def solveOpt(int_domain, J, a, model, u0, sign):
'''
INPUT:
int_domain =
J =
a =
model =
u0 =
sign =
OUTPUT:
opt_prob =
'''
def objfun(u, **kwargs):
'''objfun defines optimization problem using the pyOpt sintax'''
# 1) extract paraeters
int_domain = kwargs['int_domain']
J = kwargs['J']
x = kwargs['a']
model = kwargs['model']
sign = kwargs['sign']
# 2) define objective function and constraints
funz = np.trapz(x=int_domain, y=J * model.pf(int_domain, u, x))
g = []
fail = 0
return sign * funz, g, fail
opt_prob = Optimization('ODAA problem', objfun)
opt_prob.addObj('funz')
solver = SLSQP() # choose the solver
solver.setOption('IPRINT', -1)
opt_prob.addVar('u', 'c', lower=-1, upper=1, value=u0)
#print opt_prob # print optimization problem
solver(opt_prob, int_domain=int_domain, J=J, a=a, model=model, sign=sign)
#print opt_prob.solution(0) # print solution
return opt_prob
def solveOpt(int_domain,J,x,model,u0):
def objfun(u,**kwargs):
# 1) extract paraeters
int_domain = kwargs['int_domain']
J = kwargs['J']
x = kwargs['x']
model = kwargs['model']
# 2) define objective function
f = np.trapz(int_domain,J * model.pf(int_domain,u,x))
g = [0]*2
# 3) budget constraint
g[1] = u.sum() - 1
# 4) VaR constarint
W = model.W
sigmaMax = model.VaR / norm.ppf(1-model.alpha)
g[0] = -sigmaMax + np.sqrt(W.dot(u).dot(u))
fail = 0
return f,g,fail
opt_prob = Optimization('test problem',objfun)
opt_prob.addObj('f')
opt_prob.addCon('budget const','e')
opt_prob.addCon('VaR const','i')
opt_prob.addVarGroup('u',model.M,'c',lower=np.zeros(model.M),
upper=np.ones(model.M),value=u0)
print opt_prob
slsqp = SLSQP()
slsqp.setOption('IPRINT',-1)
slsqp(opt_prob,sens_type='FD',int_domain=int_domain,J=J,x=x,model=model)
print opt_prob.solution(0)
def solveOpt(int_domain, J, a, model, u0, sign):
'''
INPUT:
int_domain =
J =
a =
model =
u0 =
sign =
OUTPUT:
opt_prob =
'''
def objfun(u, **kwargs):
'''objfun defines the objective function and the constraints (equality
and inequality) of the optiization problem'''
# 1) extract paraeters
int_domain = kwargs['int_domain']
J = kwargs['J']
x = kwargs['a']
model = kwargs['model']
sign = kwargs['sign']
# 2) define objective function
funz = np.trapz(x=int_domain, y=J * model.pf(int_domain, u, x))
g = [0] * 2
# 3) budget constraint
g[0] = u.sum() - 1
# 4) VaR constarint
W = model.W
sigmaMax = model.VaR / norm.ppf(1 - model.alpha)
g[1] = -sigmaMax + np.sqrt(W.dot(u).dot(u))
fail = 0
return sign * funz, g, fail
opt_prob = Optimization('ODAA problem', objfun)
opt_prob.addObj('funz')
opt_prob.addCon('budget const', 'e')
opt_prob.addCon('VaR const', 'i')
slsqp = SLSQP() # instantiate Optimizer
slsqp.setOption('IPRINT', -1)
opt_prob.addVarGroup('u',
model.M,
'c',
lower=np.zeros(model.M),
upper=np.ones(model.M),
value=u0)
#print opt_prob # print optimization problem
slsqp(opt_prob,
sens_type='FD',
int_domain=int_domain,
J=J,
a=a,
model=model,
sign=sign)
#print opt_prob.solution(0) # print solution
return opt_prob
def traj_optim_static(paths, tree):
path, envs, modes, mnps = paths
guard_index = [0]
n = len(modes)
v_init = np.zeros((n, 3))
for i in range(1, n):
if not np.all(modes[i] == modes[i - 1]):
guard_index.append(i)
elif len(envs[i]) != 0:
if not envs[i][0].is_same(envs[i - 1][0]):
guard_index.append(i)
elif not (mnps[i][0].is_same(mnps[i - 1][0])
and mnps[i][1].is_same(mnps[i - 1][1])):
# manipulator change
guard_index.append(i)
g_v = np.identity(3)
g_v[0:2, 0:2] = config2trans(path[i - 1])[0:2, 0:2]
v_init[i - 1] = np.dot(g_v.T,
np.array(path[i]) - np.array(path[i - 1]))
#guard_index.append(len(modes)-1)
guard_index = np.unique(guard_index)
Gs = dict()
hs = dict()
As = dict()
bs = dict()
for i in range(len(path)):
G, h, A, b = contact_mode_constraints(path[i], mnps[i], envs[i],
modes[i], tree.world,
tree.mnp_mu, tree.env_mu,
tree.mnp_fn_max)
gid = np.any(G[:, 0:3], axis=1)
aid = np.any(A[:, 0:3], axis=1)
Gs[i] = G[gid, 0:3]
hs[i] = h[gid].flatten()
As[i] = A[aid, 0:3]
bs[i] = b[aid].flatten()
modeconstraints = (Gs, hs, As, bs)
q_goal = np.array(tree.x_goal)
opt_prob = Optimization('Trajectory Optimization', obj_fun)
x_init = np.hstack((np.array(path).flatten(), v_init.flatten()))
cs = constraints(x_init, path, Gs, hs, As, bs, guard_index)
opt_prob.addVarGroup('x', n * 6, 'c', value=x_init, lower=-10, upper=10)
opt_prob.addObj('f')
opt_prob.addConGroup('g', len(cs), 'i', lower=0.0, upper=10000.0)
print(opt_prob)
slsqp = SLSQP()
#slsqp.setOption('IPRINT', -1)
slsqp(opt_prob,
sens_type='FD',
goal=q_goal,
path=path,
modecons=modeconstraints,
guard_index=guard_index)
print(opt_prob.solution(0))
qs = [opt_prob.solution(0)._variables[i].value for i in range(n * 3)]
return qs
def optimize(k, w1, w2):
# Physical problem
rho = 0.2836 # lb/in^3
L = 5.0 # in
P = 25000.0 # lb
E = 30.0e6 # psi
ys = 36260.0 # psi
fs = 1.5
dtruss = TwoBarTruss(rho, L, P, E, ys, fs)
struss = StochasticTwoBarTruss(dtruss)
# Optimization Problem
optproblem = TwoBarTrussOpt(MPI.COMM_WORLD, struss, k, w1, w2)
opt_prob = Optimization(args.logfile, optproblem.evalObjCon)
# Add functions
opt_prob.addObj('weight')
opt_prob.addCon('buckling-bar1', type='i')
opt_prob.addCon('failure-bar1' , type='i')
opt_prob.addCon('failure-bar2' , type='i')
# Add variables
opt_prob.addVar('area-1', type='c', value= 1.5, lower= 1.5, upper= 1.5)
opt_prob.addVar('area-2', type='c', value= 1.5, lower= 1.5, upper= 1.5)
opt_prob.addVar('height', type='c', value= 4.0, lower= 4.0, upper= 10.0)
# Optimization algorithm
if args.algorithm == 'ALGENCAN':
opt = ALGENCAN()
opt.setOption('iprint',2)
opt.setOption('epsfeas',1e-6)
opt.setOption('epsopt',1e-6)
else:
