Car drag coefficient prediction using long–short term memory neural network and LASSO

• Published in: Measurement : journal of the International Measurement Confederation Vol. 225; p. 113982
• Main Authors: Shen, Shengrong, Han, Tian, Pang, Jiachen
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.02.2024
• Subjects: Computer-aided design; Deep learning; Drag coefficient; Lasso regression; LSTM; Deep learning; LSTM; Computer-aided design; Lasso regression; Drag coefficient
• ISSN: 0263-2241
• DOI: 10.1016/j.measurement.2023.113982

•A deep learning neural network-based drag coefficient analysis method is proposed to provide faster results than the traditional CFD analysis method.•Proposed a unique car side profile feature extraction method.•Correct the overfitting of the LSTM model by introducing Lasso regression.•The validity of the method is verified by realistic car body side profiles.•Improved accuracy compared to the car drag coefficient prediction study by E. Gunpinar et al. Although the methods of car's shape designing have undergone significant changes, the drag coefficient has consistently act as a crucial indicator of a car's fuel efficiency and environmental impact. Nowadays, the analysis of the drag coefficient for car bodies relies heavily on CAE, which is a time-consuming process. In this paper, a Long Short-Term Memory (LSTM) prediction model which combines Deep Learning and CAD has been established to estimate the drag coefficient. Feature extraction of the Car Side Silhouettes is carried out based on Bezier curve car body contours, and Lasso regression is employed to solve overfitting problems caused by the insufficient amount of data. The prediction average deviation is 1.1% for drag coefficient during 0.28 to 0.35, and 5% for a real car profile. The results showed that the method provides better performance than the existing method, and improves accuracy of prediction as well.