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Rapid Prediction Of Low-Boom And Aerodynamic Performance Of Supersonic Transport Aircraft Using Panel Methods, Ted N. Giblette

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The Utah State University Aerolab developed and tested a set of tools for rapid prediction of the loudness of a sonic boom generated by supersonic transport aircraft. This work supported a larger effort led by Texas A&M to investigate the use of adaptive aerostructures in lowering sonic boom loudness at off design conditions. Successful completion of this effort will improve the feasibility of supersonic commercial transport over land.

Funding was provided by a NASA University Leadership Initiative grant to several universities, including Utah State University, as well as industry partners to complete this work over a five year period. The …

A Multi-Fidelity Prediction Of Aerodynamic And Sonic Boom Characteristics Of The Jaxa Wing Body, Forrest L. Carpenter, Paul G. A. Cizmas, Christian R. Bolander, Ted N. Giblette, Doug F. Hunsaker

Mechanical and Aerospace Engineering Student Publications and Presentations

This paper presents a detailed comparison between the linear panel solver PANAIR A502 and the in-house Navier–Stokes solver UNS3D for a supersonic low-boom geometry. The high-fidelity flow solver was used to predict both the inviscid and laminar flow about the aircraft geometry. The JAXA wing body was selected as the supersonic low-boom geometry for this study. A comparison of the undertrack near-field pressure signatures showed good agreement between the three levels of model fidelity along the first 0.8L of the signature. Large oscillations in the PANAIR results were observed. The PANAIR discrepancies were traced back to violations of the …

A Procedure For The Calculation Of The Perceived Loudness Of Sonic Booms, Christian R. Bolander, Douglas F. Hunsaker, Hao Shen, Forrest L. Carpenter

Mechanical and Aerospace Engineering Student Publications and Presentations

Implementing a method to calculate the human ear’s perceived loudness of a sonic boom requires consulting scattered literature with varying amounts of detail. This work describes a comprehensive implementation of Stevens’ Mark VII in Python, called PyLdB. References to literary works are included in enough detail so that the reader could use this work as a guide to implement the Mark VII algorithm. The details behind the mathematics of the Mark VII algorithm are included and PyLdB is used to calculate the perceived loudness of an example pressure signature. PyLdB is benchmarked against a widely used and validated code by …