Aerodynamic multi-objective optimization on train nose shape using feedforward neural network and sample expansion strategy-Reference-Cited by-同舟云学术

Aerodynamic multi-objective optimization on train nose shape using feedforward neural network and sample expansion strategy

Published:2023-06-22 Issue:1 Volume:17 Page:
ISSN:1994-2060
Container-title:Engineering Applications of Computational Fluid Mechanics
language:en
Short-container-title:Engineering Applications of Computational Fluid Mechanics

Author:

Dai Zhiyuan¹,Li Tian¹²,Xiang Ze-Rui²³,Zhang Weihua¹,Zhang Jiye¹

Affiliation:

1. State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu, People’s Republic of China

2. School of Design, Southwest Jiaotong University, Chengdu, People’s Republic of China

3. Institute of Design and Research for Man-Machine-Environment Engineering System, Southwest Jiaotong University, Chengdu, People’s Republic of China

Funder

Sichuan Science and Technology Program

National Natural Science Foundation of China

Project of State Key Laboratory of Traction Power

New Interdisciplinary Cultivation Fund Program of Southwest Jiaotong University

Publisher

Informa UK Limited

Subject

Modeling and Simulation,General Computer Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/19942060.2023.2226187

Reference29 articles.

1. Research Progress of Aerodynamic Multi-Objective Optimization on High-Speed Train Nose Shape

2. An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints

3. Fluent Inc. (2021). FLUENT user's guide . https://ansyshelp.ansys.com

4. Numerical simulation of the aerodynamic characteristics of double unit train

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