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.Copyright © 2012 by ASME

[1]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[2]  Hamid Hadim,et al.  A multidisciplinary design and optimization methodology for ball grid array packages using artificial neural networks , 2005 .

[3]  J. M. Glidewell,et al.  A Flow Network Approach to Vehicle Underhood Heat Transfer Problem , 1993 .

[4]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[5]  Kevin L. Priddy,et al.  Artificial Neural Networks: An Introduction (SPIE Tutorial Texts in Optical Engineering, Vol. TT68) , 2005 .

[6]  Sreekanth reddy Gondipalle.  CFD ANALYSIS OF THE UNDER HOOD OF A CAR FOR PACKAGING CONSIDERATIONS , 2011 .

[7]  Wen-Ping Chen,et al.  Integrating Computational Fluid Dynamics and Neural Networks to Predict Temperature Distribution of the Semiconductor Chip with Multi-heat Sources , 2006, ISNN.

[8]  Saiied M. Aminossadati,et al.  An Artificial Neural Network Approach to Cooling Analysis of Electronic Components in Enclosures Filled With Nanofluids , 2011 .

[9]  Yi Miao,et al.  Multi-Objective Vehicle Layout Optimization , 2006 .

[10]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

[11]  Yi Miao,et al.  Design Configuration of Vehicle With Domain Knowledge Enhancement , 2004, DAC 2004.

[12]  Sunil K. Jain,et al.  A multi-dimensional approach to truck underhood thermal management , 2001 .

[13]  Yi Miao,et al.  Multi-Objective Configuration Optimization With Vehicle Dynamics Applied to Midsize Truck Design , 2003, DAC 2003.

[14]  Leah L. Rogers,et al.  Solving Problems in Environmental Engineering and Geosciences with Artificial Neural Networks , 1996 .

[15]  Pradip Dutta,et al.  A Combined CFD and Flow Network Modeling Approach for Vehicle Underhood Air Flow and Thermal Analysis , 2009 .

[16]  Kevin L. Priddy,et al.  Artificial neural networks - an introduction , 2005, Tutorial text series.

[17]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[18]  Georges M. Fadel,et al.  CONFIGURATION DESIGN OPTIMIZATION METHOD , 1999 .

[19]  Norihiko Watanabe,et al.  An 1D-3D Integrating Numerical Simulation for Engine Cooling Problem , 2006 .