Integrated Model for Predicting Demand of Supersonic Transports Under Low-Boom Constraint
Commercial supersonic aircraft could return between 2035 and 2040 due to the desire for fast travel and advancements in technology that could reduce the cost of supersonic transport. Nevertheless, several challenges remain to make supersonic travel a reality. This paper presents an integrated model that ties the aircraft preliminary design process, the economics of aircraft development and operations, and passenger preferences to estimate worldwide demand for subsonic overland, Mach cutoff, and low-boom aircraft design concepts. This comprehensive and iterative methodology takes into account the uncertainty of market demand for supersonic travel and the complexities of supersonic network operations. Based on the study of four conceptual supersonic designs, we found that low-boom aircraft could attract 28% more demand worldwide compared to an optimized Mach cutoff design because of the additional time-saving benefits on overland routes. The results of this work offer valuable guidance to aircraft designers by explicitly tying the technical parameters of a design and the potential market demand. In addition, the integrated model developed may streamline the initial design development cycle by quantifying fleet size as part of an integrated modeling process.
Authors
- Nicolas Hinze
- Ty Marien
- Karl Geiselhart
- Sam Dollyhigh
- Jonathan Seidel (ORCID: https://orcid.org/0000-0003-3670-0300)
- Li Wu (ORCID: https://orcid.org/0000-0002-4118-7970)
- Zhou Wang (ORCID: https://orcid.org/0000-0001-8159-7387)
- Antonio Trani
Institutions
- Langley Research Center (US)
- Glenn Research Center (US)
- Analytical Mechanics Associates (United States) (US)
- Virginia Tech (US)
Publication Details
- Journal
- Journal of Air Transportation
- Published
- 2025-03-11
- DOI
- https://doi.org/10.2514/1.d0442
- Citations
- 1
- Primary Topic
- Air Traffic Management and Optimization
- Type
- article
- Field-Weighted Citation Impact
- 4.99
Funders
- Langley Research Center