opt = SLSQP(pll_type='POA')
opt.setOption('MAXIT',999)
opt(opt_prob,
sens_type=optproblem.evalObjConGradient,
disp_opts=True,
store_hst=True,
hot_start=False)
if optproblem.comm.Get_rank() ==0:
print opt_prob.solution(0)
opt_prob.write2file(disp_sols=True)
x = optproblem.x_hist[-1]
f = optproblem.fvals[0]
print 'x', x
print 'f', f
return x, f
def optimize(self, options=[]):
# Set SLSQP as the optimizer
self.opt = SLSQP()
# Set optimization options
if (len(options) > 0):
for ii in range(len(options)):
self.opt.setOption(options.keys()[ii], options.values()[ii])
# Print the Optimizer Options
print("----------------------------------------")
print("----------------------------------------")
print("SLSQP Optimizer options:")
print(self.opt.options)
# Get optimized controller
self.opt(self.opt_prob, sens_step=1e-6)
print(self.opt_prob.solution(0))
a = self.opt_prob.solution(0)
for ii in range(self.building.policy.shape[1]):
self.building.policy[0, ii] = a.getVar(ii).value
return self.building.policy
(x[1] - b[i])**2)))
#end
g = [0.0] * 1
g[0] = 20.04895 - (x[0] + 2.0)**2 - (x[1] + 1.0)**2
fail = 0
return f, g, fail
# =============================================================================
#
# =============================================================================
opt_prob = Optimization('Langermann Function 11', objfunc)
opt_prob.addVar('x1', 'c', lower=-2.0, upper=10.0, value=8.0)
opt_prob.addVar('x2', 'c', lower=-2.0, upper=10.0, value=8.0)
opt_prob.addObj('f')
opt_prob.addCon('g', 'i')
print(opt_prob)
# Global Optimization
nsga2 = NSGA2()
nsga2(opt_prob)
print(opt_prob.solution(0))
# Local Optimization Refinement
slsqp = SLSQP()
slsqp(opt_prob.solution(0))
print(opt_prob.solution(0).solution(0))
# Add functions
opt_prob.addObj('weight')
opt_prob.addCon('buckling-bar1', type='i')
opt_prob.addCon('failure-bar1', type='i')
opt_prob.addCon('failure-bar2', type='i')
# Add variables
opt_prob.addVar('area-1', type='c', value=1.0, lower=1.0e-3, upper=2.0)
opt_prob.addVar('area-2', type='c', value=1.0, lower=1.0e-3, upper=2.0)
opt_prob.addVar('height', type='c', value=4.0, lower=4.0, upper=10.0)
# Optimization algorithm
if args.algorithm == 'ALGENCAN':
opt = ALGENCAN()
opt.setOption('iprint', 2)
opt.setOption('epsfeas', 1e-6)
opt.setOption('epsopt', 1e-6)
else:
opt = SLSQP(pll_type='POA')
opt.setOption('MAXIT', 999)
opt(opt_prob,
sens_type=optproblem.evalObjConGradient,
disp_opts=True,
store_hst=True,
hot_start=False)
if optproblem.comm.Get_rank() == 0:
print opt_prob.solution(0)
opt_prob.write2file(disp_sols=True)
f = a1 * (x[1] - x[0]**2.)**2. + (a2 - x[0])**2.
g = [0.0] * 2
g[0] = x[0]**2. + x[1]**2.0 - a3
fail = 0
return f, g, fail
# =============================================================================
#
# =============================================================================
# Instantiate Optimization Problem
opt_prob = Optimization('Rosenbrock Constrained Problem', objfunc)
opt_prob.addVar('x1', 'c', lower=0.0, upper=1.0, value=0.5)
opt_prob.addVar('x2', 'c', lower=0.0, upper=1.0, value=0.5)
opt_prob.addObj('f')
opt_prob.addCon('g1', 'i')
print opt_prob
# Arguments to pass into objfunc
a1 = 100.0
a2 = 1.0
a3 = 1.0
# Instantiate Optimizer (SLSQP) & Solve Problem
slsqp = SLSQP()
slsqp.setOption('IPRINT', -1)
slsqp(opt_prob, sens_type='FD', a12=[a1, a2], a3=a3)
print opt_prob.solution(0)
g[1] = 2 - x[1]
fail = 0
return f,g,fail
# =============================================================================
#
# =============================================================================
# Instanciate Optimization Problem
opt_prob = Optimization('TOY Constraint Problem',objfunc)
opt_prob.addVar('x1','c',value=1.0,lower=0.0,upper=10.0)
opt_prob.addVar('x2','c',value=1.0,lower=0.0,upper=10.0)
opt_prob.addObj('f')
opt_prob.addCon('g1','i')
opt_prob.addCon('g2','i')
print opt_prob
# Instanciate Optimizer (ALPSO) & Solve Problem Storing History
slsqp = SLSQP()
slsqp.setOption('IFILE','slsqp1.out')
slsqp(opt_prob,store_hst=True)
print opt_prob.solution(0)
# Solve Problem Using Stored History (Warm Start)
slsqp.setOption('IFILE','slsqp2.out')
slsqp(opt_prob, store_hst=True, hot_start='slsqp1')
print opt_prob.solution(1)
g[1] = 2 - x[1]
fail = 0
return f,g,fail
# =============================================================================
#
# =============================================================================
# Instanciate Optimization Problem
opt_prob = Optimization('TOY Constrained Problem',objfunc)
opt_prob.addVar('x1','c',value=1.0,lower=0.0,upper=10.0)
opt_prob.addVar('x2','c',value=1.0,lower=0.0,upper=10.0)
opt_prob.addObj('f')
opt_prob.addCon('g1','i')
opt_prob.addCon('g2','i')
print(opt_prob)
# Instanciate Optimizer (SLSQP) & Solve Problem Storing History
slsqp = SLSQP()
slsqp.setOption('IFILE','slsqp1.out')
slsqp(opt_prob,store_hst=True)
print(opt_prob.solution(0))
# Solve Problem Using Stored History (Warm Start)
slsqp.setOption('IFILE','slsqp2.out')
slsqp(opt_prob, store_hst=True, hot_start='slsqp1')
print(opt_prob.solution(1))
def main():
###########################################
# Define some values
###########################################
n_blades = 2
n_elements = 10
radius = unit_conversion.in2m(9.6) / 2
root_cutout = 0.1 * radius
dy = float(radius - root_cutout) / n_elements
dr = float(1) / n_elements
y = root_cutout + dy * np.arange(1, n_elements + 1)
r = y / radius
pitch = 0.0
airfoils = (('SDA1075_494p', 0.0, 1.0), )
allowable_Re = [
1000000., 500000., 250000., 100000., 90000., 80000., 70000., 60000.,
50000., 40000., 30000., 20000., 10000.
