PyPEEC - 3D Quasi-Magnetostatic Solver

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• Website: pypeec.otvam.ch
• Repository: github.com/otvam/pypeec
• Conda: anaconda.org/conda-forge/pypeec
• PyPI: pypi.org/project/pypeec

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Summary

PyPEEC is a 3D quasi-magnetostatic PEEC solver developed at Dartmouth College within the Power Management Integration Center (PMIC). PyPEEC is a fast solver (FFT and GPU accelerated) that can simulate a large variety of magnetic components (inductors, transformers, chokes, IPT coils, busbars, etc.). The tool contains a mesher (STL, PNG, and GERBER formats), a solver (static and frequency domain), and advanced plotting capabilities. The code is written in Python and is fully open source!

Capabilities

PyPEEC features the following characteristics:

• PEEC method with FFT acceleration
• Representation of the geometry with 3D voxels
• Multithreading and GPU acceleration are available
• Fast with moderate memory requirements
• Import the geometry from STL, PNG, and GERBER files
• Draw the geometry with stacked 2D vector shapes or voxel indices
• Pure Python and open source implementation
• Advanced plotting capabilities
• Can be used from the command line
• Can be used with Jupyter notebooks
• Compatible with ParaView visualizations

PyPEEC solves the following 3D quasi-magnetostatic problems:

• Frequency domain solution (DC and AC)
• Conductive and magnetic domains (ideal or lossy)
• Isotropic, anisotropic, lumped, and distributed materials
• Connection of current and voltage sources
• Extraction of the loss and energy densities
• Extraction of the current density, flux density, and potential
• Extraction of the terminal voltage, current, and power
• Computation of the free-space magnetic field

PyPEEC has the following limitations:

• No capacitive effects
• No dielectric domains
• No advanced boundaries conditions
• No model order reduction techniques
• Limited to voxel geometries

The PyPEEC package contains the following tools:

• mesher - create a 3D voxel structure from STL or PNG files
• viewer - visualization of the 3D voxel structure
• solver - solver for the magnetic field problem
• plotter - visualization of the problem solution

Warning

The geometry is meshed with a regular voxel structure (uniform grid). Some geometries/problems are not suited for voxel structures (inefficient meshing). For such cases, PyPEEC can be very slow and consume a lot of memory.

Project Links

• PyPEEC

  • Website
  • Repository
  • Issues

• Releases

  • PyPI
  • Conda
  • GitHub

• Documentation

  • Installation
  • Tutorial
  • Examples
  • Gallery

Author

• Name: Thomas Guillod
• Affiliation: Dartmouth College
• Email: [email protected]
• Website: https://otvam.ch

Credits

PyPEEC was created at Dartmouth College by the research group of Prof. Sullivan:

• Dartmouth College, NH, USA: https://dartmouth.edu
• Dartmouth Engineering: https://engineering.dartmouth.edu
• NSF/PMIC: https://pmic.engineering.dartmouth.edu

The FFT-accelerated PEEC method with voxels has been first described and implemented in:

• Torchio, R., IEEE TPEL, 10.1109/TPEL.2021.3092431, 2022
• Torchio, R., https://github.com/UniPD-DII-ETCOMP/FFT-PEEC

Copyright

(c) 2023-2024 / Thomas Guillod / Dartmouth College

This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

In order to facilitate the redistribution, this source code is multi-licensed under the following additional licenses: LGPLv2, LGPLv3, GPLv2, and GPLv3.

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