# Making KratosMultiphysics backward compatible with python 2.6 and 2.7
from __future__ import print_function, absolute_import, division
# Import Kratos core and apps
from KratosMultiphysics import *
from KratosMultiphysics.ShapeOptimizationApplication import *
# Read parameters
with open("optimization_parameters.json", 'r') as parameter_file:
parameters = Parameters(parameter_file.read())
model = Model()
# Create optimizer and perform optimization
import optimizer_factory
optimizer = optimizer_factory.CreateOptimizer(
parameters["optimization_settings"], model)
optimizer.Optimize()
# Read parameters
with open("parameters.json", 'r') as parameter_file:
parameters = Parameters(parameter_file.read())
# Defining the model_part
optimization_model_part = ModelPart(
parameters["optimization_settings"]["design_variables"]
["optimization_model_part_name"].GetString())
optimization_model_part.ProcessInfo.SetValue(
DOMAIN_SIZE, parameters["optimization_settings"]["design_variables"]
["domain_size"].GetInt())
# Create optimizer and perform optimization
import optimizer_factory
optimizer = optimizer_factory.CreateOptimizer(parameters,
optimization_model_part)
optimizer.Optimize()
# =======================================================================================================
# Test results and clean directory
# =======================================================================================================
# Testing is done using the "json_output_process" & "json_check_process" within the structural analysis
# Cleaning
original_directory = os.getcwd()
output_directory = parameters["optimization_settings"]["output"][
"output_directory"].GetString()
optimization_model_part_name = parameters["optimization_settings"][
"design_variables"]["optimization_model_part_name"].GetString()
try:
elif x <= 25.0:
return 1.0 - (x - 20.0) / 5.0
else:
return 0.0
# =======================================================================================================
# Perform optimization
# =======================================================================================================
with open("parameters.json",'r') as parameter_file:
parameters = Parameters(parameter_file.read())
model = Model()
import optimizer_factory
optimizer = optimizer_factory.CreateOptimizer(parameters["optimization_settings"], model, CustomAnalyzer())
optimizer.Optimize()
# =======================================================================================================
# Test results and clean directory
# =======================================================================================================
output_directory = parameters["optimization_settings"]["output"]["output_directory"].GetString()
optimization_log_filename = parameters["optimization_settings"]["output"]["optimization_log_filename"].GetString() + ".csv"
optimization_model_part_name = parameters["optimization_settings"]["model_settings"]["model_part_name"].GetString()
# Testing
original_directory = os.getcwd()
os.chdir(output_directory)
with open(optimization_log_filename, 'r') as csvfile:
reader = csv.reader(csvfile, delimiter=',')
def ObjectiveGradient(X, opt_iter):
""" Returns the gradient of the objective function """
sensitivity = dict()
for node in design_surface.Nodes:
delta = node.Y - TentFunction(node.X)
if abs(delta) == 0.0:
sy = 0.0
else:
sy = delta / abs(delta)
sensitivity[node.Id] = [0.0, sy, 0.0]
return sensitivity
def Analyzer(X, controls, iterator, response):
""" Performs the analysis in the optimization loop """
if controls["1"]["calc_func"]:
response["1"]["func"] = ObjectiveFunction(X, iterator)
if controls["1"]["calc_grad"]:
response["1"]["grad"] = ObjectiveGradient(X, iterator)
import optimizer_factory
optimizer = optimizer_factory.CreateOptimizer(design_surface,
settings.KratosShapeSettings,
Analyzer)
optimizer.optimize()
local_gradient[1] = 0.0
local_gradient[2] = 0.0
response_gradient[node.Id] = local_gradient
return response_gradient
# =======================================================================================================
# Perform optimization
# =======================================================================================================
# Create optimizer and perform optimization
import optimizer_factory
optimizer = optimizer_factory.CreateOptimizer(parameters,
optimization_model_part,
CustomAnalyzer())
optimizer.Optimize()
# =======================================================================================================
# Test results and clean directory
# =======================================================================================================
output_directory = parameters["optimization_settings"]["output"][
"output_directory"].GetString()
response_log_filename = parameters["optimization_settings"]["output"][
"response_log_filename"].GetString() + ".csv"
optimization_model_part_name = parameters["optimization_settings"][
"design_variables"]["optimization_model_part_name"].GetString()
# Testing
original_directory = os.getcwd()
def analyzer(X,controls,iterator,response):
# ------------------------------------------------------------------------------------------------------
# Input format:
# ------------------------------------------------------------------------------------------------------
#
# X = { unique_node_id: [x,y,z],
# unique_node_id: [x,y,z],
# ... }
#
# controls = { "unique_func_id": {"calc_func": 1/0, "calc_grad": 1/0},
# "unique_func_id": {"calc_func": 1/0, "calc_grad": 1/0},
# ... }
#
# iterator = "mayor_itr.sub_itr" (Initial design iterator = "1.0" or "1.1")
#
# ------------------------------------------------------------------------------------------------------
# Output format:
# ------------------------------------------------------------------------------------------------------
#
# response = { "unique_func_id": {"func": xxx, "grad": {unique_node_id: [dx,dy,dz], ... }},
# "unique_func_id": {"func": xxx, "grad": {unique_node_id: [dx,dy,dz], ... }},
# ... }
#
# response may contain only those entries which were called through the controller in the input
#
# ------------------------------------------------------------------------------------------------------
if( iterator=="1.0" or iterator=="1.1" ):
# Some initialization for the disciplinary solvers might go here
# ------------------------------------------------------------------------------------------------------
# Compute some primal field
# - Note that objective and constraint values may be computed within the same solve
# - Note also that through the controls, we can make sure that whenever an adjoint gradient is to be
# calculated, the primal field is computed first. At the same time though, additional unnecessary primal
# function calls may be avoided in case different adjoint solutions need to be caluclated based on the
# same primal field. This is possible since through the controls we know from the beginning all
# the function calls that the optimizer asks for in this step, so we may optimize the workflow for each
# combination.
if( controls["some_objective_name"]["calc_func"] or
controls["some_objective_name"]["calc_grad"] or
controls["some_constraint_name"]["calc_func"] or
controls["some_constraint_name"]["calc_grad"] ):
# Run e.g. some CFD or CSM code
# ...-> objective_value, constraint_value
# Store objective value in response container
response["some_objective_name"]["func"] = objective_value
# Compute and store constraint
response["some_constraint_name"]["func"] = constraint_value
# ------------------------------------------------------------------------------------------------------
# Compute adjoint field and ojbective gradient if required
if( controls["some_objective_name"]["calc_grad"] ):
# Call disciplinary solver to compute objective gradients
# ... -> dFdX
# Store gradients in response container
response["some_objective_name"]["grad"] = dFdX
# ------------------------------------------------------------------------------------------------------
# Compute adjoint field and constraint gradient if required
if( controls["some_constraint_name"]["calc_grad"] ):
# Call disciplinary solver to compute constraint gradients
# Store gradients in response container
response["some_constraint_name"]["grad"] = dFdX
# ------------------------------------------------------------------------------------------------------
# Initalize model_part here to have it available for further use in this main script
design_surface = ModelPart("design_surface")
# Create an optimizer object
optimizer = optimizer_factory.CreateOptimizer(design_surface,settings.KratosShapeSettings,analyzer)
print("\n> ==============================================================================================================")
print("> Starting optimization")
print("> ==============================================================================================================\n")
# Call the optimize function of the optimizer
optimizer.optimize()
print("\n> ==============================================================================================================")
print("> Finished shape optimization!")
print("> ==============================================================================================================\n")
