Predicting the Thermal Performance for the Multi-Objective Vehicle Underhood Packing Optimization Problem

The ever increasing demands towards improvement in vehicle performance and passenger comfort have led the automotive manufacturers to further enhance the design in the early stages of the vehicle development process. Though, these design changes enhance the overall vehicle performance to an extent, the placement of these components under the car hood also plays a vital role in increasing the vehicle performance. In the past, a study on the automobile underhood packaging or layout problem was conducted and a multi-objective optimization routine with three objectives namely, minimizing center of gravity height, maximizing vehicle components accessibility and maximizing survivability (for army vehicles) has been setup to determine the optimal locations of the underhood components. The previous study did not consider thermal performance as an objective. This study asserts the necessity of including thermal performance as an objective and makes an assessment of the several available thermal analyses that are performed on the automotive underhood to evaluate the thermal objective. A Neural Network approximation of the CFD analysis conducted over the automotive underhood is presented in this paper. The results obtained from the Neural Network are compared with the CFD results, showing good agreement. The Neural Network model is included in the multi-objective optimization routine and new layout results are obtained. A non-deterministic evolutionary multi-objective algorithm (AMGA-2) is used to perform the optimization process.