=upper)
if options is None:
options = {}
opt = Optimizer(optimizer, options=options)
opt.setOption('Iterations limit', int(1e6))
#opt.setOption('Verify level', 3)
sol = opt(opt_prob, sens=self.sens_func, storeHistory='hist.hst')
print sol
try:
exit_status = sol.optInform['value']
self.exit_flag = 1
if exit_status > 2: # bad
self.exit_flag = 0
except KeyError: #nothing is here, so something bad happened!
self.exit_flag = 0
def run(self, problem):
"""pyOpt execution. Note that pyOpt controls the execution, and the
individual optimizers (i.e., SNOPT) control the iteration.
Args
----
problem : `Problem`
Our parent `Problem`.
"""
self.pyopt_solution = None
rel = problem.root._probdata.relevance
# Metadata Setup
self.metadata = create_local_meta(None, self.options['optimizer'])
self.iter_count = 0
update_local_meta(self.metadata, (self.iter_count,))
# Initial Run
with problem.root._dircontext:
problem.root.solve_nonlinear(metadata=self.metadata)
opt_prob = Optimization(self.options['title'], self._objfunc)
# Add all parameters
param_meta = self.get_desvar_metadata()
self.indep_list = indep_list = list(param_meta)
param_vals = self.get_desvars()
for name, meta in iteritems(param_meta):
opt_prob.addVarGroup(name, meta['size'], type='c',
value=param_vals[name],
lower=meta['lower'], upper=meta['upper'])
opt_prob.finalizeDesignVariables()
# Figure out parameter subsparsity for paramcomp index connections.
# sub_param_conns is empty unless there are some index conns.
# full_param_conns gets filled with the connections to the entire
# parameter so that those params can be filtered out of the sparse
# set if the full path is also relevant
sub_param_conns = {}
full_param_conns = {}
for name in indep_list:
pathname = problem.root.unknowns.metadata(name)['pathname']
sub_param_conns[name] = {}
full_param_conns[name] = set()
for target, info in iteritems(problem.root.connections):
src, indices = info
if src == pathname:
if indices is not None:
# Need to map the connection indices onto the desvar
# indices if both are declared.
dv_idx = param_meta[name].get('indices')
indices = set(indices)
if dv_idx is not None:
indices.intersection_update(dv_idx)
ldv_idx = list(dv_idx)
mapped_idx = [ldv_idx.index(item) for item in indices]
sub_param_conns[name][target] = mapped_idx
else:
sub_param_conns[name][target] = indices
else:
full_param_conns[name].add(target)
# Add all objectives
objs = self.get_objectives()
self.quantities = list(objs)
self.sparsity = OrderedDict()
self.sub_sparsity = OrderedDict()
for name in objs:
opt_prob.addObj(name)
self.sparsity[name] = self.indep_list
# Calculate and save gradient for any linear constraints.
lcons = self.get_constraints(lintype='linear').keys()
if len(lcons) > 0:
self.lin_jacs = problem.calc_gradient(indep_list, lcons,
return_format='dict')
#print("Linear Gradient")
#print(self.lin_jacs)
# Add all equality constraints
econs = self.get_constraints(ctype='eq', lintype='nonlinear')
con_meta = self.get_constraint_metadata()
self.quantities += list(econs)
for name in self.get_constraints(ctype='eq'):
meta = con_meta[name]
size = meta['size']
lower = upper = meta['equals']
# Sparsify Jacobian via relevance
rels = rel.relevant[name]
wrt = rels.intersection(indep_list)
self.sparsity[name] = wrt
if meta['linear']:
opt_prob.addConGroup(name, size, lower=lower, upper=upper,
linear=True, wrt=wrt,
jac=self.lin_jacs[name])
else:
jac = self._build_sparse(name, wrt, size, param_vals,
sub_param_conns, full_param_conns, rels)
opt_prob.addConGroup(name, size, lower=lower, upper=upper,
wrt=wrt, jac=jac)
# Add all inequality constraints
incons = self.get_constraints(ctype='ineq', lintype='nonlinear')
self.quantities += list(incons)
for name in self.get_constraints(ctype='ineq'):
meta = con_meta[name]
size = meta['size']
# Bounds - double sided is supported
lower = meta['lower']
upper = meta['upper']
# Sparsify Jacobian via relevance
rels = rel.relevant[name]
