Extrapolative, progressive machine learning for turbulence modeling
Abstract
The design of revolutionary aero vehicle concepts requires computational tools with reasonable cost. Since resolving all turbulent scales in a simulation at high Reynolds numbers is not practical, turbulence modeling is a necessity. The research will make use of modern machine learning tools and high-fidelity numerical simulation data for turbulence modeling. The proposal differs from the current research in that it emphasizes extrapolation, which is achieved through progressive machine learning.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 29, 2024
- Source ID
- FA95502310272
Entities
People
- Xiang I. A. Yang
Organizations
- Air Force Office of Scientific Research
- Pennsylvania State University
- United States Air Force