Electric Machine Optimization Tool (EMOT)

EMOT is a tool for optimal design of electric machines.

It consists of .aedt file io for parsing model files from Ansys Maxwell and an optimization module from Playtpus in GitHub.

.aedt file io

AedtProject can read and write .aedt file directly.

This project simply convert .aedt file to xml, load it using xmltodict.

Installation

Install requirements first.

pip install -r requirements.txt

Usage:

Quick start

from EMOT import AedtProject

model1 = AedtProject('aa.aedt', active_design='Maxwell2DDesign1')
model1.change_variables(
    design_name='Maxwell2DDesign1',
    var_name='x1',
    value='10mm'
)

# save to current file
model1.save()

# save file to another place or change name
model2 = model1.save_to(filename='a1.aedt')

# run simulation
model2.run_simulation(
    design_name='Maxwell2DDesign1',
    setup='Setup1',
    timeout_in_minutes=100
)
model2.export_csv()

Generate models

from EMOT import AedtProject
from EMOT.variables import StepReal

model1 = AedtProject('aa.aedt', active_design='Maxwell2DDesign1')
vars = {
    'to': StepReal(min_value=1, max_value=10, step=1, name='T0'),
    't1': StepReal(min_value=1, max_value=10, step=1, name='T1'),
}
combinations = model1.set_var_combination(vars)

# generate model
dataset_dir = model1.generate_models(
    path='./temp_models',
    var_combination=combinations
)

# collect data and form a dataset
dataset = model1.collect_data(dataset_dir)

Tool for topology optimization

from EMOT import AedtProject
import numpy as np
from TopologyModel import TopologyModel

model1 = AEDTProject('aa.aedt', active_design='Maxwell2DDesign1')

# initialize topology and generte dxf model
topology = TopologyModel('top1.npz')
dxf_model = topology.save_dxf('dxf_file.dxf')

# import dxf model to FEM model 
model1.import_dxf_and_subtract(dxf_model, symmetric=False)

# run simulation and export
model1.run_simulation(
    setup='Setup1',
    timeout_in_minutes=60
)

torque = model1.export_csv('torque', 'aa.csv')
field = model1.export_field('bxyz', 'field.fld')

# save field to npy
np.save('bxyz.npy', field)

Cite this repository

If you are using EMOT as part of your research, teaching, or other activities, we would be grateful if you could star the repository and/or cite our work.

For citation purposes, you can use the following BibTex entry.

@software{Wu_EMOT_2021,
author = {Wu, Huihuan and Bi, Yanding and Huang, Jiahui},
doi = {10.5281/zenodo.5751256},
month = {12},
title = {{EMOT}},
url = {https://github.com/hh-wu/EMOT},
version = {1.0.6},
year = {2021}
}