Search Results

Self Organizing Maps (SOM) has evolved as a very useful visualization and data analysis tool for high dimensional data. Visualization and analysis of Pareto data for multi-objective optimization problems with more than three objectives is also a challenge. This paper will investigate the application of SOM for visualization and design selection for multi-objective Pareto data. The SOM is applied to investigate the spread of Pareto front as well as to investigate trade-off between objectives. The visualization and selection strategy is applied to mathematical test problem to explain the concept. Later it is also applied to real world automotive design problem of engine optimization.

In order for an automotive charge air cooler (CAC) to function efficiently, the flow of air through the cross tubes should be as uniform as possible. The position of the inlet and outlet, as well as the shape of the header tanks, are generally the most important determinants of the flow uniformity, and therefore of the cooling performance of the system. In an attempt to achieve this goal of flow uniformity, however, the effect on pressure loss in the system must also be considered. Further, the cost of the CAC tanks, which is directly related to the amount of material, should be minimized. Finally, the physical space in which the CAC can be located is limited by other underhood components and vehicle styling features. This presents an optimization problem with four conflicting objectives: to reduce the pressure loss in the system, to increase the uniformity of flow in the tubes, to minimize the tank material and to conform to the package volume.