wrt = rels.intersection(indep_list)
self.sparsity[name] = wrt
if meta['linear']:
opt_prob.addConGroup(name, size, upper=upper, lower=lower,
linear=True, wrt=wrt,
jac=self.lin_jacs[name])
else:
jac = self._build_sparse(name, wrt, size, param_vals,
sub_param_conns, full_param_conns, rels)
opt_prob.addConGroup(name, size, upper=upper, lower=lower,
wrt=wrt, jac=jac)
# Instantiate the requested optimizer
optimizer = self.options['optimizer']
try:
exec('from pyoptsparse import %s' % optimizer)
except ImportError:
msg = "Optimizer %s is not available in this installation." % \
optimizer
raise ImportError(msg)
optname = vars()[optimizer]
opt = optname()
#Set optimization options
for option, value in self.opt_settings.items():
opt.setOption(option, value)
self._problem = problem
# Execute the optimization problem
if self.options['pyopt_diff']:
# Use pyOpt's internal finite difference
fd_step = problem.root.fd_options['step_size']
sol = opt(opt_prob, sens='FD', sensStep=fd_step, storeHistory=self.hist_file)
else:
# Use OpenMDAO's differentiator for the gradient
sol = opt(opt_prob, sens=self._gradfunc, storeHistory=self.hist_file)
self._problem = None
# Print results
if self.options['print_results']:
print(sol)
# Pull optimal parameters back into framework and re-run, so that
# framework is left in the right final state
dv_dict = sol.getDVs()
for name in indep_list:
val = dv_dict[name]
self.set_desvar(name, val)
with self.root._dircontext:
self.root.solve_nonlinear(metadata=self.metadata)
# Save the most recent solution.
self.pyopt_solution = sol
try:
exit_status = sol.optInform['value']
self.exit_flag = 1
if exit_status > 2: # bad
self.exit_flag = 0
except KeyError: #nothing is here, so something bad happened!
self.exit_flag = 0
def run(self, problem):
"""pyOpt execution. Note that pyOpt controls the execution, and the
individual optimizers (i.e., SNOPT) control the iteration.
Args
----
problem : `Problem`
Our parent `Problem`.
"""
self.pyopt_solution = None
rel = problem.root._probdata.relevance
# Metadata Setup
self.metadata = create_local_meta(None, self.options['optimizer'])
self.iter_count = 0
update_local_meta(self.metadata, (self.iter_count,))
# Initial Run
problem.root.solve_nonlinear(metadata=self.metadata)
opt_prob = Optimization(self.options['title'], self._objfunc)
# Add all parameters
param_meta = self.get_desvar_metadata()
self.indep_list = indep_list = list(iterkeys(param_meta))
param_vals = self.get_desvars()
for name, meta in iteritems(param_meta):
opt_prob.addVarGroup(name, meta['size'], type='c',
value=param_vals[name],
lower=meta['lower'], upper=meta['upper'])
opt_prob.finalizeDesignVariables()
# Add all objectives
objs = self.get_objectives()
self.quantities = list(iterkeys(objs))
self.sparsity = OrderedDict() #{}
for name in objs:
opt_prob.addObj(name)
self.sparsity[name] = self.indep_list
# Calculate and save gradient for any linear constraints.
lcons = self.get_constraints(lintype='linear').keys()
if len(lcons) > 0:
self.lin_jacs = problem.calc_gradient(indep_list, lcons,
return_format='dict')
#print("Linear Gradient")
#print(self.lin_jacs)
# Add all equality constraints
econs = self.get_constraints(ctype='eq', lintype='nonlinear')
con_meta = self.get_constraint_metadata()
self.quantities += list(iterkeys(econs))
for name in self.get_constraints(ctype='eq'):
size = con_meta[name]['size']
lower = upper = con_meta[name]['equals']
# Sparsify Jacobian via relevance
wrt = rel.relevant[name].intersection(indep_list)
self.sparsity[name] = wrt
if con_meta[name]['linear'] is True:
opt_prob.addConGroup(name, size, lower=lower, upper=upper,
linear=True, wrt=wrt,
jac=self.lin_jacs[name])
else:
opt_prob.addConGroup(name, size, lower=lower, upper=upper,
wrt=wrt)
# Add all inequality constraints
incons = self.get_constraints(ctype='ineq', lintype='nonlinear')
self.quantities += list(iterkeys(incons))
for name in self.get_constraints(ctype='ineq'):
