Data-driven RANS Model Augmentations using Learning and Inference assisted by Feature-space Engineering

This work presents guiding principles and techniques to enable inference of robust and generalizable augmentations for RANS models from sparse experimental data in a model-consistent manner such that predictive accuracy is improved across a wide range of physical configurations (geometries/boundary conditions) without any added spurious behavior. The importance of constructing bounded features (inputs to augmentation function) via physics-based non-dimensionalization in appropriate functional forms while maintaining a balance between parsimony in the set of features and a one-to-one features-to-augmentation mapping is underlined. Particular emphasis is laid on different techniques, implementations and trade-offs associated with localized learning. The framework is tested by creating a data-driven bypass transition model which is trained on two flat plate cases and shows consistent improvements across different flat plate, turbine cascade and compressor cascade cases with varying freestream turbulence intensities, Reynolds numbers and pressure gradients.