Aerodynamic drag accounts for about 60% of heavy truck energy expenditure at highway speeds, 20–50% for passenger vehicles and 75–90% for high-speed trains (HSTs). Reducing this drag is therefore crucially important for improving the energy efficiency of terrestrial vehicles, lowering their operating costs and mitigating greenhouse gas emissions. Over recent years, research on drag reduction (DR) has made a great progress, driven by developments in experimental diagnostics, high-fidelity numerical simulation and optimization based on machine learning (ML) or artificial intelligence (AI). This paper reviews recent advances in flow control and DR strategies for terrestrial vehicles, focusing on three key avenues, i.e., (1) understanding flow physics, (2) developing passive, active and adaptive hybrid flow control (PFC, AFC, AHC) techniques and (3) leveraging AI-based virtual engineering technology for aerodynamic shape optimization. Comparative analysis is provided for automotive vehicles (with the Ahmed body as a canonical model) and HSTs, highlighting their similarities, coherent structures and control mechanisms. Their shared flow physics points to the fact that proven DR strategies for, e.g., Ahmed bodies, could be extended for HSTs. The paper also discusses trade-offs between energy efficiency, adaptability, implementation complexity and operational reliability and outlines future challenges in scaling laboratory-proven methods to robust and real-world applications. By uniting insights from bluff- and streamlined-body aerodynamics, this review aims to provide a guide for the future research and the development of next-generation terrestrial vehicles that may achieve both performance and sustainability targets.
Zhou et al. (Fri,) studied this question.