Double Cone Flow Field Reconstruction Between Mach 4 and 12 Using Machine Learning Techniques
Abstract
Analysis of hypersonic systems poses a challenge due, in part, to the physical phenomena unique to the hypersonic flight regime. One needs to consider as many of these phenomena as possible early in vehicle design. Reduced order models can provide insight into these complex phenomena at a low cost by leveraging previous results. Toward this end, we investigate the utility of machine learning models for predicting the pressure field in hypersonic flow. For this effort, we simulate compressible flow over a parameterized double cone model at hypersonic speeds using a steady-state RANS solver, Kestrel. The computed pressure fields are used to train two neural network NN models, the U Net and the Multiscale Network, as well as two meta-models: K-Nearest Neighbors and Regression Kriging. The NN models are designed to reconstruct flow field relationships using distinct methodologies: the U-Net utilizes auto-encoding while the Multiscale Network utilizes a sequential refinement scheme. All models predict the pressure values on a uniform Cartesian grid of much smaller resolution than the unstructured mesh required for CFD simulation. The accuracy, computational complexity, and versatility of the NN are compared against the meta models. Additionally, the ability for each method to accurately predict shock interactions or impingement with downstream vehicle geometry is examined. Such closed-form machine learning ML models have advantages over conventional CFD solutions as they do not require any meshing of the computational domain and can quickly generate flow field predictions - on the order of seconds compared to hours. Our analysis shows the NN models to have robust predictive performance over the generated double cone dataset. Additionally, the NN models easily apply to arbitrary geometries that the existing geometric parameterization cannot describe.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 01, 2022
- Accession Number
- AD1201832
Entities
People
- Benjamin J. Grier
- C. C. Fischer
- Jose A. Camberos
- Logan P. Riley
- Ramana V. Grandhi
- Trevor A. Toros
Organizations
- Air Force Institute of Technology
- Air Force Research Laboratory
- Ohio State University