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": 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
opt(optProb, sens=sens)
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, 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 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 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 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 test_obj(self):
termcomp = TerminateComp(max_obj=2)
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)
try:
opt = SNOPT()
except:
raise unittest.SkipTest('Optimizer not available: SNOPT')
sol = opt(optProb, sens=termcomp.sens)
self.assertEqual(termcomp.obj_count, 3)
# Exit code for user requested termination.
self.assertEqual(sol.optInform['value'][0], 71)
def test_opt_bug_print_2con(self):
# Optimization Object
optProb = Optimization('Paraboloid', objfunc_2con)
# 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')
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)
# Optimizer
try:
opt = SNOPT(optOptions = {'Major feasibility tolerance' : 1e-1})
except:
raise unittest.SkipTest('Optimizer not available: SNOPT')
sol = opt(optProb, sens=sens)
print(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
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:
print(optProb)
optProb.printSparsity()
# Global Optimizer: ALPSO
opt1 = ALPSO()
# Get first Solution
sol1 = opt1(optProb)
print(sol1)
# Now run the previous solution with SNOPT
opt2 = SNOPT()
sol2 = opt2(sol1)
# Check Solution
print(sol2)
optProb.addVarGroup('sdv1s', ords, 'c', lower=None, upper=None, value=sdv1s0)
optProb.addVarGroup('sdv2s', ords, 'c', lower=None, upper=None, value=sdv2s0)
optProb.addVarGroup('sdv3s', ords, 'c', lower=None, upper=None, value=sdv3s0)
optProb.addVarGroup('sdv4s', ords, 'c', lower=None, upper=None, value=sdv4s0)
optProb.addVarGroup('rats', ords, 'c', lower=None, upper=None, value=rats0)
optProb.addVarGroup('tnss', ords, 'c', lower=None, upper=None, value=tnss0)
optProb.addVarGroup('spr1s', ords, 'c', lower=None, upper=None, value=spr1s0)
optProb.addVarGroup('spr2s', ords, 'c', lower=None, upper=None, value=spr2s0)
optProb.addVarGroup('spr3s', ords, 'c', lower=None, upper=None, value=spr3s0)
optProb.addVarGroup('spr4s', ords, 'c', lower=None, upper=None, value=spr4s0)
optProb.addVarGroup('scl1s', ords, 'c', lower=None, upper=None, value=scl1s0)
optProb.addVarGroup('scl2s', ords, 'c', lower=None, upper=None, value=scl2s0)
optProb.addVarGroup('scl3s', ords, 'c', lower=None, upper=None, value=scl3s0)
opt = SNOPT()
opt.setOption('Scale option',0)
if comp == 'mac':
opt.setOption('Print file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptSheet/cv_cvx_SNOPT_print.out')
opt.setOption('Summary file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptSheet/cv_cvx_SNOPT_summary.out')
elif comp == 'fsl':
opt.setOption('Print file','/fslhome/ebtingey/compute/VAWTWakeModel/OptSheet/cv_cvx_'+argv[1]+argv[2]+argv[3]+argv[4]+'_SNOPT_OptSheet_print.out')
opt.setOption('Summary file','/fslhome/ebtingey/compute/VAWTWakeModel/OptSheet/cv_cvx_'+argv[1]+argv[2]+argv[3]+argv[4]+'_SNOPT_OptSheet_summary.out')
res = opt(optProb, sens=None)
if opt_print == True:
print res
error = res.fStar
mentf = res.xStar['ment']
sdv1tf = res.xStar['sdv1t']
# 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']
elif fit_opt == 'scipy':
res,_ = curve_fit(sheet,posdntr,velodtr,p0=param0,maxfev=2820000)
omega_r = 1500 # rpm
R_rotor = 0.705 # m
fuel_cap = 1.0 # kg
theta_hover = 20 # deg
x0 = scale(
[eng_displace, fuel_rate, omega_r, R_rotor, fuel_cap, theta_hover])
# x0 = [ 4.3441622, 0.16620041, 0.10885692, 2.78460696, 6.76453586, 0.85782237]
x0 = [4.61280557, 0.17376677, 0.08200489, 1.5, 7.32432873, 0.86800068]
lb = [0, 0, 0, 0, 0, 0]
ub = scale([1200.0, 1.0, 7500.0, 3.0, 12.0, 30.0])
setPayload(5.0)
# print obj_func_print(x0)
optimizer = SNOPT()
# optimizer = NSGA2()
# optimizer.setOption('maxGen', 200)
xopt, fopt, info = optimize(obj_func, x0, lb, ub, optimizer)
print 'SNOPT:', xopt, fopt, info
out = obj_func_print(xopt)
# Create the pareto frunt
steps = 20
payloads = np.linspace(0.0, 10.0, num=steps)
flight_times = np.zeros(steps)
engine_size = np.zeros(steps)
for i in range(steps):
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()
opt.setOption('Scale option',0)
opt.setOption('Iterations limit',1000000)
res = opt(optProb)
# Printing optimization results (SNOPT format)
print res
# Final results (scaled back to original values)
pow = np.array(-1*res.fStar)*1e4
xf = res.xStar['xvars']*100.
