How to solve optimization problem in Pyleecan¶

This tutorial explains how to use Pyleecan to solve constrained global optimization problem.

The notebook related to this tutorial is available on GitHub.

The tutorial introduces the different objects to define that enable to parametrize each aspect of the optimization. To do so, we will present an example to maximize the average torque and minimize the first torque harmonic by varying the stator slot opening and the rotor external radius and by adding a constraint on torque ripple.

Problem definition¶

The object OptiProblem contains all the optimization problem characteristics:

• the output that contains the simulation default parameters

• the design variable to vary some parameters of the simulation (e.g. input current, topology of the machine)

• the objective functions to minimize for the simulation

• some constraints (optional)

Default Output definition¶

To define the problem, we first define an output which contains the default simulation and its results. The optimization process will be based on this output, each evaluation will copy the simulation, set the value of the design variables and run the new simulation.

For this example, we use the simulation defined in the tutorial How to define a simulation to call FEMM, but we decrease the precision of the FEMM mesh to speed up the calculations.

[2]:

from numpy import ones, pi, array, linspace
from pyleecan.Classes.Simu1 import Simu1
from pyleecan.Classes.Output import Output
from pyleecan.Classes.InputCurrent import InputCurrent
from pyleecan.Classes.ImportMatrixVal import ImportMatrixVal
from pyleecan.Classes.MagFEMM import MagFEMM
from pyleecan.definitions import DATA_DIR
from os.path import join

# Import the machine from a script
rotor_speed = 2000 # [rpm]

# Create the Simulation
mySimu = Simu1(name="EM_SIPMSM_AL_001", machine=IPMSM_A)

# Defining Simulation Input
mySimu.input = InputCurrent()

# time discretization [s]
mySimu.input.Nt_tot = 16

# Angular discretization along the airgap circonference for flux density calculation
mySimu.input.Na_tot = 1024

# Rotor speed as a function of time [rpm]
mySimu.input.N0 = rotor_speed

# Stator currents as a function of time, each column correspond to one phase [A]
mySimu.input.Id_ref = -100 # [A]
mySimu.input.Iq_ref = 200 # [A]

# Definition of the magnetic simulation (is_mmfr=False => no flux from the magnets)
mySimu.mag = MagFEMM(
type_BH_stator=0, # 0 to use the B(H) curve,
# 1 to use linear B(H) curve according to mur_lin,
# 2 to enforce infinite permeability (mur_lin =100000)
type_BH_rotor=0,  # 0 to use the B(H) curve,
# 1 to use linear B(H) curve according to mur_lin,
# 2 to enforce infinite permeability (mur_lin =100000)
angle_stator=0,  # Angular position shift of the stator
file_name = "", # Name of the file to save the FEMM model
is_symmetry_a=True,   # 0 Compute on the complete machine, 1 compute according to sym_a and is_antiper_a
sym_a = 4, # Number of symmetry for the angle vector
is_antiper_a=True, # To add an antiperiodicity to the angle vector
Kmesh_fineness = 0.2, # Decrease mesh precision
Kgeo_fineness = 0.2, # Decrease mesh precision
)

# We only use the magnetic part
mySimu.force = None
mySimu.struct = None

# Set the default output for the optimization
defaultOutput = Output(simu=mySimu)


Minimization problem definition¶

To setup the optimization problem, we define some objective functions using the DataKeeper object.

Each objective function is stored in the keeper attribute of a DataKeeper. This type of function takes an output object in argument and returns a float to minimize. We gather the objective functions into a dictionnary.

[3]:

from pyleecan.Classes.DataKeeper import DataKeeper
import numpy as np

# Objective functions
def tem_av(output):
"""Return the average torque opposite (opposite to be maximized)"""
return -abs(output.mag.Tem_av)

def harm1(output):
"""Return the first torque harmonic """
N = output.mag.time.size

# Compute the real fft of the torque
sp = 2 / N * np.abs(np.fft.rfft(output.mag.Tem.values))

# Return the first torque harmonic
return sp[1]

my_obj = [
DataKeeper(
name="Maximization of the average torque",
symbol="Tem_av",
unit="N.m",
keeper=tem_av,
),
DataKeeper(
name="Minimization of the first torque harmonic",
symbol="Tem_h1",
unit="N.m",
keeper=harm1,
),
]


Design variables¶

We use the object OptiDesignVar to define the design variables.

To define a design variable, we have to specify different attributes:

• name to define the design variable name

• unit to define the variable unit

• type_var to specify the variable “type”:

• interval for continuous variables

• set for discrete variables

• space to set the variable bound

• setter to access to the variable in the output’s simu object. This attribute must begin by “simu”.