]
vehicle_weight = 12.455
max_chord = 0.3
max_chord_tip = 5.
alt = 0
tip_loss = True
mach_corr = False
# Forward flight parameters
v_inf = 4. # m/s
alpha0 = 0.0454 # Starting guess for trimmed alpha in radians
n_azi_elements = 5
# Mission times
time_in_hover = 300. # Time in seconds
time_in_ff = 500.
mission_time = [time_in_hover, time_in_ff]
Cl_tables = {}
Cd_tables = {}
Clmax = {}
# Get lookup tables
if any(airfoil[0] != 'simple' for airfoil in airfoils):
for airfoil in airfoils:
Cl_table, Cd_table, Clmax = aero_coeffs.create_Cl_Cd_table(
airfoil[0])
Cl_tables[airfoil[0]] = Cl_table
Cd_tables[airfoil[0]] = Cd_table
Clmax[airfoil[0]] = Clmax
# Create list of Cl functions. One for each Reynolds number. Cl_tables (and Cd_tables) will be empty for the
# 'simple' case, therefore this will be skipped for the simple case. For the full table lookup case this will be
# skipped because allowable_Re will be empty.
Cl_funs = {}
Cd_funs = {}
lift_curve_info_dict = {}
if Cl_tables and allowable_Re:
Cl_funs = dict(
zip(allowable_Re, [
aero_coeffs.get_Cl_fun(Re, Cl_tables[airfoils[0][0]],
Clmax[airfoils[0][0]][Re])
for Re in allowable_Re
]))
Cd_funs = dict(
zip(allowable_Re, [
aero_coeffs.get_Cd_fun(Re, Cd_tables[airfoils[0][0]])
for Re in allowable_Re
]))
lift_curve_info_dict = aero_coeffs.create_liftCurveInfoDict(
allowable_Re, Cl_tables[airfoils[0][0]])
###########################################
# Set design variable bounds
###########################################
# Hover opt 500 gen, 1000 pop, 12.455 N weight, 9.6 in prop
chord = np.array([
0.11923604, 0.2168746, 0.31540216, 0.39822882, 0.42919, 0.35039799,
0.3457828, 0.28567224, 0.23418368, 0.13502483
])
twist = np.array([
0.45316866, 0.38457724, 0.38225075, 0.34671967, 0.33151445, 0.28719111,
0.25679667, 0.25099005, 0.19400679, 0.10926302
])
omega = 3811.03596674 * 2 * np.pi / 60
original = (omega, chord, twist)
dtwist = np.array(
[twist[i + 1] - twist[i] for i in xrange(len(twist) - 1)])
dchord = np.array(
[chord[i + 1] - chord[i] for i in xrange(len(chord) - 1)])
twist0 = twist[0]
chord0 = chord[0]
omega_start = omega
dtwist_start = dtwist
dchord_start = dchord
twist0_start = twist0
chord0_start = chord0
omega_lower = 2000 * 2 * np.pi / 60
omega_upper = 8000.0 * 2 * np.pi / 60
twist0_lower = 0. * 2 * np.pi / 360
twist0_upper = 60. * 2 * np.pi / 360
chord0_upper = 0.1198
chord0_lower = 0.05
dtwist_lower = -10.0 * 2 * np.pi / 360
dtwist_upper = 10.0 * 2 * np.pi / 360
dchord_lower = -0.1
dchord_upper = 0.1
opt_prob = Optimization('Mission Simulator', objfun)
opt_prob.addVar('omega_h',
'c',
value=omega_start,
lower=omega_lower,
upper=omega_upper)
opt_prob.addVar('twist0',
'c',
value=twist0_start,
lower=twist0_lower,
upper=twist0_upper)
opt_prob.addVar('chord0',
'c',
value=chord0_start,
lower=chord0_lower,
upper=chord0_upper)
opt_prob.addVarGroup('dtwist',
n_elements - 1,
'c',
value=dtwist_start,
lower=dtwist_lower,
upper=dtwist_upper)
opt_prob.addVarGroup('dchord',
n_elements - 1,
'c',
value=dchord_start,
lower=dchord_lower,
upper=dchord_upper)
opt_prob.addObj('f')
opt_prob.addCon('thrust', 'i')
opt_prob.addCon('c_tip', 'i')
opt_prob.addConGroup('c_lower', n_elements, 'i')
opt_prob.addConGroup('c_upper', n_elements, 'i')
print opt_prob
slsqp = SLSQP()
slsqp.setOption('IPRINT', 1)
slsqp.setOption('MAXIT', 1000)
slsqp.setOption('ACC', 1e-8)
fstr, xstr, inform = slsqp(opt_prob,
sens_type='FD',
n_blades=n_blades,
radius=radius,
dy=dy,
dr=dr,
y=y,
r=r,
pitch=pitch,
airfoils=airfoils,
vehicle_weight=vehicle_weight,
max_chord=max_chord,
tip_loss=tip_loss,
mach_corr=mach_corr,
Cl_funs=Cl_funs,
Cd_funs=Cd_funs,
Cl_tables=Cl_tables,
Cd_tables=Cd_tables,
allowable_Re=allowable_Re,
alt=alt,
v_inf=v_inf,
alpha0=alpha0,
mission_time=mission_time,
n_azi_elements=n_azi_elements,
lift_curve_info_dict=lift_curve_info_dict,
max_chord_tip=max_chord_tip)
print opt_prob.solution(0)
# pop_size = 300
# max_gen = 500
# opt_method = 'nograd'
# nsga2 = NSGA2()
# nsga2.setOption('PrintOut', 2)
# nsga2.setOption('PopSize', pop_size)
# nsga2.setOption('maxGen', max_gen)
# nsga2.setOption('pCross_real', 0.85)
# nsga2.setOption('xinit', 1)
# fstr, xstr, inform = nsga2(opt_prob, n_blades=n_blades, radius=radius, dy=dy, dr=dr, y=y, r=r, pitch=pitch,
# airfoils=airfoils, vehicle_weight=vehicle_weight, max_chord=max_chord, tip_loss=tip_loss,
# mach_corr=mach_corr, Cl_funs=Cl_funs, Cd_funs=Cd_funs, Cl_tables=Cl_tables,
# Cd_tables=Cd_tables, allowable_Re=allowable_Re, opt_method=opt_method, alt=alt,
# v_inf=v_inf, alpha0=alpha0, mission_time=mission_time, n_azi_elements=n_azi_elements,
# pop_size=pop_size, max_gen=max_gen, lift_curve_info_dict=lift_curve_info_dict,
# max_chord_tip=max_chord_tip)
# print opt_prob.solution(0)
# opt_method = 'nograd'
# xstart_alpso = np.concatenate((np.array([omega_start, twist0_start, chord0_start]), dtwist_start, dchord_start))
# alpso = ALPSO()
# alpso.setOption('xinit', 0)
# alpso.setOption('SwarmSize', 200)
# alpso.setOption('maxOuterIter', 100)
# alpso.setOption('stopCriteria', 0)
# fstr, xstr, inform = alpso(opt_prob, xstart=xstart_alpso, n_blades=n_blades, n_elements=n_elements,
# root_cutout=root_cutout, radius=radius, dy=dy, dr=dr, y=y, r=r, pitch=pitch,
# airfoils=airfoils, thrust=thrust, max_chord=max_chord, tip_loss=tip_loss,
# mach_corr=mach_corr, Cl_funs=Cl_funs, Cd_funs=Cd_funs, Cl_tables=Cl_tables,
# Cd_tables=Cd_tables, allowable_Re=allowable_Re, opt_method=opt_method)
# print opt_prob.solution(0)
def get_performance(o, c, t):
