Toward a Practical Method for Hypersonic Transition Prediction Based on Stability Correlations

Abstract

An accurate boundary-layer transition prediction method integrated with computational fluid dynamics (CFD) solvers is pursued for hypersonic boundary-layer flows over slender hypersonic vehicles at flight conditions. The geometry and flow conditions are selected to match relevant trajectory locations from the ascent phase of the HIFiRE-1 flight experiment, namely, a 7 deg half-angle cone with 2.5 mm nose radius, freestream Mach numbers in the range of 3.8–5.5, and freestream unit Reynolds numbers in the range of . Earlier research had shown that the onset of transition during the Hypersonic International Flight Research Experimentation 1 (HIFiRE-1) flight experiment correlated with an amplification factor of for the planar Mack modes. However, to incorporate the -factor correlations into a CFD code, we investigate surrogate models for disturbance amplification that avoids the direct computation of stability characteristics. The results demonstrate that the application of the commonly used approach, which is based on a database of stability characteristics for locally similar profiles, leads to large, unacceptable errors in the predictions of amplification factors. We propose and demonstrate an alternate approach that employs the stability computations for a canonical set of blunt cone configurations to train a convolutional neural network model that is shown to provide substantially improved transition predictions. The excellent performance of the neural network model is also confirmed for cone configurations with nose radius and half-angle values outside of those used to build the database. Finally, the convolutional neural network model is found to outperform the linear stability calculations for underresolved basic states.