assert minconv > 1.95
if optimise:
# Optimise the control for minimal functional (i.e. maximum profit)
# with a gradient based optimisation algorithm using the reduced functional
# to replay the model, and computing its derivative via the adjoint
# By default scipy's implementation of L-BFGS-B is used, see
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin_l_bfgs_b.html
# options, such as maxiter and pgtol can be passed on.
if optimise_angle_only:
lb = [0] * len(farm_options.turbine_axis)
ub = [360] * len(farm_options.turbine_axis)
td_opt = minimize(rf,
method='SLSQP',
bounds=[lb, ub],
options={
'maxiter': 100,
'ftol': 1e-9
})
else:
r = farm_options.turbine_options.diameter / 2.
lb = np.array([[site_x1 + r, site_y1 + r]
for _ in farm_options.turbine_coordinates]).flatten()
ub = np.array([[site_x2 - r, site_y2 - r]
for _ in farm_options.turbine_coordinates]).flatten()
if farm_options.considering_yaw:
lb = list(lb) + [0] * len(farm_options.turbine_axis)
ub = list(ub) + [360] * len(farm_options.turbine_axis)
# to replay the model, and computing its derivative via the adjoint
# By default scipy's implementation of L-BFGS-B is used, see
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin_l_bfgs_b.html
# options, such as maxiter and pgtol can be passed on.
optimise_layout_only = False
d = farm_options.turbine_options.diameter
lb = [90] * int(len(farm_options.turbine_axis) / 2) + [270] * int(
len(farm_options.turbine_axis) / 2)
ub = [270] * int(len(farm_options.turbine_axis) / 2) + [450] * int(
len(farm_options.turbine_axis) / 2)
td_opt = minimize(rf,
method='SLSQP',
bounds=[lb, ub],
options={
'maxiter': 200,
'ptol': 1e-3
})
t_end = time.time()
print('time cost: {0:.2f}min'.format((t_end - t_start) / 60))
if 1:
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
if rank == 0:
yag = yagmail.SMTP(user='******',
password='******',
host='smtp.qq.com')
yag.send(to=['*****@*****.**'],
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin_l_bfgs_b.html
# options, such as maxiter and pgtol can be passed on.
r = farm_options.turbine_options.diameter / 2.
lb = np.array([[site_x1 + r, site_y1 + r]
for _ in farm_options.turbine_coordinates]).flatten()
ub = np.array([[site_x2 - r, site_y2 - r]
for _ in farm_options.turbine_coordinates]).flatten()
if farm_options.considering_yaw:
lb = list(lb) + [0] * len(farm_options.turbine_coordinates)
ub = list(ub) + [360] * len(farm_options.turbine_coordinates)
mdc = turbines.MinimumDistanceConstraints(farm_options.turbine_coordinates,
farm_options.turbine_axis,
r * 3.)
td_opt, res = minimize(rf,
method='SLSQP',
bounds=[lb, ub],
constraints=mdc,
options={
'maxiter': 1000,
'ftol': 1e-6,
'disp': True
})
print(res)
t_end = time.time()
print('time cost:', (t_end - t_start) / 60, 'min')