chord_meters = c * radius
prop = propeller.Propeller(t,
chord_meters,
radius,
n_blades,
r,
y,
dr,
dy,
airfoils=airfoils,
Cl_tables=Cl_tables,
Cd_tables=Cd_tables)
quad = quadrotor.Quadrotor(prop, vehicle_weight)
ff_kwargs = {
'propeller': prop,
'pitch': pitch,
'n_azi_elements': n_azi_elements,
'allowable_Re': allowable_Re,
'Cl_funs': Cl_funs,
'Cd_funs': Cd_funs,
'tip_loss': tip_loss,
'mach_corr': mach_corr,
'alt': alt,
'lift_curve_info_dict': lift_curve_info_dict
}
trim0 = np.array([alpha0, o])
alpha_trim, omega_trim, converged = trim.trim(quad, v_inf, trim0,
ff_kwargs)
T_ff, H_ff, P_ff = bemt.bemt_forward_flight(
quad,
pitch,
omega_trim,
alpha_trim,
v_inf,
n_azi_elements,
alt=alt,
tip_loss=tip_loss,
mach_corr=mach_corr,
allowable_Re=allowable_Re,
Cl_funs=Cl_funs,
Cd_funs=Cd_funs,
lift_curve_info_dict=lift_curve_info_dict)
dT_h, P_h = bemt.bemt_axial(prop,
pitch,
o,
allowable_Re=allowable_Re,
Cl_funs=Cl_funs,
Cd_funs=Cd_funs,
tip_loss=tip_loss,
mach_corr=mach_corr,
alt=alt)
return sum(dT_h), P_h, T_ff, P_ff, alpha_trim, omega_trim
omega = xstr[0]
twist0 = xstr[1]
chord0 = xstr[2]
dtwist = xstr[3:3 + len(r) - 1]
dchord = xstr[3 + len(r) - 1:]
twist = calc_twist_dist(twist0, dtwist)
chord = calc_chord_dist(chord0, dchord)
print "chord = " + repr(chord)
print "twist = " + repr(twist)
# twist_base = calc_twist_dist(twist0_base, dtwist_base)
# chord_base = calc_chord_dist(chord0_base, dchord_base)
perf_opt = get_performance(omega, chord, twist)
perf_orig = get_performance(original[0], original[1], original[2])
print "omega_orig = " + str(original[0])
print "Hover Thrust of original = " + str(perf_orig[0])
print "Hover Power of original = " + str(perf_orig[1])
print "FF Thrust of original = " + str(perf_orig[2])
print "FF Power of original = " + str(perf_orig[3])
print "Trim original (alpha, omega) = (%f, %f)" % (perf_orig[4],
perf_orig[5])
print "omega = " + str(omega * 60 / 2 / np.pi)
print "Hover Thrust of optimized = " + str(perf_opt[0])
print "Hover Power of optimized = " + str(perf_opt[1])
print "FF Thrust of optimized = " + str(perf_opt[2])
print "FF Power of optimized = " + str(perf_opt[3])
print "Trim optimized (alpha, omega) = (%f, %f)" % (perf_opt[4],
perf_opt[5])
# print "Omega base = " + str(omega_start*60/2/np.pi)
# print "Thrust of base = " + str(sum(perf_base[0]))
# print "Power of base = " + str(sum(perf_base[1]))
#
plt.figure(1)
plt.plot(r, original[1], '-b')
plt.plot(r, chord, '-r')
plt.xlabel('radial location')
plt.ylabel('c/R')
plt.legend(['start', 'opt'])
plt.figure(2)
plt.plot(r, original[2] * 180 / np.pi, '-b')
plt.plot(r, twist * 180 / np.pi, '-r')
plt.xlabel('radial location')
plt.ylabel('twist')
plt.legend(['start', 'opt'])
plt.show()
fail = 0
return f,g, fail
# =============================================================================
#
# =============================================================================
# Instantiate Optimization Problem
opt_prob = Optimization('TP37 Constraint Problem',objfunc)
opt_prob.addVar('x1','c',lower=0.0,upper=42.0,value=10.0)
opt_prob.addVar('x2','c',lower=0.0,upper=42.0,value=10.0)
opt_prob.addVar('x3','c',lower=0.0,upper=42.0,value=10.0)
opt_prob.addObj('f')
opt_prob.addCon('g1','i')
opt_prob.addCon('g2','i')
# Instantiate Optimizer (SLSQP)
slsqp = SLSQP()
slsqp.setOption('IPRINT',-1)
# Solve Problem (Without Parallel Gradient)
slsqp(opt_prob,sens_type='CS')
if myrank == 0:
print opt_prob.solution(0)
#end
# Solve Problem (With Parallel Gradient)
slsqp(opt_prob,sens_type='CS',sens_mode='pgc')
print opt_prob.solution(1)
def optimizeTrajectory(self, plot_func=None):
# use non-linear optimization to find parameters for minimal
# condition number trajectory
self.plot_func = plot_func
if self.config['showOptimizationGraph']:
self.initGraph()
## describe optimization problem with pyOpt classes
from pyOpt import Optimization
from pyOpt import ALPSO, SLSQP
# Instanciate Optimization Problem
opt_prob = Optimization('Trajectory optimization', self.objective_func)
opt_prob.addObj('f')
# add variables, define bounds
# w_f - pulsation
opt_prob.addVar('wf', 'c', value=self.wf_init, lower=self.wf_min, upper=self.wf_max)
# q - offsets
for i in range(self.dofs):
opt_prob.addVar('q_%d'%i,'c', value=self.qinit[i], lower=self.qmin[i], upper=self.qmax[i])
# a, b - sin/cos params
for i in range(self.dofs):
for j in range(self.nf[0]):
opt_prob.addVar('a{}_{}'.format(i,j), 'c', value=self.ainit[i][j], lower=self.amin, upper=self.amax)
for i in range(self.dofs):
for j in range(self.nf[0]):
opt_prob.addVar('b{}_{}'.format(i,j), 'c', value=self.binit[i][j], lower=self.bmin, upper=self.bmax)
# add constraint vars (constraint functions are in obfunc)
if self.config['minVelocityConstraint']:
opt_prob.addConGroup('g', self.dofs*5, 'i')
else:
opt_prob.addConGroup('g', self.dofs*4, 'i')
#print opt_prob
initial = [v.value for v in list(opt_prob._variables.values())]
if self.config['useGlobalOptimization']:
### optimize using pyOpt (global)
opt = ALPSO() #augmented lagrange particle swarm optimization
opt.setOption('stopCriteria', 0)
opt.setOption('maxInnerIter', 3)
opt.setOption('maxOuterIter', self.config['globalOptIterations'])
opt.setOption('printInnerIters', 1)
opt.setOption('printOuterIters', 1)
opt.setOption('SwarmSize', 30)
opt.setOption('xinit', 1)
#TODO: how to properly limit max number of function calls?
