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Aerodynamic vehicle design improvements require flow simulation driven iterative shape changes. The 3-D flow field simulations (CFD analysis) are not explicitly descriptive in providing the direction for aerodynamic shape changes (reducing drag force or increasing the down-force). In recent times, aerodynamic shape optimization using the adjoint method has been gaining more attention in the automotive industry. The traditional DOE (Design of Experiment) optimization method based on the shape parameters requires a large number of CFD flow simulations for obtaining design sensitivities of these shape parameters. The large number of CFD flow simulations can be significantly reduced if the adjoint method is applied. The main purpose of the present study is to demonstrate and validate the adjoint method for vehicle aerodynamic shape improvements.

Three-dimensional flow and temperature distributions in a passenger compartment are very important for evaluating passenger comfort and improving A/C system design. In the present study, the Reynolds-averaged Navier-Stokes equations and the energy transport equation were solved, by both quasi-steady and full transient approaches, to simulate a passenger compartment cooling process. By comparing the predictions with experimental results for a simplified GM-10 passenger compartment, the accuracy of the simulation was assessed. Throughout the 800-second period, good agreement was observed between the measured breath-level air temperatures and the prediction of the transient simulation. The quasi-steady simulation underpredicted air temperatures at the very early stage of the cooling process. However, after 200 seconds of cool down, the quasi-steady simulation predicted air temperatures equally as well as the full transient simulation.

The growing applications of Computer-Aided Engineering (CAE) tools have been motivated by the need to create more effective product development processes. Computational Fluid Dynamics (CFD), as one of the CAE tools, has enjoyed growing popularity for analysis of many airflow situations, including road vehicle aerodynamics. In many cases, these applications have been limited by the level of predictive accuracy that is possible with CFD codes today. In the present exercise, simplified representations of three vehicle models (1:12 scale) were chosen to assess the overall level of predictability of the GMTEC CFD code, using detailed measurements that were made in a scale-model wind tunnel. The CFD computations used two turbulence models (standard k-εand RNG k-ε) and were matched to the experimental geometry and test conditions.

Thermal cabin comfort is the largest consumer of battery energy second only to propulsion in Battery Electric Vehicles (BEV’s). Accurate prediction of thermal comfort in the vehicle cabin with fast turnaround times will allow engineers to study the impact of various thermal comfort technologies and develop energy efficient Heating, Ventilation and Air Conditioning (HVAC) systems. In this study a novel data-driven model based on physics-guided Sparse Identification of Nonlinear Dynamics (SINDy) method was developed to predict Equivalent Homogeneous Temperature (EHT), Mean Radiant Temperature (MRT) and cabin air temperature under transient conditions and drive cycles. EHT is a recognized measure of the total heat loss from the human body that can be used to characterize highly non-uniform thermal environments such as a vehicle cabin. The SINDy model was trained on drive cycle data from Climatic Wind Tunnel (CWT) for a representative Battery Electric Vehicle.