size = con_meta[name]['size']
# Bounds - double sided is supported
lower = con_meta[name]['lower']
upper = con_meta[name]['upper']
# Sparsify Jacobian via relevance
wrt = rel.relevant[name].intersection(indep_list)
self.sparsity[name] = wrt
if con_meta[name]['linear'] is True:
opt_prob.addConGroup(name, size, upper=upper, lower=lower,
linear=True, wrt=wrt,
jac=self.lin_jacs[name])
else:
opt_prob.addConGroup(name, size, upper=upper, lower=lower,
wrt=wrt)
# Instantiate the requested optimizer
optimizer = self.options['optimizer']
try:
exec('from pyoptsparse import %s' % optimizer)
except ImportError:
msg = "Optimizer %s is not available in this installation." % \
optimizer
raise ImportError(msg)
optname = vars()[optimizer]
opt = optname()
#Set optimization options
for option, value in self.opt_settings.items():
opt.setOption(option, value)
self._problem = problem
# Execute the optimization problem
if self.options['pyopt_diff'] is True:
# Use pyOpt's internal finite difference
fd_step = problem.root.fd_options['step_size']
sol = opt(opt_prob, sens='FD', sensStep=fd_step, storeHistory=self.hist_file)
else:
# Use OpenMDAO's differentiator for the gradient
sol = opt(opt_prob, sens=self._gradfunc, storeHistory=self.hist_file)
self._problem = None
# Print results
if self.options['print_results'] is True:
print(sol)
# Pull optimal parameters back into framework and re-run, so that
# framework is left in the right final state
dv_dict = sol.getDVs()
for name in indep_list:
val = dv_dict[name]
self.set_desvar(name, val)
self.root.solve_nonlinear(metadata=self.metadata)
# Save the most recent solution.
self.pyopt_solution = sol
try:
exit_status = sol.optInform['value']
self.exit_flag = 1
if exit_status > 2: # bad
self.exit_flag = 0
except KeyError: #nothing is here, so something bad happened!
self.exit_flag = 0
Cp = 0.4
dens = 1.225
velf = 15.
power_iso = powerval(Cp,dens,velf,turb_dia)
x0 = xt
y0 = yt
area = 2
xlow = points[0]-spacing*area
xupp = points[-1]+spacing*area
ylow = points[0]-spacing*area
yupp = points[-1]+spacing*area
optProb = Optimization('VAWT_Power', obj_func)
optProb.addObj('obj')
n = np.size(x0)
optProb.addVarGroup('xvars', n, 'c', lower=xlow, upper=xupp, value=x0)
optProb.addVarGroup('yvars', n, 'c', lower=ylow, upper=yupp, value=y0)
num_cons_sep = (n-1)*n/2
optProb.addConGroup('sep', num_cons_sep, lower=0, upper=None)
opt = SNOPT()
opt.setOption('Scale option',0)
res = opt(optProb, sens=None)
print res
pow = np.array(-1*res.fStar)
def execute(self):
"""pyOpt execution. Note that pyOpt controls the execution, and the
individual optimizers control the iteration."""
self.pyOpt_solution = None
self.run_iteration()
opt_prob = Optimization(self.title, self.objfunc)
# Add all parameters
self.param_type = {}
self.nparam = self.total_parameters()
param_list = []
for name, param in self.get_parameters().iteritems():
# We need to identify Enums, Lists, Dicts
metadata = param.get_metadata()[1]
values = param.evaluate()
# Assuming uniform enumerated, discrete, or continuous for now.
val = values[0]
choices = []
if 'values' in metadata and \
isinstance(metadata['values'], (list, tuple, array, set)):
vartype = 'd'
choices = metadata['values']
elif isinstance(val, bool):
vartype = 'd'
choices = [True, False]
elif isinstance(val, (int, int32, int64)):
vartype = 'i'
elif isinstance(val, (float, float32, float64)):
vartype = 'c'
else:
msg = 'Only continuous, discrete, or enumerated variables' \
' are supported. %s is %s.' % (name, type(val))
self.raise_exception(msg, ValueError)
self.param_type[name] = vartype
lower_bounds = param.get_low()
upper_bounds = param.get_high()
opt_prob.addVarGroup(name, len(values), type=vartype,
lower=lower_bounds, upper=upper_bounds,
value=values, choices=choices)
param_list.append(name)
# Add all objectives
for name, obj in self.get_objectives().iteritems():
name = '%s.out0' % obj.pcomp_name
opt_prob.addObj(name)