yf = res.xStar['yvars']*100.
rpmf = res.xStar['rpm']*10.
rpmfw = np.zeros((nwind,nturb))
k = 0
for i in range(nwind):
for j in range(nturb):
optProb.addVarGroup('me',
2,
lower=np.array([-1.0, 0.0]),
upper=np.array([0.0, 0.9]),
value=np.array([-0.5, 0.3])) # , scalar=1E-1)
optProb.addVarGroup('MU',
2,
lower=np.array([0.0, 1.5]),
upper=np.array([1.0, 20.0]),
value=np.array([0.5, 5.5])) # , scalar=1E-1)
optProb.addVarGroup('aU', 1, lower=0.0, upper=20.0,
value=5.0) # , scalar=1E-1)
optProb.addVarGroup('bU', 1, lower=0.0, upper=5.0,
value=1.66) # , scalar=1E-1)
optProb.addVarGroup('cos_spread', 1, lower=0.0, upper=10.0,
value=2.0) # , scalar=1E-1)
# add objective
optProb.addObj('obj', scale=1E0)
# initialize optimizer
snopt = SNOPT(options={'Print file': 'SNOPT_print_tune_all.out'})
# run optimizer
sol = snopt(optProb, sens=None)
# print solution
print sol
# plot fit
tuning_obj_function(xdict=sol.xStar, plot=True)
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
input = {'dx':dx,'dy':dy,'offset':offset,'rotate':rotate}
funcs,_ = obj_func_grid(input)
AEPstart = funcs['obj']
print AEPstart
nCalls = 0
"""Optimization"""
optProb = Optimization('Wind_Farm_AEP', obj_func_grid)
optProb.addObj('obj')
optProb.addVar('dx', type='c', lower=0., upper=None, value=dx)
optProb.addVar('dy', type='c', lower=0., upper=None, value=dy)
optProb.addVar('offset', type='c', lower=None, upper=None, value=offset)
optProb.addVar('rotate', type='c', lower=None, upper=None, value=rotate)
num_cons_sep = (nTurbs-1)*nTurbs/2
optProb.addConGroup('sep', num_cons_sep, lower=0., upper=None)
optProb.addConGroup('bound', nTurbs, lower=0., upper=None)
opt = SNOPT()
opt.setOption('Scale option',0)
opt.setOption('Iterations limit',1000000)
opt.setOption('Summary file','summary_grid.out')
opt.setOption('Major optimality tolerance',1.e-5)
opt.setOption('Major feasibility tolerance',1.e-6)
res = opt(optProb)
dx_f = res.xStar['dx']
dy_f = res.xStar['dy']
o_f = res.xStar['offset']
r_f = res.xStar['rotate']
# print 'dx_f: ', dx_f
# print 'dy_f: ', dy_f
# print 'offset_f: ', o_f
# print 'rotate_f: ', r_f
print nCalls
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
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')
print sol
# tuning_obj_function(xdict={'ke': sol.xStar['ke'], 'spread_angle': sol.xStar['spread_angle'],
# 'rotation_offset_angle': sol.xStar['rotation_offset_angle'],
# 'ky': sol.xStar['ky']}, plot=True)
tuning_obj_function(xdict=sol.xStar, plot=True)
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)
xf = res.xStar['xvars']
yf = res.xStar['yvars']
power_turb = funcs['power_turb']
veleff = funcs['veleff']
print 'The power is:',pow,'kJ'
print 'The isolated power is:',power_iso*np.size(xt),'kJ'
print 'The x-locations:',xf
print 'The y-locations:',yf
print 'The effective wind speeds:',veleff
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
jdist = cp.MvNormal(initial_seed, std_dev)
collocation = StochasticCollocation(3, "Normal")
collocation_grad = StochasticCollocation(3,
"Normal",
QoI_dimensions=rv_systemsize)
threshold_factor = 0.9
dominant_space = DimensionReduction(threshold_factor,
n_arnoldi_sample=3,
exact_Hessian=False)
dominant_space.getDominantDirections(QoI, jdist)
# Setup the problem
optProb = Optimization('Paraboloid', objfunc)
lower_bound = -20 * np.ones(rv_systemsize)
upper_bound = 20 * np.ones(rv_systemsize)
optProb.addVarGroup('xvars',
rv_systemsize,
'c',
lower=lower_bound,
upper=upper_bound,
value=10 * np.ones(rv_systemsize))
optProb.addObj('obj')
# Optimizer
opt = SNOPT(optOptions={'Major feasibility tolerance': 1e-6})
sol = opt(optProb, sens=sens)
# Check Solution
import inspect
print(sol.fStar)
print(sol.getDVs()['xvars'])
# 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
opt = SNOPT(optOptions={"Major feasibility tolerance": 1e-1})
sol = opt(optProb, sens=sens)
# Check Solution
print(sol)
print("Solution shoud be (x, y) = (7.166667, -7.833334)\n")
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 optimize(self,
