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In motorsports, aerodynamic development processes target to achieve gains in performance. This requires a comprehensive understanding of the prevailing aerodynamics and the capability of analysing large quantities of numerical data. However, manual analysis of a significant amount of Computational Fluid Dynamics (CFD) data is time consuming and complex. The motivation is to optimize the aerodynamic analysis workflow with the use of deep learning architectures. In this research, variants of 3D deep learning models (3D-DL) such as Convolutional Autoencoder (CAE) and U-Net frameworks are applied to flow fields obtained from Reynolds Averaged Navier Stokes (RANS) simulations to transform the high-dimensional CFD domain into a low-dimensional embedding. Consequently, model order reduction enables the identification of inherent flow structures represented by the latent space of the models.

One main goal of the automotive industry is to reduce the aerodynamic drag of passenger vehicles. Therefore, a deeper understanding of the flow field is necessary. Time-resolved data of the flow field is required to get an insight into the complex unsteady flow phenomena around passenger vehicles. This data helps to understand the temporal development of wake structures and enables the analysis of the formation of vortical structures. Numerical simulations are an efficient method to analyze the time-resolved data of the unsteady flow field. The analysis of the steady and unsteady numerical data is only relevant for aerodynamic developments in the wind tunnel, if the predicted temporal evolving structures of a passenger vehicle’s simulated flow field correspond to the structures of the flow field in the wind tunnel. In this study, time-resolved measurements of the empty wind tunnel and a notchback passenger vehicle in the wind tunnel are conducted.

The recent growth of available computational resources has enabled the automotive industry to utilize unsteady Computational Fluid Dynamics (CFD) for their product development on a regular basis. Over the past years, it has been confirmed that unsteady CFD can accurately simulate the transient flow field around complex geometries. Concerning the aerodynamic properties of road vehicles, the detailed analysis of the transient flow field can help to improve the driving stability. Until now, however, there haven’t been many investigations that successfully identified a specific transient phenomenon from a simulated flow field corresponding to driving stability. This is because the unsteady flow field around a vehicle consists of various time and length scales and is therefore too complex to be analyzed with the same strategies as for steady state results.

The open jet wind tunnel is a widespread test section configuration for developing full scale passenger cars in the automotive industry. However, using a realizable nozzle cross section for cost effective aerodynamic development is always connected to the presence of wind tunnel effects. Wind tunnel wall interferences which are not present under open road conditions, can affect the measurement of aerodynamic forces. Thus, wind tunnel corrections may be required. This work contains the results of a CFD (Computational Fluid Dynamics) approach using unsteady Delayed Detached Eddy Simulations (DDES) to evaluate wind tunnel interferences for open jet test sections. The Full Scale DrivAer reference geometry of the Technical University of Munich (TUM) using different rear end shapes has been selected for these investigations.

Aerodynamic design and thermal management are some of the most important tasks when developing new concepts for the flow around tractor-trailers. Today, both experimental and numerical studies are an integral part of the aerodynamic and thermal design processes. A variety of studies have been conducted how the aerodynamic design reduces the drag coefficient for fuel efficiency as well as for the construction of radiators to provide cooling on tractor-trailers. However, only a few studies cover the combined effect of the aerodynamic and thermal design on the air temperature of the under hood flow [8, 13, 16, 17, 20]. The objective of this study is to analyze the heat transfer through forced convection for a scaled Cab-over-Engine (CoE) tractor-trailer model with under hood flow. Different design concepts are compared to provide low under hood air temperature and efficient cooling of the sub components.