https://github.com/otvam/pypeec
PyPEEC - 3D Quasi-Magnetostatic FFT/PEEC Solver
https://github.com/otvam/pypeec
3d conductor electric fft field-simulation frequency-domain magnetic maxwell-equations peec power-electronics pypeec voxel
Last synced: 2 months ago
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PyPEEC - 3D Quasi-Magnetostatic FFT/PEEC Solver
- Host: GitHub
- URL: https://github.com/otvam/pypeec
- Owner: otvam
- License: mpl-2.0
- Created: 2022-12-29T02:49:10.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-04-12T20:28:26.000Z (2 months ago)
- Last Synced: 2025-04-12T20:29:41.634Z (2 months ago)
- Topics: 3d, conductor, electric, fft, field-simulation, frequency-domain, magnetic, maxwell-equations, peec, power-electronics, pypeec, voxel
- Language: Python
- Homepage: https://pypeec.otvam.ch
- Size: 13.6 MB
- Stars: 20
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE.txt
- Security: .github/SECURITY.md
Awesome Lists containing this project
README
# PyPEEC - 3D Quasi-Magnetostatic Solver
[](https://www.python.org/downloads)
[](http://mozilla.org/MPL/2.0)
[](https://github.com/otvam/pypeec)
[](https://doi.org/10.5281/zenodo.14941571)
[](https://pypeec.otvam.ch)
[](https://pypi.org/project/pypeec)
[](https://anaconda.org/conda-forge/pypeec)
---
* **Website: [pypeec.otvam.ch](https://pypeec.otvam.ch)**
* **Repository: [github.com/otvam/pypeec](https://github.com/otvam/pypeec)**
* **Conda: [anaconda.org/conda-forge/pypeec](https://anaconda.org/conda-forge/pypeec)**
* **PyPI: [pypi.org/project/pypeec](https://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** pr 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 boundaries 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 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](https://pypeec.otvam.ch)
* [Repository](https://github.com/otvam/pypeec)
* [Issues](https://github.com/otvam/pypeec/issues)
* **Releases**
* [PyPI](https://pypi.org/project/pypeec)
* [Conda](https://anaconda.org/conda-forge/pypeec)
* [GitHub](https://github.com/otvam/pypeec/releases)
* [Zenodo](https://doi.org/10.5281/zenodo.14941571)
* **Documentation**
* [Installation](https://pypeec.otvam.ch/content/install.html)
* [Tutorial](https://pypeec.otvam.ch/content/tutorial.html)
* [Examples](https://pypeec.otvam.ch/content/examples.html)
* [Gallery](https://pypeec.otvam.ch/content/gallery.html)
## 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) 2023-2025 / 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.
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