Data and Machine Learning Enabled Wall Modeling for LES of Transitional, Non-Equilibrium Turbulent Boundary Layers
Abstract
The proposed effort aims to significantly enhance the accuracy, efficiency, and robustness of wall-modeled Large Eddy Simulations. W""e propose to develop anintegrated approach to wall modeling, bridging first-principles models for the initial laminar with the post"#NAME?ck of analytical models motivate the use of machine learning tools. These will be used for model training to predict the instability mechanisms and local skin friction. The approach will be tested on available data including applications to boundary layers and flow over NACA 65 airfoil.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Sep 29, 2017
- Source ID
- N000141712937
Entities
People
- Charles Meneveau
Organizations
- Johns Hopkins University
- Office of Naval Research
- United States Navy