• get_value to define the variable for the first generation, the function takes the space in argument and returns the variable value

We store the design variables in a dictionnary that will be in argument of the problem. For this example, we define two design variables:

1. Stator slot opening: can be any value between 0 and the slot width.

2. Rotor external radius: can be one of the four value specified [99.8%, 99.9%, 100%, 100.1%] of the default rotor external radius

[4]:

from pyleecan.Classes.OptiDesignVar import OptiDesignVar
import random

# Design variables
my_design_var = [
OptiDesignVar(
name="Stator slot opening",
symbol = "SW0",
unit = "m",
type_var="interval",
space=[
0 * defaultOutput.simu.machine.stator.slot.W2,
defaultOutput.simu.machine.stator.slot.W2,
],
get_value=lambda space: random.uniform(*space), # To initiate randomly the first generation
setter="simu.machine.stator.slot.W0", # Variable to edit

),
OptiDesignVar(
symbol = "Rext",
unit = "m",
type_var="set",
space=[
0.998 * defaultOutput.simu.machine.rotor.Rext,
0.999 * defaultOutput.simu.machine.rotor.Rext,
defaultOutput.simu.machine.rotor.Rext,
1.001 * defaultOutput.simu.machine.rotor.Rext,
],
get_value=lambda space: random.choice(space),
setter = "simu.machine.rotor.Rext"
),
]


Constraints¶

The class OptiConstraint enables to define some constraint. For each constraint, we have to define the following attributes:

• name

• type_const: type of constraint

• “==”

• “<=”

• “<”

• “>=”

• “>”

• value: value to compare

• get_variable: function which takes output in argument and returns the constraint value

We also store the constraints into a dict.

[5]:

from pyleecan.Classes.OptiConstraint import OptiConstraint
my_constraint = [
OptiConstraint(
name = "const1",
type_const = "<=",
value = 700,
get_variable = lambda output: abs(output.mag.Tem_rip_pp),
)
]


Evaluation function¶

We can create our own evaluation function if needed by defining a function which only take an output in argument.

For this example we keep the default one which calls the Output.simu.run method.

[6]:

from pyleecan.Classes.OptiProblem import OptiProblem

# Problem creation
my_prob = OptiProblem(
output=defaultOutput,
design_var=my_design_var,
obj_func=my_obj,
constraint = my_constraint,
eval_func = None # To keep the default evaluation function
)


Solver¶

The class OptiGenAlgNsga2 enables to solve our problem using NSGA-II genetical algorithm. The algorithm takes several parameters:

Parameter

Description

Type

Default Value

problem

Problem to solve

OptiProblem

mandatory

size_pop

Population size per generation

int

40

nb_gen

Generation number

int

100

p_cross

Crossover probability

float

0.9

p_mutate

Mutation probability

float

0.1

The solve method performs the optimization and returns an OutputMultiOpti object which contains the results.

[7]:

from pyleecan.Classes.OptiGenAlgNsga2Deap import OptiGenAlgNsga2Deap

# Solve problem with NSGA-II
solver = OptiGenAlgNsga2Deap(problem=my_prob, size_pop=16, nb_gen=8, p_mutate=0.5)
res = solver.solve()

16:24:12 Starting optimization...
Log file: C:\Users\eomys\AppData\Roaming\pyleecan\Pyleecan_optimization.log
Number of generations: 8
Population size: 16

16:24:12  gen     0: 100%,    0 errors,   0 infeasible.
16:31:46  gen     1: 100%,    0 errors,   0 infeasible.
16:40:23  gen     2: 100%,    0 errors,   0 infeasible.
16:48:47  gen     3: 100%,    0 errors,   0 infeasible.
16:57:52  gen     4: 100%,    0 errors,   0 infeasible.
17:07:01  gen     5: 100%,    0 errors,   0 infeasible.
17:15:13  gen     6: 100%,    0 errors,   0 infeasible.
17:23:42  gen     7: 100%,    0 errors,   0 infeasible.


During the algorithm the object displays some data containing:

• number of errors: failure during the objective function execution

• number of infeasible: number of individual with constraints violations

Plot results¶

OutputMultiOpti has several methods to display some results:

• plot_generation: to plot individuals for in 2D

• plot_pareto: to plot the pareto front in 2D

[8]:

%matplotlib notebook
import matplotlib.pyplot as plt

# Create a figure containing 4 subfigures (axes)
fig, axs = plt.subplots(2,2, figsize=(8,8))

# Plot every individual in the fitness space
res.plot_generation(
x_symbol = "Tem_av", # symbol of the first objective function or design variable
y_symbol = "Tem_h1", # symbol of the second objective function or design variable
ax = axs[0,0] # ax to plot
)

# Plot every individual in the design space
res.plot_generation(
x_symbol = "SW0",
y_symbol = "Rext",
ax = axs[0,1]
)

# Plot pareto front in fitness space
res.plot_pareto(
x_symbol = "Tem_av",
y_symbol = "Tem_h1",
ax = axs[1,0]
)

# Plot pareto front in design space
res.plot_pareto(
x_symbol = "SW0",
y_symbol = "Rext",
ax = axs[1,1]
)

fig.tight_layout()