# no. func calls = (SwarmSize * inner) * outer + SwarmSize
self.iter_max = opt.getOption('SwarmSize') * opt.getOption('maxInnerIter') * opt.getOption('maxOuterIter') + opt.getOption('SwarmSize')
# run fist (global) optimization
try:
#reuse history
opt(opt_prob, store_hst=False, hot_start=True, xstart=initial)
except NameError:
opt(opt_prob, store_hst=False, xstart=initial)
print(opt_prob.solution(0))
### pyOpt local
# after using global optimization, get more exact solution with
# gradient based method init optimizer (only local)
opt2 = SLSQP() #sequential least squares
opt2.setOption('MAXIT', self.config['localOptIterations'])
if self.config['verbose']:
opt2.setOption('IPRINT', 0)
# TODO: amount of function calls depends on amount of variables and iterations to approximate gradient
# iterations are probably steps along the gradient. How to get proper no. of func calls?
self.iter_max = "(unknown)"
if self.config['useGlobalOptimization']:
if self.last_best_sol is not None:
#use best constrained solution
for i in range(len(opt_prob._variables)):
opt_prob._variables[i].value = self.last_best_sol[i]
else:
#reuse previous solution
for i in range(len(opt_prob._variables)):
opt_prob._variables[i].value = opt_prob.solution(0).getVar(i).value
opt2(opt_prob, store_hst=False, sens_step=0.1)
else:
try:
#reuse history
opt2(opt_prob, store_hst=True, hot_start=True, sens_step=0.1)
except NameError:
opt2(opt_prob, store_hst=True, sens_step=0.1)
local_sol = opt_prob.solution(0)
if not self.config['useGlobalOptimization']:
print(local_sol)
local_sol_vec = np.array([local_sol.getVar(x).value for x in range(0,len(local_sol._variables))])
if self.last_best_sol is not None:
local_sol_vec = self.last_best_sol
print("using last best constrained solution instead of given solver solution.")
sol_wf, sol_q, sol_a, sol_b = self.vecToParams(local_sol_vec)
print("testing final solution")
self.iter_cnt = 0
self.objective_func(local_sol_vec)
print("\n")
self.trajectory.initWithParams(sol_a, sol_b, sol_q, self.nf, sol_wf)
if self.config['showOptimizationGraph']:
plt.ioff()
return self.trajectory
design_problem = PyOptOptimization(dp.comm,
dp.eval_objcon,
dp.eval_objcon_grad,
number_of_steps=3)
opt_prob = Optimization('crm_togw', design_problem.eval_obj_con)
opt_prob.addObj('TOGW')
opt_prob.addCon('cruise_lift', type='e')
opt_prob.addCon('maneuver_lift', type='e')
opt_prob.addCon('area', type='e')
opt_prob.addCon('ksfailure', type='i')
for i in range(187 - 3):
opt_prob.addCon('Smoothness %i a' % i, type='i')
opt_prob.addCon('Smoothness %i b' % i, type='i')
variables = dp.model.get_variables()
for i, var in enumerate(variables):
print('i', i)
opt_prob.addVar(var.name,
type='c',
value=var.value / dp.var_scale[i],
lower=var.lower / dp.var_scale[i],
upper=var.upper / dp.var_scale[i])
opt = SLSQP(pll_type='POA')
opt.setOption('MAXIT', 999)
opt(opt_prob, sens_type=design_problem.eval_obj_con_grad, disp_opts=True)
def optimize(self, options):
# Set max number of optimization iterations
maxIter = 1
if (len(options) > 0):
maxIter = options["MAXIT"]
# Log costs and constraints for all internal iterations
self.costs = np.zeros((maxIter + 1, 1))
self.constraints = np.zeros((maxIter + 1, self.ncons))
self.policies = np.zeros((maxIter + 1, self.nvars))
# GP_SS process
initPolicy = self.building.policy.copy()
for ii in range(maxIter):
# Train GP state-space models
kernel = GPy.kern.Matern52(self.X.shape[1], ARD=False)
for jj in range(0, len(self.states)):
if (self.X.shape[0] > self.num_inducing):
print("Using Sparse GP Model...")
dynModel = GPy.models.SparseGPRegression(
self.X,
self.Y[:, jj].reshape(self.Y.shape[0], 1),
kernel,
num_inducing=self.num_inducing)
self.dynModels.append(dynModel.copy())
else:
print("Using Full GP Model...")
