Rapid Prediction of Low-Boom and Aerodynamic Performance of Supersonic Transport Aircraft Using Panel Methods

• Main Author: Giblette, Ted N.
• Format: Others
• Published: DigitalCommons@USU 2019
• Subjects: panel methods; sonic boom; class-shape transformations; PANAIR; supersonic; PyLdB; Mechanical Engineering
• Online Access: https://digitalcommons.usu.edu/etd/7603; https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=8736&context=etd

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 work presented in this thesis was done over the first year of the grant. The Aerolab team was specifically tasked with developing a set of tools for rapidly predicting the sonic boom loudness of supersonic aircraft. Specifically, this work included an assessment of the existing analysis tools available followed by the planning, development, and testing of a framework of tools for performing the needed calculations. Results of the framework were compared against high fidelity solutions available from the 2017 AIAA Sonic Boom Prediction Workshop. These comparisons revealed that panel methods perform well for simple geometries. However, localized errors appear when modeling more complex geometries that reduce the accuracy of the predicted sonic boom loudness. It was found that these localized errors were a consequence of the inherent assumptions built into panel methods. Consequently, in future work, it may be necessary to develop techniques for combining the results of panel methods with higher fidelity methods or to revisit the panel method formulation.