Author

T. Benamara

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#machine learning Preprint Sep 2026

Periodic Neural Mapping for Unsteady Rotor-Blade Pressure and Aeroelastic Load Prediction

Accurate prediction of unsteady aerodynamic loads remains a major challenge in turbomachinery design. High-fidelity Computational Fluid Dynamics (CFD) simulations are expensive, while aeroelastic Quantities of Interest (QoI) depend sensitively on the temporal evolution of the pressure field. This work introduces period...

Lionel Salesses, J. Dominique, T. Benamara et al. · 0 citations

Open access Aug 2026

INDUSTRIAL MULTI-FIDELITY SURROGATE-BASED OPTIMIZATION FRAMEWORK FOR COMPRESSOR BLADE DESIGN: BALANCING AEROMECHANICS, CONTACT ROBUSTNESS, AND NI-POD MODELING

This paper presents the improvements of a modular, multi-disciplinary, and multi-fidelity Surrogate-Based Optimization (SBO) framework for rotor blade design, aiming at reducing computational cost and accelerating the design process in aeromechanical applications. The concept of “useful accuracy” is central to our ap...

F. Nyssen, R. Nigro, L. Baert et al. · Journal of turbomachinery · 0 citations

#machine learning Preprint Sep 2026

Neural Field Ensembles for Aerodynamic Surface Prediction: Winning Solution to the ONERA CRM Wall Distribution 2025 Challenge

Machine-learning surrogate models offer a promising alternative to high-fidelity Computational Fluid Dynamics (CFD) simulations for aerodynamic analysis and design. However, constructing accurate surrogates for realistic aircraft configurations remain challenging due to complex geometries, multiple flow regimes, and li...

Lionel Salesses, C. Sainvitu, T. Benamara · 0 citations