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

Abstract

This study investigates the aerodynamic feasibility of high-speed evacuated tube transport (ETT) using an integrated CFD-machine learning framework for pod geometry optimization and a staged converging-diverging (CD) tube concept for supersonic operation. The pod geometry is parameterized using composite cubic Bezier curves with seven design variables. Latin Hypercube Sampling is used to generate 200 configurations across subsonic and supersonic regimes, which are evaluated using two-dimensional axisymmetric ANSYS Fluent simulations validated against published compressible-flow data. The resulting CFD dataset is used to train surrogate models for rapid drag prediction. XGBoost provides the best overall performance, achieving a test R^2 of 0.9524, and is coupled with Differential Evolution and Particle Swarm Optimization to identify low-drag geometries that are subsequently verified using CFD. The study further examines a staged CD tube architecture combining controlled area variation with gradual pressure reduction to mitigate choking during supersonic acceleration. In the idealized axisymmetric simulations, the proposed strategy enables transition from atmospheric entry at Mach 1.6 to near-vacuum cruise at Mach 2.5 and a blockage ratio of 0.50 without forming a choked annular flow region. Total drag decreases from 68,913.7 N under atmospheric supersonic conditions to 120.5 N in the final near-vacuum stage, associated with a transition from wave-drag-dominated to viscous-shear-dominated resistance. The results demonstrate the potential of combined pod-shape optimization and staged pressure-area management for reducing aerodynamic resistance in supersonic ETT systems.