dynModel = GPy.models.GPRegression(
self.X, self.Y[:, jj].reshape(self.Y.shape[0], 1),
kernel)
dynModel.optimize_restarts(num_restarts=2)
dynModel.optimize('bfgs', messages=True, max_iters=5000)
self.dynModels.append(dynModel.copy())
print(self.dynModels[jj])
self.checkModelAccuracy(dynModel, self.X, self.Y[:, jj])
# Define Box Constraints (min/max values) for the control parameters
boxConstraints = []
for jj in range(self.nvars):
boxConstraints.append(self.controlLim)
# Link to the python function calculating the cost and the constraints. Note that
# this is not the actual simulation, but the propagate function
self.opt_prob = Optimization('GPSS_SLSQP Constrained Problem',
self.propagate)
# Setupt Box Constrains in pyOpt
for jj in range(self.nvars):
self.opt_prob.addVar('x' + str(jj + 1),
'c',
lower=boxConstraints[jj][0],
upper=boxConstraints[jj][1],
value=self.building.policy[0, jj])
# Setupt Cost Function in pyOpt
self.opt_prob.addObj('f')
# Setupt Inequality Constraints in pyOpt
for jj in range(self.ncons + 1):
self.opt_prob.addCon('g' + str(jj + 1), 'i')
# Print the Optimization setup
print("----------------------------------------")
print("----------------------------------------")
print("GPSS_SLSQP Optimization setup:")
print(self.opt_prob)
optionsSLSQP = {'ACC': 1.0e-20, 'MAXIT': 10000, 'IPRINT': 1}
# Set SLSQP as the optimizer
self.opt = SLSQP()
# Set optimization options
for jj in range(len(optionsSLSQP)):
self.opt.setOption(optionsSLSQP.keys()[jj],
optionsSLSQP.values()[jj])
# Print the Optimizer Options
print("----------------------------------------")
print("----------------------------------------")
print("SLSQP Optimizer options:")
print(self.opt.options)
# Get optimized controller
self.opt(self.opt_prob, sens_step=1e-6)
print(self.opt_prob.solution(0))
a = self.opt_prob.solution(0)
for jj in range(self.building.policy.shape[1]):
self.building.policy[0, jj] = a.getVar(jj).value
# Evaluate the optimized controller in the simulation model
xa, cf, cc = self.building.simulate(self.building.policy)
print("COST: = =========== " + str(np.sum(cf)))
print("CONSTRAINT: = =========== " + str(np.sum(cc)))
xx = xa[0:-1, ]
yy = xa[1:, self.states]
if (ii == 0):
self.X = xx
self.Y = yy
else:
self.X = np.concatenate((self.X, xx), axis=0)
self.Y = np.concatenate((self.Y, yy), axis=0)
self.costs[ii, 0] = np.sum(cf)
for jj in range(self.ncons):
self.constraints[ii, jj] = np.sum(cc[:, jj])
self.policies[ii, :] = self.building.policy.copy()
self.building.policy = initPolicy.copy()
self.policies[ii + 1, :] = self.baseline[0].copy()
self.costs[ii + 1, 0] = self.baseline[1]
self.constraints[ii + 1, 0] = self.baseline[2]
policyIndex = self.selectBestController(self.costs, self.constraints)
self.building.policy = self.policies[policyIndex, :].copy()
return self.building.policy
class GP_SS(Algorithm):
""" Implementation of Building Identification and Control using Gaussian Process
State-Space models. A GP regression model per state is created and then the
constrained optimization problem is solved using PyOpt's SLSQP solver."""
def __init__(self):
"""Initialize the GP_SS algorithm for a specific building.
"""
print("Initializing the GP_SS...")
self.building = Building()
self.X = []
self.Y = []
self.dynModels = []
# Number of Initial Exploration Simulation
self.initExploration = 36
# Number of Samples for witching to Sparse Gaussian Processes
self.num_inducing = 2000
# Safety constraint for exploration
self.explorationConstraint = 0.03
# Needed to determine the best controller out of all iterations
self.costs = []
self.constraints = []
self.policies = []
self.baseline = []
def configure(self, building):
# Get building and optimization setup properties
self.building = deepcopy(building)
self.T, self.states, self.actions, self.disturbances, self.controlLim, self.actionLim, self.comfort, self.occ, self.nvars, self.ncons = self.building.getConfiguration(
)
# Run initial random simulations and contstruct initial dataset
for ii in range(0, self.initExploration):
self.building.rand = 1
xa, cf, cc = self.building.simulate(self.building.policy)
xx = xa[0:-1, ]
yy = xa[1:, self.states]
if (ii == 0):
self.X = xx
self.Y = yy
else:
self.X = np.concatenate((self.X, xx), axis=0)
self.Y = np.concatenate((self.Y, yy), axis=0)
# Last simulation is the baseline controller
self.building.rand = 0
xa, cf, cc = self.building.simulate(self.building.policy)
self.baseline.append(self.building.policy)
self.baseline.append(np.sum(cf))
self.baseline.append(np.sum(cc))
xx = xa[0:-1, ]
yy = xa[1:, self.states]
self.X = np.concatenate((self.X, xx), axis=0)
self.Y = np.concatenate((self.Y, yy), axis=0)
def optimize(self, options):
# Set max number of optimization iterations
maxIter = 1
if (len(options) > 0):
maxIter = options["MAXIT"]
# Log costs and constraints for all internal iterations
self.costs = np.zeros((maxIter + 1, 1))
self.constraints = np.zeros((maxIter + 1, self.ncons))
self.policies = np.zeros((maxIter + 1, self.nvars))
# GP_SS process
initPolicy = self.building.policy.copy()
for ii in range(maxIter):
# Train GP state-space models
kernel = GPy.kern.Matern52(self.X.shape[1], ARD=False)
for jj in range(0, len(self.states)):
if (self.X.shape[0] > self.num_inducing):
print("Using Sparse GP Model...")
dynModel = GPy.models.SparseGPRegression(
self.X,
self.Y[:, jj].reshape(self.Y.shape[0], 1),
kernel,
num_inducing=self.num_inducing)
self.dynModels.append(dynModel.copy())
else:
print("Using Full GP Model...")
dynModel = GPy.models.GPRegression(
self.X, self.Y[:, jj].reshape(self.Y.shape[0], 1),
kernel)
dynModel.optimize_restarts(num_restarts=2)
dynModel.optimize('bfgs', messages=True, max_iters=5000)
self.dynModels.append(dynModel.copy())
print(self.dynModels[jj])
self.checkModelAccuracy(dynModel, self.X, self.Y[:, jj])
# Define Box Constraints (min/max values) for the control parameters
boxConstraints = []
for jj in range(self.nvars):
boxConstraints.append(self.controlLim)
# Link to the python function calculating the cost and the constraints. Note that
# this is not the actual simulation, but the propagate function
self.opt_prob = Optimization('GPSS_SLSQP Constrained Problem',
self.propagate)
# Setupt Box Constrains in pyOpt
for jj in range(self.nvars):
self.opt_prob.addVar('x' + str(jj + 1),
'c',
lower=boxConstraints[jj][0],
upper=boxConstraints[jj][1],
value=self.building.policy[0, jj])
# Setupt Cost Function in pyOpt
self.opt_prob.addObj('f')
# Setupt Inequality Constraints in pyOpt
for jj in range(self.ncons + 1):
self.opt_prob.addCon('g' + str(jj + 1), 'i')
# Print the Optimization setup
print("----------------------------------------")
print("----------------------------------------")
print("GPSS_SLSQP Optimization setup:")
print(self.opt_prob)
optionsSLSQP = {'ACC': 1.0e-20, 'MAXIT': 10000, 'IPRINT': 1}
# Set SLSQP as the optimizer
self.opt = SLSQP()
# Set optimization options
for jj in range(len(optionsSLSQP)):
self.opt.setOption(optionsSLSQP.keys()[jj],
optionsSLSQP.values()[jj])
# Print the Optimizer Options
print("----------------------------------------")
print("----------------------------------------")
print("SLSQP Optimizer options:")
print(self.opt.options)
# Get optimized controller
self.opt(self.opt_prob, sens_step=1e-6)
print(self.opt_prob.solution(0))
a = self.opt_prob.solution(0)
for jj in range(self.building.policy.shape[1]):
self.building.policy[0, jj] = a.getVar(jj).value
# Evaluate the optimized controller in the simulation model
xa, cf, cc = self.building.simulate(self.building.policy)
print("COST: = =========== " + str(np.sum(cf)))
print("CONSTRAINT: = =========== " + str(np.sum(cc)))
xx = xa[0:-1, ]
yy = xa[1:, self.states]
if (ii == 0):
self.X = xx
self.Y = yy
else:
self.X = np.concatenate((self.X, xx), axis=0)
self.Y = np.concatenate((self.Y, yy), axis=0)
self.costs[ii, 0] = np.sum(cf)
for jj in range(self.ncons):
self.constraints[ii, jj] = np.sum(cc[:, jj])
self.policies[ii, :] = self.building.policy.copy()
self.building.policy = initPolicy.copy()
self.policies[ii + 1, :] = self.baseline[0].copy()
self.costs[ii + 1, 0] = self.baseline[1]
self.constraints[ii + 1, 0] = self.baseline[2]
policyIndex = self.selectBestController(self.costs, self.constraints)
self.building.policy = self.policies[policyIndex, :].copy()
return self.building.policy
def selectBestController(self, costs, constraints):
"""A function that selects the best controller out of a set of controllers,
based on their performance on the cost function and the constraints.
