The Hyperloop system is a new concept that allows a train to travel through a near-vacuum tunnel at transonic speeds. Aerodynamic drag is one of the most important factors in analyzing such systems. The blockage ratio (BR), pod speed/length, tube pressure, and temperature affect the aerodynamic drag, but the specific relationships between the drag and these parameters have not yet been comprehensively examined. In this study, we investigated the flow phenomena of a Hyperloop system, focusing on the effects of changes in the above parameters. Two-dimensional axisymmetric simulations were performed in a large parameter space covering various BR values (0.25, 0.36), pod lengths (10.75–86 m), pod speeds (50–350 m/s), tube pressures (~100–1000 Pa), and tube temperatures (275–325 K). As BR increased, the pressure drag was significantly affected. This is because of the smaller critical Mach number for a larger BR. As the pod length increased, the total drag and pressure drag did not change significantly, but there was a considerable influence on the friction drag. As the pod speed increased, strong shock waves occurred near the end of the pod. At this point, the flows around the pod were severely choked at both BR values, and the ratio of the pressure drag to the total drag converged to its saturation level. At tube pressures above 500 Pa, the friction drag increased significantly under the rapidly increased turbulence intensity near the pod surface. High tube temperatures increase the speed of sound, and this reduces the Mach number for the same pod speed, consequently delaying the onset of choking and reducing the aerodynamic drag. The results presented in this study are applicable to the fundamental design of the proposed Hyperloop system.

The design of multiscale materials and products has necessitated an inductive and robust design approach to ensure satisfying the performance goals for complex engineering problems. Inductive design exploration method is a performance-driven design approach that explores feasible design spaces while considering the effect of uncertainty that leads to performance variability. However, the existing design method suffers from high computational costs for pre-defined sample data, which sacrifices the accuracy of solution spaces. In this study, we present an improved implementation of the inductive design exploration method by applying the active learning algorithm that is mainly used in machine learning techniques. The purpose of this study is to minimize the sampling effort while maintaining reasonable accuracy in the exploration of design spaces, thereby alleviating computational burden. The capabilities of the improved method are highlighted and demonstrated via a design problem of the blast resistant sandwich panel.