- Website: pypeec.otvam.ch
- Repository: github.com/otvam/pypeec
- Conda: anaconda.org/conda-forge/pypeec
- PyPi: pypi.org/project/pypeec
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!
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
- Can be used from the command line
- Can be used with Jupyter notebooks
- Advanced plotting capabilities
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
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.
- PyPEEC
- Releases
- Documentation
- Name: Thomas Guillod
- Affiliation: Dartmouth College
- Email: [email protected]
- Website: https://otvam.ch
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
(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/.
