Academic Journal of Science and Technology ISSN: 2771-3032 | Vol. 14, No. 3, 2025 217 Data‐Driven Aerodynamic Design and Optimization of Vehicles Juncheng Peng* Beijing Xuanwu Foreign Language Experimental School, Beijing, 100055, China *Corresponding author’s e-mail: peng20060909@outlook.com Abstract: This paper investigates the aerodynamic design and optimization of vehicles based on data-driven methods, aiming to enhance the energy efficiency and performance of transportation vehicles. The research integrates machine learning algorithms, big data analytics, and Computational Fluid Dynamics (CFD) simulation techniques to establish an efficient aerodynamic optimization model. By analyzing massive design parameters and flow field data, innovative design solutions are proposed and validated for practical application. This study offers new approaches to traditional design methods, significantly shortening the design cycle, reducing research and development costs, and laying a technological foundation for the future development of intelligent transportation vehicles. The findings are of great significance for promoting green transportation and achieving energy conservation and emission reduction, providing both theoretical support and practical guidance for sustainable transportation development. Keywords: Data-Driven; Transportation Vehicles; Aerodynamic Optimization; Computational Fluid Dynamics; Drag Coefficient. 1. Introduction With the rapid development of the transportation sector, the aerodynamic performance of vehicles has become increasingly important for energy efficiency, speed, and environmental impact. However, traditional aerodynamic design methods primarily rely on empirical formulas and experimental techniques, which face challenges such as long development cycles, high costs, and limited optimization effectiveness. Additionally, traditional methods struggle to comprehensively address the coupling effects of multi- dimensional design parameters when dealing with complex nonlinear flow field problems. To address these challenges, this paper explores a data-driven aerodynamic optimization approach, building on previous research and combining big data and machine learning technologies. By efficiently analyzing and utilizing multi-source flow field data, the paper aims to optimize the design process, improve prediction accuracy, and uncover new design potentials, thereby providing innovative solutions for enhancing vehicle performance and promoting green development in the transportation sector. 2. Research Status 2.1. International Research Status In recent years, significant progress has been made abroad in data-driven design methods and their application in the aerodynamic optimization of transportation vehicles. For example, a research team at Stanford University developed a deep learning-based aerodynamic shape optimization framework that rapidly predicts the aerodynamic performance of different geometries using neural networks, significantly reducing computational costs [1]. BMW, in collaboration with the Technical University of Munich, proposed a design method combining Generative Adversarial Networks (GANs), enabling automated optimization of automotive body shapes. This method was validated in wind tunnel tests, demonstrating significant reductions in aerodynamic drag [2]. Additionally, researchers at the University of Tokyo in Japan applied Support Vector Machines (SVM) and multi- objective optimization algorithms to optimize the nose shape of high-speed trains. This not only improved the efficiency of streamlined design but also reduced wind noise and energy consumption [3]. These studies show that international scholars have made significant achievements in utilizing advanced algorithms and integrating data-driven design technologies, providing new insights for traditional aerodynamic optimization methods. 2.2. Domestic Research Status In recent years, with the growing demand for aerodynamic design in transportation vehicles, domestic research in data- driven design has gradually emerged and made significant progress. Several universities, research institutions, and enterprises have actively explored the application of advanced data-driven methods for aerodynamic optimization, injecting new momentum into industry development. For example, the research team at Tsinghua University combined big data and deep learning technologies to develop a multi- objective optimization-based vehicle aerodynamic performance prediction model. This model significantly improved design efficiency, with related research published in the Journal of Mechanics [4]. Furthermore, Tongji University, in collaboration with a domestic automotive manufacturer, applied a combination of CFD simulations and Genetic Algorithms (GA) to optimize the exterior shape of electric vehicle bodies, successfully reducing the drag coefficient. This method was validated in multiple practical cases [5]. CRRC Group, in cooperation with universities, applied data-driven technologies to high- speed train nose design, using a combination of Support Vector Regression (SVR) and neural network models to optimize the aerodynamic performance of high-speed trains and achieve dual reductions in energy consumption and noise. 218 At the same time, government support for intelligent transportation and energy conservation policies has provided strong backing for data-driven aerodynamic design. The National Natural Science Foundation has funded several related projects, such as “Aerodynamic Optimization of High- Speed Trains Based on Big Data,” further promoting the integration of theory and practice [6]. Despite starting later than abroad, the domestic field has great potential for development under the dual drive of scientific research and industrial application, laying a solid foundation for more efficient and intelligent design in the future. 