CFD-Machine Learning Driven Pod Optimization and Staged Pressure-Area Management for Supersonic Evacuated Tube Transport

This paper presents a CFD-machine learning framework for aerodynamic optimization of high-speed evacuated tube transport systems. The pod geometry is parameterized using composite cubic Bezier curves with seven design variables, and 200 configurations are generated via Latin Hypercube Sampling across subsonic and supersonic regimes. A staged converging-diverging tube concept is introduced to enable supersonic operation.