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PyPEEC - 3D Quasi-Magnetostatic Solver

PyPEEC Banner

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
  • 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

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

Author

Credits

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

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

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/.

Dartmouth and PMIC