Stewart, David R;
Vatani, Matin;
Alden, Rosemary E;
Lewis, Donovin D;
Asef, Pedram;
Ionel, Dan M;
(2024)
Combined Machine Learning and Differential Evolution for Optimal Design of Electric Aircraft Propulsion Motors.
In:
2024 13th International Conference on Renewable Energy Research and Applications (ICRERA).
(pp. pp. 1823-1828).
IEEE: Nagasaki, Japan.
Preview |
Text
2024 ICRERA ML Differential Evolution Optimal Design Electric Aircraft Propulsion.pdf - Accepted Version Download (7MB) | Preview |
Abstract
Electric aircraft propulsion requires highly efficient and power-dense fault-tolerant electric motors optimized for specific flight profile operation. State-of-the-art design of electric motors involves substantial computational resources and combines electromagnetic finite element analysis (FEA) and optimization techniques. This paper proposes a new approach using a physics-based machine learning (ML) multi-input univariate meta-model trained on FEA and differential evolution (DE) optimization results to predict electromagnetic torque output. Hundreds of individual designs, generated through multiple generations of a DE algorithm, are analyzed by 3D FEA to create a database, which is then employed for the training and satisfactory validation of the ML model. The coreless axial flux permanent magnet (CAFPM) machine topology considered for an example study typically necessitates intensive 3D FEA simulation due to its specific geometry, although it does not experience the non-linear saturation associated with ferromagnetic core materials. The hybrid ML-DE model is satisfactorily validated with an R 2 value of 0.97 and normalized root mean squared error (NRMSE) of less than 0.05. The relative merits of the newly proposed combined ML-DE optimization are discussed, especially in terms of low error and the potential for overall computational time minimization.
| Type: | Proceedings paper |
|---|---|
| Title: | Combined Machine Learning and Differential Evolution for Optimal Design of Electric Aircraft Propulsion Motors |
| Event: | 2024 13th International Conference on Renewable Energy Research and Applications (ICRERA) |
| Dates: | 9 Nov 2024 - 13 Nov 2024 |
| ISBN-13: | 979-8-3503-7558-9 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICRERA62673.2024.10815216 |
| Publisher version: | https://doi.org/10.1109/icrera62673.2024.10815216 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Meta-Modeling, artificial neural network, deep learning, electric aircraft, axial flux, coreless stator, Halbach PM array |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10205528 |
Archive Staff Only
 |
View Item |
