AERODYNAMICS DESIGN AND CFD SIMULATION OF A COMPACT CAR USING OPENFOAM: A CASE STUDY FROM THE CITY OF BUCARAMANGA, COLOMBIA

Aerodynamics Design and CFD Simulation of a Compact Car Using OpenFOAM: A Case Study from Bucaramanga, Colombia ADRIAN VARGAS-LIZARAZO1,3, JORGE LUIS CHACÓN-VELASCO2,3 1Universidad Industrial de Santander, Mechanical Engineering school, [email protected] 2Universidad Industrial de Santander, Mechanical Engineering school, [email protected] 3Grupo de Investigación en Energía y Medio Ambiente – GIEMA, Bucaramanga, Colombia Introduction Global efforts to mitigate climate change, reinforced by the Paris Agreement, has prompted countries such as Colombia to pursue substantial reductions in greenhouse gas (GHG) emissions. The transportation sector is one of the main contributors to these emissions [1], particularly in urban environments such as Bucaramanga, which is characterized by a warm climate and high-density mobility, where the transition toward electric and hybrid vehicles is vital. However, in such vehicles, aerodynamic drag can constitute over 50% of total energy consumption at cruising speeds [2], underscoring the necessity to enhance their aerodynamic behavior to improve their efficiency. This study presents the aerodynamic design and and planned simulation of a two-passenger compact vehicle using OpenFOAM as the main CFD tool. The workflow involves the use of CAD modeling and meshing strategies, with an initial baseline already prepared. The next stages will incorporate RANS-based simulations with the k–w SST model and later extend to a RANS–LES hybrid approach to capture wake turbulence. These simulations are expected to be executed on high-performance computing (HPC) resources to handle large meshes and reduce turnaround times. Governing Equations & Turbulence Modeling To simulate the airflow around the vehicle, the motion of the fluid is governed by the Navier–Stokes equations for incompressible flow. These include the conservation of mass (continuity) and momentum, which describe how velocity, pressure, and viscosity interact. In turbulent flows, additional terms arise that must be modeled using turbulence closures. In this study, the k–w SST model will be used to resolve boundary layers accurately. Incompresible RANS Equations •Continuity: Ensures mass conservation in the flow. ∇ · (¯ u) = 0(1) •Momentum: Balances inertia, pressure, and viscous forces. ∂¯ u ∂t +∇ · (¯ u ¯ u)=−1 ρ(∇p)+ν∇2¯ u+1 ρ∇ · τR(2) •Turbulence model: k–ωSST, solves two additional equations for modeling the turbulence, namely, the turbulent kinetic energy and the specific rate of dissipation. ∇tk+∇ · (¯ uk) = τR:∇¯ u −β∗kω +∇ ·              ν+νt σk        ∇k       (3) ∂ω ∂t +∇ · (uω) = α νt τR:∇¯ u−βω2 +∇ ·              ν+νt σω        ∇ω        +2(1−F1)σω2 ω∇k· ∇ω (4) Kolmogorov scales Figure 1: Kolmogorov’s Scales of turbulence. Methodology The aerodynamic analysis of the compact vehicle is structured in three main phases. At the current stage, the conceptual design and the first computational mesh have been completed. These results provide the foundation for subsequent simulations. The following sections summarize the progress achieved and the planned steps that will extend the study. Phase 1: CAD design of the vehicle and initial steady-state RANS (k-w SST, y+=1). During the preliminary phase, the vehicle’s conceptual design was developed in CAD software, with emphasis on minimizing the frontal area and achieving smooth geometric transitions suitable for urban environments. Figure 2: CAD model of the compact electric or hybrid vehicle. A first computational mesh was generated, including local refinements around the vehicle body and in the wake region. The near-wall resolution was ensured with y+ 1, allowing direct capture of the viscous sublayer without wall functions. These actions establish the baseline for accurate evaluation of aerodynamic coefficients in the upcoming simulations. Phase 2: Parametric study The next stage will explore how vehicle speed (40–70 km/h), ground clearance (250–300 mm), and accessory angles affect aerodynamic behavior. The study will focus on drag and lift coefficients, as well as surface pressure distributions, to identify opportunities for design improvements. Phase 3: Hybrid RANS-LESS (DESS Finally, a Delayed Detached Eddy Simulation (DDES) approach will be applied. This hybrid model combines RANS near walls with LES in separated regions, allowing the resolution of large turbulent structures in the wake without requiring impractically fine meshes. Preliminary Results So far, the project has produced the CAD design of the vehicle and a first computational mesh. The mesh includes refinements around the body and in the wake, with nearwall resolution adjusted to y+ = 1. These preliminary results confirm the feasibility of the modeling approach and establish the foundation for subsequent aerodynamic analyses. The upcoming stages will build upon this baseline to deliver quantitative aerodynamic coefficients and flow field characterizations. Figure 3: Boundary layer mesh refinement around the vehicle. For the mesh generation, a domain with more than 3.3 million cells and 10 million faces was created, ensuring local refinements around the body and wake and nearwall resolution with y+ = 1. Mesh quality was verified with acceptable values (Table 2), confirming its suitability for CFD analyses. Test runs using 28 processors required approximately 20 minutes for relatively short simulations (mesh generation), highlighting the computational intensity of the problem. Table 1: Mesh Statistics. Table 2: Mesh Quality Summary. Acknowledgments The authors acknowledge the support from MinCiencias (project 82713) for funding, and the Universidad Industrial de Santander, especially the SC3 Laboratory, for providing the computational resources for the simulations. References [1] J. Thema and M. C. R. Garcia, La transición energética en Colombia. [2] P. Gonzales, “La aerodinámica en los coches eléctricos,” HackerCar. Accessed: Apr. 29, 2025. [Online]. Available: https://hackercar.com/ por-que-la-aerodinamica-es-clave-para-el-futuro-del-coche-electrico/ [3] M. Leschziner, Statistical turbulence modelling for fluid dynamics, demystified: an introductory text for graduate engineering students. London; Hackensack, NJ: Imperial College Press, 2016. CARLA 2025, September 22-26, Kingston, Jamaica