C-Coil: A High Performance Computing Approach for Magnetostatic Circular Coil Calculations

Accurate computation of magnetostatic coupling between non-coaxial circular coils remains prohibitively expensive when millions of configurations must be evaluated for design-space exploration. We propose a novel approach based on numerical methods to improve performance by 5 to 7 orders of magnitude while matching the accuracy of state-of-the-art semi-analytical methods. While other approaches strive to reduce the number of integration directions in the six-fold integral to 2 or 4, we propose a five-fold integral with simple-to-evaluate integrands. In place of the filament method, we employ the Gauss-Legendre quadrature due to its exponential convergence and find that numerical integration can be quicker than analytic integral evaluation. Furthermore, to tackle the complexity of allocating the computational resources to each of the five integration directions, we propose a heuristic that leads to 2 orders of magnitude lower computation time or 2 to 4 orders of magnitude higher accuracy. We also provide an implementation of our approach in C-Coil, an open-source C++ library with Python bindings that can also be used in MATLAB.

Authors

Institutions

Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3589331
Primary Topic
Electric Motor Design and Analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

C-Coil: A High Performance Computing Approach for Magnetostatic Circular Coil Calculations

Lara Vrabac, Dario Bojanjac, Davor Dobrota, Nikola Sočec et al.
IEEE Access
Electric Motor Design and Analysis
article

C-Coil: A High Performance Computing Approach for Magnetostatic Circular Coil Calculations

Lara Vrabac, Dario Bojanjac, Davor Dobrota, Nikola Sočec, Filip Vučić
article en

Abstract

Accurate computation of magnetostatic coupling between non-coaxial circular coils remains prohibitively expensive when millions of configurations must be evaluated for design-space exploration. We propose a novel approach based on numerical methods to improve performance by 5 to 7 orders of magnitude while matching the accuracy of state-of-the-art semi-analytical methods. While other approaches strive to reduce the number of integration directions in the six-fold integral to 2 or 4, we propose a five-fold integral with simple-to-evaluate integrands. In place of the filament method, we employ the Gauss-Legendre quadrature due to its exponential convergence and find that numerical integration can be quicker than analytic integral evaluation. Furthermore, to tackle the complexity of allocating the computational resources to each of the five integration directions, we propose a heuristic that leads to 2 orders of magnitude lower computation time or 2 to 4 orders of magnitude higher accuracy. We also provide an implementation of our approach in C-Coil, an open-source C++ library with Python bindings that can also be used in MATLAB.

IEEE AccessVol. 13
University of Zagreb (HR), Xerox (France) (FR), Zürcher Fachhochschule (CH), École Polytechnique Fédérale de Lausanne (CH)
Affordable and clean energy
Openalex Percentile: Top 85%
Electric Motor Design and Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.