CNN-based flow field prediction for bus aerodynamics analysis

Abstract

The aim of this article is to evaluate the ability of a convolutional neural network (CNN) to predict velocity and pressure aerodynamic fields in heavy vehicles. For training and testing the developed CNN, various CFD simulations of three different vehicle geometries have been conducted, considering the RANS-based k-ω SST turbulent model. Two geometries correspond to the SC7 and SC5 coach models of the bus manufacturer SUNSUNDEGUI and the third one corresponds to Ahmed body. By generating different variants of these three geometries, a large number of representations of the velocity and pressure fields are obtained that will be used to train, verify, and evaluate the convolutional neural network. To improve the accuracy of the CNN, the field representations obtained are discretized as a function of the expected velocity gradient, so that in the areas where there is a greater variation in velocity, the corresponding neuron is smaller. The results show good agreement between numerical results and CNN predictions, being the CNN able to accurately represent the velocity and pressure fields with very low errors. Additionally, a substantial improvement in the computational time needed for each simulation is appreciated, reducing it by four orders of magnitude.

Identification of Myocardial Infarction (MI) Probability from Imbalanced Medical Survey Data: An Artificial Neural Network (ANN) with Explainable AI (XAI) Insights

“…This method has enabled the facts to distinguish the various contributions of different domains to the model's predictions, revealing insight into which domains have the most influence on the model's performance. Due 10/ 35 to the imbalanced class distribution, assessing recall for both target classes is essential for a more meaningful evaluation of performance, as only accuracy can be misleading in this case. Determining the true positive rate as well as the true negative rate is essential to extract more important and informative insights about the input domains which is a primary emphasis in this study.…”

“…The interpretability and explainability of artificial intelligence (AI) models are critical in the medical arena since healthcare practitioners demand insights into the model’s decision-making process 32,33 . Deep learning models, particularly neural networks, have been criticized for their “black-box” nature, which makes it difficult to grasp the logic behind the predictions made by these approaches 34,35,36,37,38,39,40 . This study intends to overcome these important issues by proposing reliable, explainable, and thus more transparent methods for exploring cutting-edge deep-learning techniques for medical research and practice.…”