A sports car exhibits many challenges from an aerodynamic point of view: drag that limits top speed, lift - or down force - and balance that affects handling, brake cooling and insuring that the heat exchangers have enough air flowing through them under several vehicle speeds and ambient conditions. All of which must be balanced with a sports car styling and esthetic. Since this sports car applies two electric motors to drive front axle and a high-rev V6 turbo charged engine in series with a 9-speed double-clutch transmission and one electric motor to drive rear axle, additional cooling was required, yielding a total of ten air cooled-heat exchangers. It is also a challenge to introduce cooling air into the rear engine room to protect the car under severe thermal conditions. This paper focuses on the cooling and heat resistance concept. The experimental and computational developments of ten air cooled-heat exchangers are described with the tradeoffs that were required during the development process. Since the cooling and heat resistance package was being developed as the vehicle concept and styling were coming together, CFD played a large role in the heat exchanger size, location, heat resistance package and performance. Specific correlation tests were conducted to gain a greater understanding of the performance of some of the heat exchangers. Ultimately, the flow through all of the heat exchangers was confirmed as meeting all the targets at the first wind tunnel test and also heat resistance package was fixed with significant CFD support.

In many engineering domains like aerospace, vehicle or engine design the analysis of flow fields, acquired from computational fluid dynamics (CFD) simulations, can reveal important insights on the behavior of the simulated objects. However, the huge amount of flow data produced by each simulation complicates the data processing and limits the application of computational tools for flow analysis. Thus, an a priori transformation of the flow data into a compact low dimensional representation is desired. This paper introduces a new procedure for transforming flow field data into a compact streamline based representation. Wherein, streamlines with negligible information contribution are removed from the representation. The reduced set of streamlines defines the basis for a subsequent quantification of flow field distances. Experimental studies show that the distances calculated based on the compact representation well approximate the distances of the uncompressed flow field with a significant drop in memory consumption.

The choice of the representation in evolutionary design optimization defines the flexibility and constraints of the search process. Finding an adequate representation is a pre-requisite for the success of an optimization but requires extensive knowledge about the design at hand. Based on the results of an automotive part design optimization, the authors provide evidence that an adaptation of the representation based on sensitivity information leads to new outperforming designs. for retrieving reliable sensitivity estimates a robust variant of the mutual information has been introduced. the robust sensitivity measure provides valuable information for the setup of an improved representation.

We describe global and local methods for comparing flow fields, and a visualization tool that allows the user to adjust how flow fields are compared. Our first global method operates on path-lines and measures variations in orientation and curvature between samples on the same path-line. We then apply the global approach to first-order attributes computed from the gradient tensors of the vector field. We show that local distributions are useful when the goal is to identify distinct regions and visualize patterns of vector field attributes directly within the flow field. To aggregate local descriptors into a global signature that allows us to compare entire vector fields, we use clustering. Our experiments show that the global methods outperform the local methods based on vector spin images, while also being more efficient. In conclusion, we recommend the 1D global distributions that can be efficiently combined into a single measure of similarity with adjustable weights for each attribute. I. Introduction omparing vector fields will become increasingly crucial as computational methods for simulating fluid dynamics (CFD), sensor technology for dynamic data, and video surveillance increase in accuracy and ubiquity. Automotive applications of comparing vector field include engine design where engineers need to compare simulated in-cylinder flow to an ideal swirl flow 4 , and in traffic analysis where positioning and velocity data from cars may be used to identify emerging traffic jams after comparing with smooth traffic flow field. We present results from our research on: