Aerodynamic drag optimization of a high-speed train. Issue 204 (September 2020)

• Record Type: Journal Article
• Authors: Muñoz-Paniagua, J.; García, J.
• Is Part Of: Journal of wind engineering and industrial aerodynamics. Issue 204(2020)
• Publication Date: 2020-09
• Subjects: Shape optimization -- High-speed train -- Genetic algorithm -- Surrogate model -- SAS; Wind-pressure -- Periodicals; Buildings -- Aerodynamics -- Periodicals; Pression du vent -- Périodiques; Constructions -- Aérodynamique -- Périodiques; Buildings -- Aerodynamics; Wind-pressure; Periodicals
• DOI: 10.1016/j.jweia.2020.104215
• Languages: English
• ISSNs: 0167-6105

Abstract

Abstract: This paper considers the optimization of the nose shape of a high-speed train to minimize the drag coefficient in zero-yaw-angle conditions. The optimization is performed using genetic algorithms (GA) and is based on the Aerodynamic Train Model (ATM) as the reference geometry. Since the GA requires the parameterization of each optimal candidate, 25 design variables are used to define the shape of the train nose and, in particular, to reproduce that of the ATM. The computational cost associated to the GA is reduced by introducing a surrogate model in the optimization workflow so that it evaluates each optimal candidate in a more efficient way. This surrogate model is built from a large set of simulations defined in a Latin Hypercube Sampling design of experiments, and its accuracy is improved each optimization iteration (online optimization). In this paper we detail the whole optimization process, ending with an extense analysis of results, both statistical (analysis of variance (ANOVA) to identify the most significant variables and clustering using Self-Organized Maps (SOM)), and aerodynamic. The latter is performed running two accurate simulations using Scale-Adaptive Simulation (SAS) turbulence model. The optimal design reduces the drag coefficient a 32.5% of the reference geometry. Highlights: Genetic algorithm is used as the optimization method to minimize the drag coefficient of a high-speed train under front wind. A RBF network improves the performance of the GA. The simulations required to build it yield insight into the design space.. An ANOVA test and Self-Organizing Maps (SOM) are included to analyze the influence of each design variable. Nose width at the hood region results as the most significant design variable. SAS turbulence model is used for a more accurate aerodynamic study of the optimum solution, compared it with the reference design.