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

This research investigates aerodynamic feasibility of high-speed evacuated tube transport using an integrated CFD-machine learning framework. The pod geometry is parameterized using composite cubic Bezier curves with seven design variables. Latin Hypercube Sampling generates 200 configurations across subsonic and supersonic regimes, evaluated using two-dimensional CFD analysis. A staged converging-diverging tube concept is proposed for supersonic operation.