Accelerating Design Space Exploration of Film Cooling with Parametric Machine Learning Based on High-Fidelity CFD.

Jan 1, 2026·
Sondak D
Nicolas Fougere
Nicolas Fougere
,
Higgins J
,
Jammalamadaka A
,
Laskowski G
,
Bi J
,
et al
· 0 min read
Image credit: simpleflying.com
Abstract
Shaped film cooling holes are a key element in cooling turbine components to increase their operating temperature while preventing component damage. There is therefore a need for designers to rapidly explore options and identify the optimum film cooling hole configuration. In this study, we propose a machine learning (ML) approach to evaluate the performance of a given film cooling hole shape in a matter of seconds using the publicly available 777-shaped hole design. The machine learning model was trained using data generated with a Lattice Boltzmann Method (LBM) numerical solver as part of a Design of Experiment with four different parameters the forward expansion angle, the lateral expansion angle, the slot injection angle, and the blowing ratio. Blind predictions from inference of the ML model were compared to results predicted by LBM and showed a mean error of ~3 percent for the area averaged film cooling effectiveness while preserving the design relative ranking. This suggests that the method described in this paper is viable by industry standards.
Publication
AIAA SCITECH 2026 Forum. 2026. p. 1966.
Status
Peer-reviewed
publications
ML Thermal Turbomachinery Film Cooling Aeronautics Aerospace
Nicolas Fougere
Authors
Nicolas Fougere (he/him)
Senior Portfolio Manager

Nicolas Fougere is a technology leader with experience helping global industrial organizations accelerate innovation through digital engineering and transformation. As a Senior Portfolio Manager at Dassault Systèmes, he works with manufacturers and technology leaders to develop strategies around virtual twins, simulation, systems engineering, and AI.

Before joining the private sector, Nicolas contributed to scientific research supporting NASA and the European Space Agency’s Rosetta mission and authored numerous peer-reviewed publications. He holds advanced engineering and scientific degrees from the University of Michigan.

Nicolas is passionate about connecting technology, business strategy, and customer success to help organizations solve complex challenges, build high-performing teams, and create lasting business value.