492,
outputdir=
'../../../outputs/6.yaw_environment/Paper3/Zhoushan_mesh_30min/restart_30min-e&v'
)
# Operation of tidal turbine farm through a callback
cb = turbines.TurbineFunctionalCallback(solver_obj)
solver_obj.add_callback(cb, 'timestep')
#Effected area location
E_area_centre_point = [(xmin + xmax) / 2, ymax + 800]
E_area_circle = 60
# Operation of tidal turbine farm about each turbine output through a callback
cb2 = rmse_r2.RMSECallback(
solver_obj,
'../../../outputs/6.yaw_environment/Paper3/Zhoushan_mesh_30min/optimisation/intermediate-forward',
E_area_centre_point, E_area_circle)
solver_obj.add_callback(cb2, 'timestep')
# start computer forward model
solver_obj.iterate(update_forcings=update_forcings)
# ###set up interest functional and control###
power_output = sum(cb.average_power)
maxoutput, maxeffect = 2027.5361102533254, 1218.9462261767894
interest_functional = (P_factor * (power_output / maxoutput) - (1 - P_factor) *
(cb2.RMSEaverage / maxeffect)) * maxoutput
print(interest_functional, power_output, cb2.RMSEaverage)
# specifies the control we want to vary in the optimisation
c = [Control(x) for x in farm_options.turbine_axis
solver_obj.load_state(
492,
outputdir=
'../../../outputs/6.yaw_environment/Paper3/Zhoushan_mesh/restart_5min-e&v')
# Operation of tidal turbine farm through a callback
cb = turbines.TurbineFunctionalCallback(solver_obj)
solver_obj.add_callback(cb, 'timestep')
#Effected area location
E_area_centre_point = [(xmin + xmax) / 2, (ymax + ymin) / 2 + (214 + 400)]
E_area_circle = 60
# Operation of tidal turbine farm about each turbine output through a callback
cb2 = rmse_r2.RMSECallback(
solver_obj,
'../../../outputs/6.yaw_environment/Paper3/Zhoushan_mesh/forward',
E_area_centre_point, E_area_circle)
solver_obj.add_callback(cb2, 'timestep')
#Effected area location
E_area_centre_point = [(xmin + xmax) / 2, (ymax + ymin) / 2 - (214 + 400)]
E_area_circle = 60
# Operation of tidal turbine farm about each turbine output through a callback
cb3 = rmse_r2.RMSECallback(
solver_obj,
'../../../outputs/6.yaw_environment/Paper3/Zhoushan_mesh/forward',
E_area_centre_point, E_area_circle)
solver_obj.add_callback(cb3, 'timestep')
# start computer forward model
def update_forcings(t):
print_output("Updating tidal elevation at t = {}".format(t))
ramp = tanh(t / 2000.)
tidal_vel.project(Constant(-ramp * 2))
#set initial condition
# solver_obj.assign_initial_conditions(uv=as_vector((1e-7, 0.0)), elev=tidal_elev)
solver_obj.load_state(60, '../../../outputs/3.environment/restart')
# Operation of tidal turbine farm through a callback
cb = turbines.TurbineFunctionalCallback(solver_obj)
solver_obj.add_callback(cb, 'timestep')
cb2 = rmse_r2.RMSECallback(solver_obj,
'../../../outputs/3.environment/restart', 3)
solver_obj.add_callback(cb2, 'timestep')
# start computer forward model
solver_obj.iterate(update_forcings=update_forcings)
###set up interest functional and control###
power_output = sum(cb.integrated_power)
interest_functional = -cb2.RMSEaverage
print(interest_functional)
# specifies the control we want to vary in the optimisation
optimise_angle_only = False
# if farm_options.considering_yaw:
# if optimise_angle_only:
# if farm_options.considering_yaw:
solver_obj.assign_initial_conditions(uv=as_vector((-Constant(speed), 0.0)),
elev=tidal_elev)
# Operation of tidal turbine farm through a callback
cb = turbines.TurbineFunctionalCallback(solver_obj)
solver_obj.add_callback(cb, 'timestep')
#Effected area location
E_area_centre_point = [450, 250]
E_area_circle = 40
# Operation of tidal turbine farm about each turbine output through a callback
cb2 = rmse_r2.RMSECallback(
solver_obj,
'../../../outputs/6.yaw_environment/Yaw_Ideal/restart-conference_mesh2_with_effected_area',
E_area_centre_point, E_area_circle)
solver_obj.add_callback(cb2, 'timestep')
# start computer forward model
solver_obj.iterate(update_forcings=update_forcings)
# ###set up interest functional and control###
power_output = sum(cb.current_power)
maxoutput, maxeffect = 10567.419789875763, 6277.669515955079
interest_functional = (P_factor * (power_output / maxoutput) - (1 - P_factor) *
(cb2.RMSE_current[-1] / maxeffect)) * maxoutput
print(interest_functional, power_output, cb2.RMSE_current[-1])
# specifies the control we want to vary in the optimisation
optimise_angle_only = False
solver_obj.assign_initial_conditions(uv=as_vector((-Constant(speed), 0.0)),
elev=tidal_elev)
# Operation of tidal turbine farm through a callback
cb = turbines.TurbineFunctionalCallback(solver_obj)
solver_obj.add_callback(cb, 'timestep')
#Effected area location
E_area_centre_point = [600, 250]
E_area_circle = 40
# Operation of tidal turbine farm about each turbine output through a callback
cb2 = rmse_r2.RMSECallback(
solver_obj,
'../../../outputs/6.yaw_environment/Yaw_Ideal/test_yaw_effect_restart',
E_area_centre_point, E_area_circle)
solver_obj.add_callback(cb2, 'timestep')
# start computer forward model
solver_obj.iterate(update_forcings=update_forcings)
# ###set up interest functional and control###
power_output = sum(cb.current_power)
interest_functional = (P_factor * (power_output / 7643.52) - (1 - P_factor) *
(cb2.RMSE_current[-1] / 7040.53)) * 7643.52
# print(interest_functional,power_output,cb2.RMSE_current[-1])
# specifies the control we want to vary in the optimisation
optimise_angle_only = True
optimise_layout_only = False