Investigation of optimal position of a vortex generator in a blocked channel for heat transfer enhancement of electronic chips

Abstract A heat transfer optimization of a channel including three blocks attached to its bottom wall (i.e. electronic chips) with an inserted triangular bar acting as a vortex generator (control element) is carried out in order to maximize heat transfer rate as well as to achieve heat flux uniformity over the blocks. Genetic algorithm (GA) combined with Gaussian Process (GP) is utilized as the optimization algorithm. The Nusselt number of the samples is computed with the aid of a Navier–Stokes solver. It is shown that a well trained GP can accurately predict the Nusselt number of each block separately which matches very well with data obtained from the outputs of Navier–Stokes solver. The fitness function is defined as the summation of weighted Nusselt numbers and standard deviation term multiplied by a constant coefficient (to control the priority of homogeneity). The optimization results show that the greater value of the standard deviation multiplier, the more uniform Nusselt numbers. In addition, the optimum location of vortex generator is seen to be above the first block for which uniformity is neglected; however, it displaces from top of the first block to the top of the second block as uniformity become more important. Another conclusion obtained in the present work is that the optimal position of the vortex generator is independent of the Reynolds number.

[1]  John Daniel. Bagley,et al.  The behavior of adaptive systems which employ genetic and correlation algorithms : technical report , 1967 .

[2]  Horng-Wen Wu,et al.  Effect of an oblique plate on the heat transfer enhancement of mixed convection over heated blocks in a horizontal channel , 1999 .

[3]  Frank P. Incropera,et al.  Convection heat transfer in electronic equipment cooling , 1988 .

[4]  Mohamed Gad-el-Hak,et al.  New Approach to Constrained Shape Optimization Using Genetic Algorithms , 1998 .

[5]  Louis Gosselin,et al.  Review of utilization of genetic algorithms in heat transfer problems , 2009 .

[6]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[7]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[8]  Nestor V. Queipo,et al.  Genetic algorithms for thermosciences research: application to the optimized cooling of electronic components , 1994 .

[9]  J.C. Principe,et al.  Modeling and control of unknown chaotic systems via multiple models , 2004, Proceedings of the 2004 14th IEEE Signal Processing Society Workshop Machine Learning for Signal Processing, 2004..

[10]  Liang Fang,et al.  Optimization of concrete hollow brick using hybrid genetic algorithm combining with artificial neural networks , 2010 .

[11]  A. Valencia Heat transfer enhancement due to self-sustained oscillating transverse vortices in channels with periodically mounted rectangular bars , 1999 .

[12]  Yasin Varol,et al.  Control of heat transfer and fluid flow using a triangular bar in heated blocks located in a channel , 2009 .

[13]  Ahmad Sohankar, Lars Davidson,et al.  EFFECT OF INCLINED VORTEX GENERATORS ON HEAT TRANSFER ENHANCEMENT IN A THREE-DIMENSIONAL CHANNEL , 2001 .

[14]  Suh-Jenq Yang,et al.  A NUMERICAL INVESTIGATION OF HEAT TRANSFER ENHANCEMENT FOR ELECTRONIC DEVICES USING AN OSCILLATING VORTEX GENERATOR , 2002 .

[15]  Richard S. Sutton,et al.  Neural networks for control , 1990 .

[16]  S. Patankar Numerical Heat Transfer and Fluid Flow , 2018, Lecture Notes in Mechanical Engineering.