Args:
costs (numpy array): The resulting cost of each controller, as evaluated
on the simulation model
constraints (numpy array): The resulting constraints of each controller, as evaluated
on the simulation model
Returns:
policyIndex (float): the index of the best controller
"""
wCost = 1
wConstraints = 10000000
p = np.zeros((costs.shape[0], 1))
for ii in range(costs.shape[0]):
p[ii, 0] = p[ii, 0] + wCost * costs[ii, 0]
for jj in range(constraints.shape[1]):
p[ii, 0] = p[ii, 0] + wConstraints * constraints[ii, jj]
policyIndex = np.argmin(p)
return policyIndex
def propagate(self, policy):
"""A function that uses the GP state-space models identified from the data
to perform rollouts based on a given controller.
Args:
policy (numpy array): the controller to be used for the rollout
Returns:
f (float): the cost function value
g (list): the vales of all constraints
fail (0/1): indicates if the function finished successfully
"""
# Initial state
xx = np.zeros((self.building.T + 1,
len(self.building.states) + self.building.w.shape[1]))
uu = np.zeros((self.building.T + 1, len(self.building.actions)))
cc = np.zeros((self.building.T + 1, 1))
cf = np.zeros((self.building.T + 1, 1))
var = np.zeros((self.building.T, len(self.building.states)))
xx[0, ] = self.X[0, 0:2] # [17, w[0,]]
uu[0, ] = self.building.controller(policy, xx[0, ], 0)
cc[0, ] = self.building.comfortConstraints(xx[0, self.building.states],
0)
cf[0, ] = self.building.costFunction(uu[0, ], 0)
# state propagation using the provided controller
for ii in range(1, self.building.T + 1):
newX = np.hstack([xx[ii - 1, ],
uu[ii - 1]]).reshape(1,
xx.shape[1] + uu.shape[1])
for jj in range(0, len(self.building.states)):
results = self.dynModels[jj].predict(newX)
xx[ii, jj] = results[0]
var[ii - 1, jj] = results[1]
xx[ii, 1] = self.building.w[ii, ]
uu[ii, ] = self.building.controller(policy, xx[ii, ], ii)
cc[ii, ] = self.building.comfortConstraints(
xx[ii, self.building.states], ii)
cf[ii, ] = self.building.costFunction(uu[ii, ], ii)
f = np.sum(cf)
g = []
g.append(np.mean(var) -
self.explorationConstraint) # Exploration constraint
g.append(np.sum(cc))
fail = 0
return f, g, fail
def checkModelAccuracy(self, dynModel, xtest, ytest):
"""A function that evaluates the accuracy of the GP state-space model.
Args:
dynModel (GPy object): The GP model
xtest (numpy array): The features of the regression
ytest (numpy array): The targets of the regression
"""
results = dynModel.predict(xtest)
ypred = results[0]
# sGP = results[1]
rsqTrain, maeTrain, rsqAdjTrain = self.evaluateGoodnessOfFit(
xtest, ytest, ypred)
print("Rsq train Gaussian Processes Regression = " + str(rsqTrain))
print("Rsq Adjusted train Gaussian Processes Regression = " +
str(rsqAdjTrain))
print("MAE train Gaussian Processes Regression = " + str(maeTrain))
def evaluateGoodnessOfFit(self, x, y, ypred):
"""A function that evaluates the goodness of fit, under different measures.
Args:
x (numpy array): The features of the regression
y (numpy array): The targets of the regression
ypred (numpy array): The predictions of the regression model
Returns:
Rsquared (float): R-squared
mae (float): Mean Absolute Error
rsqAdj (float): The Adjusted R-square
"""
print(y.shape[0])
print(x.shape[1])
y_hat = np.mean(y)
SStot = np.sum(np.power(y - y_hat, 2))
SSres = np.sum(np.power(y - ypred.flatten(), 2))
if (SStot == 0):
rsq = 1
else:
rsq = 1 - SSres / SStot
mae = np.sum(np.abs(y - ypred.flatten())) / ypred.shape[0]
rsqAdj = 1 - (1 - rsq) * (y.shape[0] - 1) / (y.shape[0] - x.shape[1] -
1)
return rsq, mae, rsqAdj
def wrapSimulation(self, policy):
"""A function that runs a building simulation and wraps the results in the
format required by PyOpt library.
Args:
policy (numpy array): the controller to be used for the simulation
Returns:
f (float): the cost function value
g (list): the vales of all constraints
fail (0/1): indicates if the function finished successfully
"""
# Cost and Constraints
f = 0
g = []
fail = 0
# Run building simulation
x, cost, constraints = self.building.simulate(policy)
f = np.sum(cost)
g.append(np.sum(constraints))
# print(f)
# print(g[0])
return f, g, fail
opt_prob = Optimization('Aerothermoelasticity', design_problem.eval_obj_con)
opt_prob.addObj('Average Temperature')
opt_prob.addVar('x1', type='c', value=t0, lower=0.1, upper=1.0)
opt_prob.addVar('x2', type='c', value=t0, lower=0.1, upper=1.0)
opt_prob.addVar('x3', type='c', value=t0, lower=0.1, upper=1.0)
opt_prob.addCon('maximum mass', type='i', lower=-np.inf, upper=np.inf, equal=0.0)
if 'verify' in sys.argv:
x0 = np.array([t], TransferScheme.dtype)
f0, c0, fail = dp.eval_obj(x0)
g, A, fail = dp.eval_obj_grad(x0, f0, c0)
for dh in [1e-6, 5e-7, 1e-7, 5e-8, 1e-8]:
x = x0 + dh
f1, c1, fail = dp.eval_obj(x)
fd = (c1[0] - c0[0])/dh
rel_err = (fd - A[0,0])/fd
print('Finite-difference interval: %25.15e'%(dh))
print('Finite-difference value: %25.15e'%(fd))
print('Gradient value: %25.15e'%(A[0,0]))
print('Relative error: %25.15e'%(rel_err))
else:
opt = SLSQP(pll_type='POA')
opt.setOption('ACC',1e-10) # Set optimization tolerance to 1e-5 (default 1e-6)
opt.setOption('MAXIT', 999)
opt(opt_prob, sens_type=design_problem.eval_obj_con_grad, disp_opts=True)
print('FINISHED')
class SLSQP_pyOpt(Algorithm):
""" Utilization of the SLSQP algorithm of pyOpt package."""
def __init__(self):
"""Initialize the SLSQP algorithm for a specific building.
