Framework for ML in Turbulence Modeling | Aero Track | 10:50 am

This presentation is a collection of thoughts and a reflection on the current state of approaches to Machine Learning, Digital Thread, Optimization, and related technologies in structures at Boeing Commercial Airplanes. This is a one-person perspective on what is currently being done in practice and thoughts on how to improve the current practice.

Philippe Spalart

Philippe Spalart studied Mathematics and Engineering in Paris, and obtained an Aerospace PhD at Stanford/NASA-Ames in 1982.

Still at Ames, he conducted Direct Numerical Simulations of transitional and turbulent boundary layers. Moving to Boeing in 1990, he created the Spalart-Allmaras one-equation Reynolds-Averaged Navier-Stokes turbulence model. He wrote a review and co-holds a patent on airplane trailing vortices. In 1997 he proposed the Detached-Eddy Simulation approach, blending RANS and Large-Eddy Simulation to address separated flows at high Reynolds numbers with a manageable cost.

He received the AIAA Fluid Dynamics Award in 2006, became a Boeing Senior Technical Fellow in 2007, was elected to the National Academy of Engineering in 2017, and had the AIAA Reed Award for 2019. His papers have been cited 48,000 times. Recent work includes refinements to the SA model and DES, computational aeroacoustics, theories for aerodynamics and turbulence, and the design of research experiments. Philippe retired from Boeing in 2020.