Sonic Boom Prediction Methods Using Feature-Based Adaptation of Unstructured Meshes
A Thesis Presented for the Master of Science in Computational Engineering Degree, The University of Tennessee at Chattanooga
Jacob Chackasseril Varghese, December 2009
This study examines the improvement of near-field sonic boom prediction of an inviscid supersonic configuration using two grid generation refinement procedures. The first method uses P_HUGG, a parallel hierarchical Cartesian mesh generation algorithm to generate a volume mesh, with the solution-based mesh adaptation capability of P_HUGG being exploited. The mesh quality was improved using P_OPT, a parallel optimization-based mesh-smoothing program. In the second method, the commercially-available software POINTWISE™ is used for volume mesh generation. Then, P_REFINE, a parallel subdivision refinement code, is used to adaptively refine the mesh. The effectiveness of capturing far field shocks was examined using TENASI, an unstructured flow solver developed at the SimCenter at the University of Tennessee at Chattanooga. The grids are adapted to high pressure gradient using SPACING, a program that computes the desired spacing at all points in the mesh. Results from both methods are compared with wind-tunnel based experimental data.