Multi-objective aerodynamic optimization of high-speed train heads based on the PDE parametric modeling

Abstract With the increasing running speed, aerodynamic issues of high-speed trains are being raised and impact stability energy efficiency. The optimization design head shape is significantly important in improving performance trains. Existing methods limited by parametric modeling train heads which unable to accurately completely parameterize both global shapes local details. Due this reason, they cannot optimize heads. In order tackle problem, we propose a novel multi-objective method based on partial differential equation (PDE) modeling. method, half parameterized with 17 PDE surface patches describe details keep smooth. We take drag lift as objectives; divide process into two stages: optimization; develop methods, respectively. first stage, non-dominated sorting genetic algorithm (NSGA-II) adopted obtain framework an optimized shape. second Latin hypercube sampling (LHS) applied determined improve effectiveness our proposed demonstrated better achieved from solutions stages comparison original head.