PyPEEC - 3D Quasi-Magnetostatic Solver

• Website: pypeec.otvam.ch
• Repository: github.com/otvam/pypeec
• Paper: doi.org/10.21105/joss.06644
• Conda: anaconda.org/conda-forge/pypeec
• PyPI: pypi.org/project/pypeec

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.
• Fast with moderate memory requirements.
• Representation of the geometry with 3D voxels.
• Parallel processing and GPU acceleration are available.
• 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.
• Can be used from the command line or with an API.
• Advanced plotting and visualization capabilities.
• Compatible with Jupyter notebooks.
• Compatible with ParaView.

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 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 force computations.
• No advanced boundary conditions.
• No domain decomposition techniques.
• No hierarchical matrix techniques.
• No model order reduction techniques.
• Limited to voxel geometries.

The PyPEEC package contains the following tools:

• mesher - Create a 3D voxel structure from the geometry.
• viewer - Visualization of the 3D voxel structure.
• solver - Solve the quasi-magnetostatic 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
  • Paper
  • Citation
  • Issues

• Releases

  • PyPI
  • Conda
  • GitHub
  • Zenodo

• Documentation

  • Installation
  • Tutorial
  • Examples
  • Gallery

Author

• Name: Thomas Guillod
• Affiliation: Dartmouth College
• Email: guillod@otvam.ch
• 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) 2023-2025 / 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.