# Calculate and save gradient for any linear constraints.
lcons = self.get_constraints(linear=True).values() + \
self.get_2sided_constraints(linear=True).values()
if len(lcons) > 0:
lcon_names = ['%s.out0' % obj.pcomp_name for obj in lcons]
self.lin_jacs = self.workflow.calc_gradient(param_list, lcon_names,
return_format='dict')
# Add all equality constraints
nlcons = []
for name, con in self.get_eq_constraints().iteritems():
size = con.size
lower = zeros((size))
upper = zeros((size))
name = '%s.out0' % con.pcomp_name
if con.linear is True:
opt_prob.addConGroup(name, size, lower=lower, upper=upper,
linear=True, wrt=param_list,
jac=self.lin_jacs[name])
else:
opt_prob.addConGroup(name, size, lower=lower, upper=upper)
nlcons.append(name)
# Add all inequality constraints
for name, con in self.get_ineq_constraints().iteritems():
size = con.size
upper = zeros((size))
name = '%s.out0' % con.pcomp_name
if con.linear is True:
opt_prob.addConGroup(name, size, upper=upper, linear=True,
wrt=param_list, jac=self.lin_jacs[name])
else:
opt_prob.addConGroup(name, size, upper=upper)
nlcons.append(name)
# Add all double_sided constraints
for name, con in self.get_2sided_constraints().iteritems():
size = con.size
upper = con.high * ones((size))
lower = con.low * ones((size))
name = '%s.out0' % con.pcomp_name
if con.linear is True:
opt_prob.addConGroup(name, size, upper=upper, lower=lower,
linear=True, wrt=param_list,
jac=self.lin_jacs[name])
else:
opt_prob.addConGroup(name, size, upper=upper, lower=lower)
nlcons.append(name)
self.objs = self.list_objective_targets()
self.nlcons = nlcons
# Instantiate the requested optimizer
optimizer = self.optimizer
try:
exec('from pyoptsparse import %s' % optimizer)
except ImportError:
msg = "Optimizer %s is not available in this installation." % \
optimizer
self.raise_exception(msg, ImportError)
optname = vars()[optimizer]
opt = optname()
# Set optimization options
for option, value in self.options.iteritems():
opt.setOption(option, value)
# Execute the optimization problem
if self.pyopt_diff:
# Use pyOpt's internal finite difference
sol = opt(opt_prob, sens='FD', sensStep=self.gradient_options.fd_step)
else:
# Use OpenMDAO's differentiator for the gradient
sol = opt(opt_prob, sens=self.gradfunc)
# Print results
if self.print_results:
print sol
# Pull optimal parameters back into framework and re-run, so that
# framework is left in the right final state
dv_dict = sol.getDVs()
param_types = self.param_type
for name, param in self.get_parameters().iteritems():
val = dv_dict[name]
if param_types[name] == 'i':
val = int(round(val))
self.set_parameter_by_name(name, val)
self.run_iteration()
# Save the most recent solution.
self.pyOpt_solution = sol
def fit(s,t,length,plot,comp,read_data,opt_print):
global posdn
global poslt
global velod
t2 = t+'.0'
wfit = s+'_'+t2
wfit2 = s+'_'+t2
wfit3 = s+'_'+t2
wfit4 = s+'_'+t2
wfit5 = s+'_'+t2
wfit6 = s+'_'+t2
length2 = length
length3 = length
length4 = length
length5 = length
length6 = length
wind = 15.
wind2 = 14.
wind3 = 12.
wind4 = 16.
rad = 3.
dia = rad*2.
tsr = float(wfit[3]+'.'+wfit[4]+wfit[5])
rot = tsr*wind/rad
rot2 = tsr*wind2/rad
rot3 = tsr*wind3/rad
rot4 = tsr*wind4/rad
rot5 = 17.
rot6 = 18.