"""
print("Initializing the SLSQP Optimizer...")
self.opt_prob = []
self.opt = []
self.building = Building()
def configure(self, building):
# Get building and optimization setup properties
self.building = deepcopy(building)
self.T, self.states, self.actions, self.disturbances, self.controlLim, self.actionLim, self.comfort, self.occ, self.nvars, self.ncons = self.building.getConfiguration(
)
# Define Box Constraints (min/max values) for the control parameters
boxConstraints = []
for ii in range(self.nvars):
boxConstraints.append(self.controlLim)
# Link to the python function calculating the cost and the constraints
self.opt_prob = Optimization('SLSQP Constrained Problem',
self.wrapSimulation)
# Setupt Box Constrains in pyOpt
for ii in range(self.nvars):
self.opt_prob.addVar('x' + str(ii + 1),
'c',
lower=boxConstraints[ii][0],
upper=boxConstraints[ii][1],
value=self.building.policy[0, ii])
# Setupt Cost Function in pyOpt
self.opt_prob.addObj('f')
# Setupt Inequality Constraints in pyOpt
for ii in range(self.ncons):
self.opt_prob.addCon('g' + str(ii + 1), 'i')
# Print the Optimization setup
print("----------------------------------------")
print("----------------------------------------")
print("SLSQP Optimization setup:")
print(self.opt_prob)
def optimize(self, options=[]):
# Set SLSQP as the optimizer
self.opt = SLSQP()
# Set optimization options
if (len(options) > 0):
for ii in range(len(options)):
self.opt.setOption(options.keys()[ii], options.values()[ii])
# Print the Optimizer Options
print("----------------------------------------")
print("----------------------------------------")
print("SLSQP Optimizer options:")
print(self.opt.options)
# Get optimized controller
self.opt(self.opt_prob, sens_step=1e-6)
print(self.opt_prob.solution(0))
a = self.opt_prob.solution(0)
for ii in range(self.building.policy.shape[1]):
self.building.policy[0, ii] = a.getVar(ii).value
return self.building.policy
def wrapSimulation(self, policy):
"""A function that runs a building simulation and wraps the results in the
format required by PyOpt library.
Args:
policy (numpy array): the controller to be used for the simulation
Returns:
f (float): the cost function value
g (list): the vales of all constraints
fail (0/1): indicates if the function finished successfully
"""
# Cost and Constraints
f = 0
g = []
fail = 0
# Run building simulation
x, cost, constraints = self.building.simulate(policy)
f = np.sum(cost)
g.append(np.sum(constraints))
# print(f)
# print(g[0])
return f, g, fail
def optimize_twist(**k):
omega = k['omega']
omega_lower = k['omega_lower']
omega_upper = k['omega_upper']
twist0 = k['twist0']
twist0_lower = k['twist0_lower']
twist0_upper = k['twist0_upper']
n_elements = k['n_elements']
dtwist = k['dtwist']
dtwist_lower = k['dtwist_lower']
dtwist_upper = k['dtwist_upper']
opt_prob_fc = Optimization('Rotor in Hover w/ Fixed Chord',
objfun_optimize_twist)
opt_prob_fc.addVar('omega',
'c',
value=omega,
lower=omega_lower,
upper=omega_upper)
opt_prob_fc.addVar('twist0',
'c',
value=twist0,
lower=twist0_lower,
upper=twist0_upper)
opt_prob_fc.addVarGroup('dtwist',
n_elements - 1,
'c',
value=dtwist,
lower=dtwist_lower,
upper=dtwist_upper)
opt_prob_fc.addObj('f')
opt_prob_fc.addCon('thrust', 'i')
n_blades = k['n_blades']
root_cutout = k['root_cutout']
radius = k['radius']
dy = k['dy']
dr = k['dr']
y = k['y']
r = k['r']
pitch = k['pitch']
airfoils = k['airfoils']
thrust = k['thrust']
chord = k['chord']
allowable_Re = k['allowable_Re']
Cl_tables = k['Cl_tables']
Cd_tables = k['Cd_tables']
Cl_funs = k['Cl_funs']
Cd_funs = k['Cd_funs']
tip_loss = k['tip_loss']
mach_corr = k['mach_corr']
alt = k['alt']
# Routine for optimizing twist with a constant chord
slsqp2 = SLSQP()
slsqp2.setOption('IPRINT', 1)
slsqp2.setOption('MAXIT', 200)
slsqp2.setOption('ACC', 1e-7)
fstr, xstr, inform = slsqp2(opt_prob_fc,
sens_type='FD',
n_blades=n_blades,
n_elements=n_elements,
root_cutout=root_cutout,
radius=radius,
dy=dy,
dr=dr,
y=y,
r=r,
pitch=pitch,
airfoils=airfoils,
thrust=thrust,
tip_loss=tip_loss,
mach_corr=mach_corr,
omega=omega,
chord=chord,
allowable_Re=allowable_Re,
Cl_tables=Cl_tables,
Cd_tables=Cd_tables,
Cl_funs=Cl_funs,
Cd_funs=Cd_funs,
alt=alt)
return fstr, xstr
config,
feasible_area,
attraction_center=(0.5 * (site_x_start + site_x_end),
0.5 * (site_y_start + site_y_end)))
distance_constraint = get_minimum_distance_constraint_func(config)
constraints = [feasible_constraint, distance_constraint]
# Place some turbines
config.set_site_dimensions(site_x_start, site_x_end, site_y_start, site_y_end)
deploy_turbines(config, nx=8, ny=2, friction=10.5)
config.info()
rf = ReducedFunctional(config)
#m0 = rf.initial_control()
#rf.update_turbine_cache(m0)
#File("turbines.pvd") << config.turbine_cache.cache["turbine_field"]
#import sys; sys.exit()
# Load checkpoints if desired by the user
if len(sys.argv) > 1 and sys.argv[1] == "--from-checkpoint":
rf.load_checkpoint("checkpoint")
parameters['form_compiler'][
'cpp_optimize_flags'] = '-O3 -ffast-math -march=native'
nlp, grad = rf.pyopt_problem(constraints=constraints)
slsqp = SLSQP(options={"MAXIT": 300})
res = slsqp(nlp, sens_type=grad)
