CFD AND EXPERIMENTAL TESTING IN VEHICLE AERODYNAMICS

Аннотация

This study investigates the aerodynamic performance of vehicles through a comparative analysis of virtual (Computational Fluid Dynamics, CFD) and experimental (wind tunnel) testing methods, focusing on their application in optimizing automotive design. Using a simplified sedan model (Ahmed body, 1:4 scale), the research evaluates drag coefficient (Cd), lift coefficient (Cl), pressure distribution, and flow characteristics at an air velocity of 30 m/s. CFD simulations were conducted using ANSYS Fluent with the k-ε RNG turbulence model, while experimental tests were performed in a low-speed wind tunnel equipped with high-precision sensors. Results indicate a high degree of agreement between the two methods, with a 2.3% difference in Cd(0.295 for CFD vs. 0.302 for wind tunnel) and a 6.6% difference in Cl (0.085 vs. 0.091). Pressure distribution showed 90% consistency, with minor discrepancies attributed to CFD’s turbulence modeling limitations and the wind tunnel’s accurate representation of road surface effects. The findings confirm that CFD offers cost-effective and rapid design iterations, while wind tunnel tests provide superior real-world accuracy. Integrating both methods enhances design efficiency, reduces costs, and improves fuel efficiency and environmental compliance. Recommendations for future research include adopting advanced turbulence models like Large Eddy Simulation (LES) and improving CFD road surface modeling to enhance accuracy. This study provides practical insights for automotiveengineers aimingto balance accuracy, cost, andtimein aerodynamicoptimization. Keywords: Vehicle aerodynamics, Computational Fluid Dynamics (CFD), wind tunnel testing, drag coefficient, lift coefficient, turbulence modeling, automotivedesign Introduction. The automotive industry is one of the most significant and rapidly developing sectors in the modern world, holding substantial importance in economic, environmental, and social domains. The functional characteristics, safety, and efficiency of vehicles depend on numerous factors, particularly aerodynamic properties. Aerodynamics directly impacts a vehicle’s fuel consumption, speed, stability, and noise levels (Anderson, 2010). In recent decades, the growing global demand for improved energy efficiency and reduced environmental impact has further increased the relevance of aerodynamic research. These demands have necessitated the use of advanced methods, specifically virtual (computational) andexperimental testing, tooptimizeaerodynamic performance in vehicledesign. Aerodynamic research focuses on analyzing a vehicle’s resistance to airflow (drag), lift forces, and factors ensuring lateral stability. Aerodynamic drag is one of the primary factors affecting fuel consumption, influenced by the vehicle’s shape, surface quality, and external component design (Hucho, 1998). For instance, modern vehicles typically have a drag coefficient (Cd) ranging from 0.25 to 0.35, and reducing this coefficient by 0.01 can decrease fuel consumption by approximately 1-2% (Schütz, 2011). Consequently, automotive manufacturers invest significant resources in optimizing aerodynamic design. Two primary types of aerodynamic testing—experimental and virtual (computational)—are widely used in the automotive industry. Experimental tests, particularly those conducted in wind tunnels, provide high accuracy indetermining a vehicle’s actual aerodynamicproperties. Wind tunnels allow real-time measurement of airflow effects on a vehicle but require page801 INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE ISSN:2692-5206,ImpactFactor:12,23 AmericanAcademicpublishers,volume05,issue05,2025 Journal: https://www.academicpublishers.org/journals/index.php/ijai substantial costs and time (Katz, 2006). In contrast, virtual testing, such as computational fluid dynamics (CFD) modeling, accelerates the design process and reduces costs. CFD software, such as ANSYS Fluent or STAR-CCM+, enables the analysis of aerodynamic properties in a virtual environment (Versteeg & Malalasekera, 2007). However, the accuracy ofvirtual tests depends onmodel quality, boundary conditions, and computational algorithms, often requiring validation with experimental results. In recent years, the integration of virtual and experimental testing has gained popularity in the automotive industry. This approach combines the strengths of both methods, optimizing the design process. For example, initial designs are tested virtually, and only the most promising variants undergo experimental validation in wind tunnels (Gu, 2015). This method not only reduces costs but also allows for more design iterations. However, discrepancies between virtual and experimental results must be analyzed to ensure reliability, highlighting the need forcomparativestudies. The importance of aerodynamic testing extends beyond fuel efficiency. In modern vehicles, reducing noise levels and enhancing driver and passenger comfort are also key objectives. For instance,