wind5 = rot5*rad/tsr
wind6 = rot6*rad/tsr
if comp == 'mac':
# fdata = '/Users/ning1/Documents/Flow Lab/STAR-CCM+/NACA0021/MoveForward/test.csv'
fdata = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/Velocity Sections/'+wfit+'.csv'
fdata2 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel14/Velocity/'+wfit2+'.csv'
fdata3 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel12/Velocity/'+wfit3+'.csv'
fdata4 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel16/Velocity/'+wfit4+'.csv'
fdata5 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot17/Velocity/'+wfit5+'.csv'
fdata6 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot18/Velocity/'+wfit6+'.csv'
elif comp == 'fsl':
fdata = '/fslhome/ebtingey/compute/moveForward/Velocity/'+wfit+'.csv'
fdata2 = '/fslhome/ebtingey/compute/moveForward/vel14/Velocity/'+wfit2+'.csv'
fdata3 = '/fslhome/ebtingey/compute/moveForward/vel12/Velocity/'+wfit3+'.csv'
fdata4 = '/fslhome/ebtingey/compute/moveForward/vel16/Velocity/'+wfit4+'.csv'
fdata5 = '/fslhome/ebtingey/compute/moveForward/rot17/Velocity/'+wfit5+'.csv'
fdata6 = '/fslhome/ebtingey/compute/moveForward/rot18/Velocity/'+wfit6+'.csv'
elif comp == 'win':
fdata = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//Velocity Sections//'+wfit+'.csv'
fdata2 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel14//Velocity//'+wfit2+'.csv'
fdata3 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel12//Velocity//'+wfit3+'.csv'
fdata4 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel16//Velocity//'+wfit4+'.csv'
fdata5 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//rot17//Velocity//'+wfit5+'.csv'
fdata6 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//rot18//Velocity//'+wfit6+'.csv'
if read_data ==1:
posdn,poslt,velod = starccm_read(np.array([fdata]),dia,np.array([wind]),length,opt_print)
if read_data ==2:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2]),dia,np.array([wind,wind2]),length,opt_print)
if read_data ==3:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3]),dia,np.array([wind,wind2,wind3]),length,opt_print)
if read_data ==4:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3,fdata4]),dia,np.array([wind,wind2,wind3,wind4]),length,opt_print)
if read_data ==5:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3,fdata4,fdata5]),dia,np.array([wind,wind2,wind3,wind4,wind5]),length,opt_print)
if read_data ==6:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3,fdata4,fdata5,fdata6]),dia,np.array([wind,wind2,wind3,wind4,wind5,wind6]),length,opt_print)
if plot == True:
if read_data ==1:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata]),dia,np.array([wind]),opt_print)
if read_data ==2:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2]),dia,np.array([wind,wind2]),opt_print)
if read_data ==3:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3]),dia,np.array([wind,wind2,wind3]),opt_print)
if read_data ==4:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3,fdata4]),dia,np.array([wind,wind2,wind3,wind4]),opt_print)
if read_data ==5:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3,fdata4,fdata5]),dia,np.array([wind,wind2,wind3,wind4,wind5]),opt_print)
if read_data ==6:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3,fdata4,fdata5,fdata6]),dia,np.array([wind,wind2,wind3,wind4,wind5,wind6]),opt_print)
start = length/30.
xd = np.linspace(start,length,30)/dia
## Optimization
optProb = Optimization('VAWTWake_Velo', obj_func)
optProb.addObj('obj')
spr10 = 10.0
pow10 = 5.0
pow20 = 0.5
pow30 = 1.0
spr20 = 2.0
skw0 = 0.0
odr0 = 2.0
scl10 = 0.5
scl20 = 0.1
scl30 = 20.0
# spr10 = 213.8593169
# pow10 = 10.39210953
# pow20 = 2.086951239
# pow30 = 0.035659319
# spr20 = 0.007589688
# skw0 = 10.63462155
# odr0 = 2.0
# scl10 = 0.537566448
# scl20 = 0.041077603
# scl30 = 56.74689143
param0 = np.array([spr10,pow10,pow20,pow30,spr20,skw0,odr0,scl10,scl20,scl30])
param_l = np.array([0.,0.,0.,0.,0.,None,0.,0.,0.,0.])
