AI-Based Drag Reduction of a High-Speed Train Using Distributed Jets

This work studies experimentally the aerodynamic drag reduction (DR) of a high-speed maglev train (HSMT) model based on artificial intelligence (AI) control, following our successful campaign on the DR of Ahmed bodies. A highly streamlined 3-car HSMT model is used, and the Reynolds number Re is 4.0 × 105 based on the square root of the model cross-section. The aerodynamic drag of the model is measured using two force balances. More than 90 steady jets are deployed on the tail car, in which the dependence of DR on their blowing angles and blowing ratios is documented for each jet. The individual jets produce a maximum DR of 7% and a maximum net power saving of 4%. Seven spatially distributed jets are selected and an AI control system (Zhang et al. in Artificial intelligence control of a low-drag Ahmed body using distributed jet arrays. J Fluid Mech 963 [6]) is deployed to find the best strategies to combine the seven jets in terms of their blowing ratios. Both DR and control power input are incorporated in the cost function. The AI control discovers forcing that creates a DR of 10%. Furthermore, the net power saving reaches about 5% given a DR of 6%.