Sonic boom prediction methods using feature-based adaptation of unstructured meshes

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 t o 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.

Grantor dc:publisher

University of Tennessee at Chattanooga

Author dc:creator

• Varghese, Jacob Chackasseril

Contributors dc:contributor

• Karman, Steve L., Jr.
• Swafford, Timothy W.; Hyams, Daniel G.
• College of Engineering and Computer Science

• Computer programs

Licence

• https://rightsstatements.org/page/InC/1.0/?language=en

Language dc:language

English, eng

Repository record dc:identifier

https://scholar.utc.edu/theses/221

OAI identifier oai:identifier

oai:scholar.utc.edu:theses-1361