param_u = np.array([None,None,None,None,None,None,None,1.,1.,None])
nparam = np.size(param0)
optProb.addVarGroup('param', nparam, 'c', lower=param_l, upper=param_u, value=param0)
opt = SNOPT()
opt.setOption('Scale option',2)
if comp == 'mac':
opt.setOption('Print file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_summary'+s+'_'+t+'.out')
elif comp == 'fsl':
opt.setOption('Print file','/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_summary'+s+'_'+t+'.out')
elif comp == 'win':
opt.setOption('Print file','C://Users//TingeyPC//Documents//FLOW Lab//VAWTWakeModel//wake_model//data//optVel//SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','C://Users//TingeyPC//Documents//FLOW Lab//VAWTWakeModel//wake_model//data//OptVel//SNOPT_summary'+s+'_'+t+'.out')
res = opt(optProb, sens=None)
if opt_print == True:
print res
pow = res.fStar
paramf = res.xStar['param']
if opt_print == True:
print paramf[0]
print paramf[1]
print paramf[2]
print paramf[3]
print paramf[4]
print paramf[5]
print paramf[6]
print paramf[7]
print paramf[8]
print paramf[9]
spr1 = paramf[0]
pow1 = paramf[1]
pow2 = paramf[2]
pow3 = paramf[3]
spr2 = paramf[4]
skw = paramf[5]
odr = paramf[6]
scl1 = paramf[7]
scl2 = paramf[8]
scl3 = paramf[9]
paper = False
if plot == True:
if paper == True:
for i in range(30):
name = str(i+1)
ind = str(i)
plt.figure(1)
ax1 = plt.subplot(5,6,i+1)
color = 'bo'
color2 = 'r-'
fs = 15
lab = 'CFD'
lab2 = 'Trend'
tex = '$x/D$ = '+str("{0:.2f}".format(x[i]/dia))
exec('xfit = np.linspace(min(pos'+name+'/dia)-1.,max(pos'+name+'/dia)+1.,500)')
if i == 5:
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color,label=lab)')
skw_v,spr_v,scl_v,rat_v,spr_v = paramfit(xd[i],skw,spr,scl,rat,spr)
plt.plot(veldist(xfit,skw_v,spr_v,scl_v,rat_v,spr_v),xfit,'r-',linewidth=2,label=lab2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
plt.legend(loc="upper left", bbox_to_anchor=(1,1),fontsize=fs)
else:
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color)')
skw_v,spr_v,scl_v,rat_v,spr_v = paramfit(xd[i],skw,spr,scl,rat,spr)
plt.plot(veldist(xfit,skw_v,spr_v,scl_v,rat_v,spr_v),xfit,'r-',linewidth=2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
plt.text(0.3,0.8,tex,fontsize=fs)
if i <= 23:
plt.setp(ax1.get_xticklabels(), visible=False)
else:
plt.xlabel('$y/D$',fontsize=fs)
plt.xticks(fontsize=fs)
if i == 0 or i == 6 or i == 12 or i == 18 or i ==24:
plt.ylabel(r'$u/U_\infty$',fontsize=fs)
plt.yticks(fontsize=fs)
else:
plt.setp(ax1.get_yticklabels(), visible=False)
elif paper == False:
for i in range(30):
name = str(i+1)
plt.figure(1)
plt.subplot(5,6,i+1)
color = 'bo'
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color)')
plt.plot(veldist(xd[i],xfit,spr1,pow1,pow2,pow3,spr2,skw,odr,scl1,scl2,scl3),xfit,'r-',linewidth=2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
# plt.legend(loc=1)
plt.xlabel('Normalized Velocity')
plt.ylabel('$y/D$')
return spr1,pow1,pow2,pow3,spr2,skw,odr,scl1,scl2,scl3
exec('posdn'+name+'tr = posdns'+str(int(scvtr[i]))+'t'+str(int(tcvtr[i])))
exec('poslt'+name+'tr = poslts'+str(int(scvtr[i]))+'t'+str(int(tcvtr[i])))
exec('velod'+name+'tr = velods'+str(int(scvtr[i]))+'t'+str(int(tcvtr[i])))
exec('xt'+name+'tr = xttr[i]')
exec('ys'+name+'tr = ystr[i]')
for i in range(np.size(scvts)):
name = str(i+1)
exec('posdn'+name+'ts = posdns'+str(int(scvts[i]))+'t'+str(int(tcvts[i])))
exec('poslt'+name+'ts = poslts'+str(int(scvts[i]))+'t'+str(int(tcvts[i])))
exec('velod'+name+'ts = velods'+str(int(scvts[i]))+'t'+str(int(tcvts[i])))
exec('xt'+name+'ts = xtts[i]')
exec('ys'+name+'ts = ysts[i]')
# Optimization
optProb = Optimization('VAWTWake_Sheet', obj_func)
optProb.addObj('obj')
if comp == 'mac':
ordt = 5
ords = 3
elif comp == 'fsl':
ordt = int(argv[1])
ords = int(argv[2])
ment0 = np.linspace(0.,0.,3)
sdv1t0 = np.linspace(0.,0.,ordt)
sdv2t0 = np.linspace(0.,0.,ordt)
sdv3t0 = np.linspace(0.,0.,ordt)
sdv4t0 = np.linspace(0.,0.,ordt)
ratt0 = np.linspace(0.,0.,ordt)