sparse=True,
tol=None,
optOptions={},
storeHistory=False):
# set N
if sparse:
self.N = 50000
else:
self.N = 500
# Optimization Object
optProb = Optimization("large and sparse", self.objfunc)
# Design Variables
optProb.addVar("x", lower=-100, upper=150, value=0)
optProb.addVarGroup("y",
self.N,
lower=-10 - np.arange(self.N),
upper=np.arange(self.N),
value=0)
optProb.addVarGroup("z",
2 * self.N,
upper=np.arange(2 * self.N),
lower=-100 - np.arange(2 * self.N),
value=0)
# Constraints
optProb.addCon("con1", upper=100, wrt=["x"])
optProb.addCon("con2", upper=100)
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)
optProb.addConGroup(
"lincon",
self.N,
lower=2 - 3 * np.arange(self.N),
linear=True,
wrt=["x", "y"],
jac={
"x": xJac,
"y": yJac
},
)
optProb.addObj("obj")
# Optimizer
try:
opt = SNOPT(options=optOptions)
except Error:
raise unittest.SkipTest("Optimizer not available: SNOPT")
sol = opt(optProb, sens=self.sens)
# Check Solution
if tol is not None:
if opt.version != "7.7.7":
assert_allclose(sol.objectives["obj"].value,
10.0,
atol=tol,
rtol=tol)
else:
assert_allclose(sol.fStar, 10.0, atol=tol, rtol=tol)
assert_allclose(sol.variables["x"][0].value,
2.0,
atol=tol,
rtol=tol)
return sol
if optimize == True:
# optimization setup
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)
num_cons_obs = nobs * nwind
optProb.addConGroup('SPL', num_cons_obs, lower=0, upper=SPLlim / 10.)
opt = SNOPT()
opt.setOption('Scale option', 0)
if rotdir_spec == 'cn':
opt.setOption(
'Print file',
basepath + path.sep + 'optimization_results/SNOPT_print_SPL' +
str(SPLlim) + '_turb' + str(n) + '_counterrot.out')
opt.setOption(
'Summary file', basepath + path.sep +
'optimization_results/SNOPT_summary_SPL' + str(SPLlim) +
'_turb' + str(n) + '_counterrot.out')
elif rotdir_spec == 'co':
opt.setOption(
'Print file',
basepath + path.sep + 'optimization_results/SNOPT_print_SPL' +
str(SPLlim) + '_turb' + str(n) + '_corot.out')
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
"""Optimization"""
optProb = Optimization('Wind_Farm_AEP', obj_func_no_damage)
optProb.addObj('AEP')
# optProb.addObj('damage')
optProb.addVarGroup('turbineX', nTurbs, type='c', lower=min(xBounds), upper=max(xBounds), value=turbineX)
optProb.addVarGroup('turbineY', nTurbs, type='c', lower=min(yBounds), upper=max(yBounds), value=turbineY)
num_cons_sep = (nTurbs-1)*nTurbs/2
optProb.addConGroup('sep', num_cons_sep, lower=0., upper=None)
optProb.addConGroup('bound', nTurbs, lower=0., upper=None)
# optProb.addConGroup('damage', nTurbs, lower=None, upper=damage_lim)
# optProb.addCon('damage', lower=aep_lim, upper=None)
opt = SNOPT()
opt.setOption('Scale option',0)
opt.setOption('Iterations limit',1000000)
opt.setOption('Summary file','%s/summary.out'%folder)
opt.setOption('Major optimality tolerance',1.e-4)
opt.setOption('Major feasibility tolerance',1.e-6)
res = opt(optProb)
x1 = res.xStar['turbineX']
y1 = res.xStar['turbineY']
input = {'turbineX':x1,'turbineY':y1}
funcs,_ = obj_func_damage(input)
for i in range(2, 3):
print "----------------- Part: ", part[i], " -------------------"
for j in range(0, 6):
ub = 5.0*np.ones(len(x))
lb = -5.0*np.ones(len(x))
#Somthing is wrong with NSGA2 that returns segmentation fault (core dumped) when run multiple times
#...so I did it outside the loop..
#if part[i] == "a":
# optimizer = NSGA2()
# optimizer.setOption('maxGen', 500)
# optimizer.setOption('PopSize', len(x)*10)
# func, _ = mul_dim_rosen(x, part[i])
if part[i] == "b":
func, _ = mul_dim_rosen(x, part[i])
optimizer = SNOPT()
elif part[i] == "c":
optimizer = SNOPT()
func, _, _, _ = mul_dim_rosen(x, part[i])
xopt, fopt, info = optimize(mul_dim_rosen, x, lb, ub, optimizer, args = [part[i]])
print "n: ", len(x)
print "x_in: ", x
print "f_in: ", func
print "xopt: ", xopt
print "fopt: ", fopt
print "function calls: ", function_calls
print "info: ", info, "\n\n"
function_calls_array = np.append(function_calls_array, function_calls)
x = np.append(x, x)
function_calls = 0