3. Review Objectives and Methods The primary objective of this paper is to systematically review the research status of data-driven aerodynamic design and optimization of transportation vehicles, analyze the key technologies and application progress, and explore future development trends. Specifically, this paper aims to achieve the following objectives: (1) Summarize the Research Status: To comprehensively review the research achievements in the field of data-driven aerodynamic design for transportation vehicles, both domestically and internationally, and analyze the strengths and limitations of existing methods. (2) Explore Optimization Methods: To develop optimization methods based on machine learning and big data, improving the prediction accuracy of key aerodynamic parameters such as drag coefficient and overcoming the limitations of traditional design methods. (3) Propose Optimization Design Solutions: To propose practical aerodynamic optimization design solutions for transportation vehicles by combining flow field data and multi-objective optimization algorithms. (4) Experimental Validation: To validate the reliability and effectiveness of the optimization solutions through numerical simulations and experimental tests, providing theoretical support and practical references for the development of this field. Through this review and analysis, this paper aims to provide guidance for future research on data-driven vehicle design, offering innovative solutions to meet the practical needs of the industry. 4. Review Content 4.1. Data Collection and Processing In data-driven aerodynamic design for transportation vehicles, data collection and processing are critical foundational steps. Currently, internationally, a combination of CFD simulations, wind tunnel experiments, and real-world vehicle testing is widely used to obtain aerodynamic data. These data cover input parameters such as the vehicle’s geometry, size, speed, and wind direction, as well as output results like drag coefficient, lift coefficient, and pressure distribution. Specifically, CFD simulation technology can generate large amounts of high-resolution flow field data, providing precise predictions for complex designs. Wind tunnel experiments validate the effectiveness of design solutions by controlling experimental environments, while real-world vehicle testing provides final performance metrics under actual conditions. These multi-source data serve as rich foundational information for building data-driven models. During the data preprocessing phase, to ensure data quality and improve model training effectiveness, the raw data undergo systematic processing: (1) Data Cleaning: Removing noise and outliers from the experiments to ensure data accuracy and consistency. (2) Data Normalization: Converting data with different units of measurement to a common range, avoiding the influence of scale differences between features on model performance. (3) Feature Extraction: Employing methods like Principal Component Analysis (PCA) or autoencoders to extract key features, reducing data redundancy and improving computational efficiency. Through scientific data collection and processing, researchers can build high-quality training datasets, providing a solid foundation for developing subsequent data-driven models. 4.2. Machine Learning Model Construction In the machine learning model construction phase, appropriate machine learning algorithms (e.g., neural networks) are selected to predict the drag coefficient. By training and optimizing the model on the training data, a model with high prediction accuracy can be developed. Validation data will then be used to assess and adjust the model’s performance, improving its accuracy and generalization ability. 4.3. Design Optimization and Experimental Validation During the design optimization phase, machine learning models can be used for optimization design, exploring how different design parameter combinations affect the drag coefficient. Optimized design solutions can be generated, and improvements proposed to enhance the vehicle’s aerodynamic performance. Finally, CFD simulations and wind tunnel experiments will be used to validate the optimized design solutions, assessing their effectiveness under practical conditions. 4.4. Feasibility Study In the wake of the rapid development of artificial intelligence (AI) technologies, intelligent technologies have provided new ideas and methods for aerodynamic research. Scholars worldwide have conducted numerous beneficial explorations and attempts in the integrated application of AI and aerodynamic design, with advantages including efficient analysis of massive aerodynamic data and advanced aerodynamic modeling tools for vehicle design. As AI technology evolves, many AI techniques offer the potential to move aerodynamic design from the “designer era” to the “machine era.” 5. Challenges and Difficulties Despite the advancements in AI methods, challenges remain in their application, as they are still constrained by human-set parameters in specific processes. Currently, AI’s role is limited to improving computational efficiency for specific problems or saving memory, and there remains a significant gap between AI capabilities and the expectations in the field. The development of AI algorithms, coupled with a deeper understanding of aerodynamic design by designers, will lay the foundation for transitioning from “human-in-the- 219 loop” to “robot-in-the-loop” and ultimately to “machine design.” This shift will continue to enhance the impact of AI methods in aerodynamic design. 6. Conclusion and Outlook This study has conducted an in-depth exploration and practical implementation of data-driven methods for the aerodynamic design and optimization of transportation vehicles. By integrating multi-source data, constructing machine learning models, and applying optimization algorithms, the research has achieved improvements in design efficiency, reductions in drag coefficient, and advancements in technological innovation. Looking ahead, we will continue to deepen research and practical efforts in this field, aiming to provide more efficient and environmentally friendly solutions for future transportation. We also look forward to collaborating and exchanging ideas with more scholars and institutions to collectively promote the development of aerodynamic design in transportation vehicles. References [1] Smith, J. (2021) A deep learning framework for aerodynamic shape optimization. Journal of Computational Aerodynamics, 15: 123-135. [2] Müller, A., & Schmidt, B. (2022) Generative adversarial networks for automotive body design. Automotive Engineering Review, 8: 56-70. [3] Tanaka, H. (2020) Multi-objective optimization of high-speed train nose shapes. Journal of Railway Engineering, 12: 89-102. [4] Wang, Q., Li, H., & Zhao, Y. (2021) Multi-objective vehicle aerodynamic performance prediction model. Acta Mechanica, 56: 234-249. [5] Li, H., Zhao, M., & Chen, Y. (2020) Optimization design of electric vehicle body using CFD and genetic algorithm. Journal of Vehicle Design, 20: 78-91. [6] National Natural Science Foundation of China (2021) Research on aerodynamic optimization of high-speed trains based on big data. Science Fund Report.