On the subsonic and low transonic aerodynamic performance of the land speed record car, Bloodhound LSR. (July 2022)

• Record Type: Journal Article
• Authors: Evans, Ben; Townsend, Jack; Hassan, Oubay; Morgan, Kenneth; Ayers, Ron; Chapman, Mark; Green, Andy
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 236:Number 9(2022)
• Page Start: 1895
• Page End: 1921
• Publication Date: 2022-07
• Subjects: Bloodhound land speed record -- land speed record -- computational fluid dynamics -- Reynolds-averaged Navier–stokes -- transonic -- ground effect; Aeronautics -- Periodicals; Astronautics -- Periodicals; Airplanes -- Design and construction -- Periodicals; Aerospace industries -- Periodicals; 629.1
• DOI: 10.1177/09544100211046159
• Languages: English
• ISSNs: 0954-4100

Abstract

The land speed record vehicle, Bloodhound, undertook testing at subsonic and low transonic speeds (up to Mach 0.8) at Hakskeen Pan, South Africa, during October and November of 2019. A decade of CFD-led aerodynamic design had been undertaken to produce a vehicle with the aim of minimised Mach number aerodynamic dependencies and minimised overall drag. This paper sets out and explains the measured pressure distributions with a focus on the highest speed run of Bloodhound up to a peak speed of 628 mile/h. It compares the measured aerodynamic performance with the various CFD model predictions used throughout the design process showing that, whilst localised discrepancies between CFD model and real behaviour exist, overall the Reynolds-averaged Navier–Stokes (RANS)-based CFD tools used to design the car did result in sufficiently accurate aerodynamic data to predict the overall vehicle performance to a high degree of accuracy. The work outlined in this paper, and the conclusions and recommendations drawn, form the basis for a future record attempt and the understanding of what will be required in principle to extend the World Land Speed Record to 1000 mile/h. It also provides guidance on how to effectively make use of RANS-based CFD modelling predictions for other complex, ground-interacting high-speed applications.