Wind and Structures

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Prediction of aerodynamic coefficients of streamlined bridge decks using artificial neural network based on CFD dataset

  • Severin Tinmitonde (National Engineering Research Center of High-speed Railway Construction Technology, Central South University) ;
  • Xuhui He (National Engineering Research Center of High-speed Railway Construction Technology, Central South University) ;
  • Lei Yan (National Engineering Research Center of High-speed Railway Construction Technology, Central South University) ;
  • Cunming Ma (Department of Bridge Engineering, Southwest Jiaotong University) ;
  • Haizhu Xiao (Major Bridge Reconnaissance & Design Institute Co., Ltd.)
  • Received : 2022.05.23
  • Accepted : 2022.12.08
  • Published : 2023.06.25
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Abstract

Aerodynamic force coefficients are generally obtained from traditional wind tunnel tests or computational fluid dynamics (CFD). Unfortunately, the techniques mentioned above can sometimes be cumbersome because of the cost involved, such as the computational cost and the use of heavy equipment, to name only two examples. This study proposed to build a deep neural network model to predict the aerodynamic force coefficients based on data collected from CFD simulations to overcome these drawbacks. Therefore, a series of CFD simulations were conducted using different geometric parameters to obtain the aerodynamic force coefficients, validated with wind tunnel tests. The results obtained from CFD simulations were used to create a dataset to train a multilayer perceptron artificial neural network (ANN) model. The models were obtained using three optimization algorithms: scaled conjugate gradient (SCG), Bayesian regularization (BR), and Levenberg-Marquardt algorithms (LM). Furthermore, the performance of each neural network was verified using two performance metrics, including the mean square error and the R-squared coefficient of determination. Finally, the ANN model proved to be highly accurate in predicting the force coefficients of similar bridge sections, thus circumventing the computational burden associated with CFD simulation and the cost of traditional wind tunnel tests.

Keywords

Acknowledgement

This work was supported by the National Natural Science Foundation of China [Grant Numbers 52178516, 51925808], the Science and Technology Research and Development Program of China Railway Group Limited [Grant Number 2021-Special-04-2] and the Tencent Foundation or XPLORER PRIZE. The authors are grateful for resources from the High-Performance Computing Center of the Chinese Academy of Sciences (HPC-CAS-Beijing).

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