Surrogate-model-based Robust Multi-physics design optimization methods for electrical machines

Like all sectors concerned by electrification, the transport sector requires the design of high-performance electrical systems that respond to multiple constraints, such as cost, compactness, and efficiency. In this context, optimization has become an essential step in the design process of these systems, particularly for electrical machines.
When designing an electrical machine, methods based on finite elements, recognized for their accuracy and generic nature, are often used to simulate its performance. However, due to their relatively long computation times, their coupling with optimization loops is penalizing. 
Furthermore, to find robust solutions, the uncertainties affecting the design parameters as well as the physical properties of all components should be considered during the optimization, which increase significantly the optimization time. 
The proposed thesis subject aims at developing new optimization approaches that can handle multiple physics and take into account the different sources of uncertainties in the context of costly simulations. Learning surrogate models to replace the costly simulations and the combination of multi-fidelity simulations are the two main strategies that will be explored to reduce the computational cost of the complex optimization problem.

Keywords: Electrical machines, Multi-physics design optimization, Robust optimization, Surrogate models. 

• Academic supervisor    Prof Sami HLIOUI, SATIE, ORCID : 0000-0002-3992-8266 
• Doctoral School    ED147 Sciences et Ingénierie, CY Cergy Paris Université
• IFPEN supervisors    Dr André NASR, ORCID : 0000-0001-8185-4232 & Dr Delphine SINOQUET, ORCID : 0000-0002-3365-2051 
• PhD location    IFPEN, Rueil-Malmaison, France  
• Duration and start date    3 years, starting in the fourth quarter 2025 (Novembre 3)
• Employer    IFPEN
• Funding    Currently under instruction
• Academic requirements    University master’s degree in applied mathematics, Statistics     
• Language requirements    English level B2 (CEFR)  
• Other requirements    Statistics, Optimization, Programming skills (Matlab/R/Python)

To apply, please send your cover letter and CV to the IFPEN supervisors indicated here below.