Hybrid Genetic Optimization for IC Chips Thermal Control (eBook, ePUB)
With MATLAB® Applications
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Hybrid Genetic Optimization for IC Chips Thermal Control (eBook, ePUB)
With MATLAB® Applications
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The continuous miniaturization of integrated circuit (IC) chips and the increase in the sleekness of the design of electronic components have led to the monumental rise of volumetric heat generation in electronic components.
Hybrid Genetic Optimization for IC Chips Thermal Control: With MATLAB® Applications focuses on the detailed optimization strategy carried out to enhance the performance (temperature control) of the IC chips oriented at different positions on a switch-mode power supply (SMPS) board and cooled using air under various heat transfer modes. Seven asymmetric protruding…mehr
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Hybrid Genetic Optimization for IC Chips Thermal Control: With MATLAB® Applications focuses on the detailed optimization strategy carried out to enhance the performance (temperature control) of the IC chips oriented at different positions on a switch-mode power supply (SMPS) board and cooled using air under various heat transfer modes. Seven asymmetric protruding IC chips mounted at different positions on an SMPS board are considered in the present study that is supplied with non-uniform heat fluxes.
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- Produktdetails
- Verlag: Taylor & Francis
- Seitenzahl: 174
- Erscheinungstermin: 7. Juni 2022
- Englisch
- ISBN-13: 9781000590432
- Artikelnr.: 63741127
- Verlag: Taylor & Francis
- Seitenzahl: 174
- Erscheinungstermin: 7. Juni 2022
- Englisch
- ISBN-13: 9781000590432
- Artikelnr.: 63741127
Dr. Tapano Kumar Hotta is currently working as an Associate Professor in the School of Mechanical Engineering, VIT Vellore, India. He has a Ph.D. degree in Mechanical Engineering from IIT Madras in the area of Electronic cooling. His area of research in a broad sense includes; Active and passive cooling of electronic devices, Heat transfer enhancement, Optimization of thermal systems, Thermal comfort modeling, etc. Dr. Hotta has about 13 years of teaching cum research experience and has around 40 publications to his credit in journals and conferences of international repute. He has guided more than 30 undergraduates, a dozen of postgraduates, and 2 doctorate students for their project work. He has also published 2 patents. He is a member of the editorial board and reviewer for various international journals and conferences related to heat transfer.
) 4.3 Numerical framework 4.3.1 Governing equations 4.3.2 Boundary conditions 4.3.3 Grid independence study 4.4 Results and discussion 4.4.1 Maximum temperature excess variation of different configurations with
4.4.2 Temperature variation for the IC chips of the lower (
= 0.25103) and the upper extreme (
= 1.87025) configurations 4.4.3 Empirical correlation 4.5 Hybrid optimization strategy 4.5.1 Artificial neural network (ANN) 4.5.2 Genetic algorithm (GA) 4.5.3 Combination of ANN and GA 4.6 Conclusions CHAPTER 5 HYBRID OPTIMIZATION STRATEGY TO STUDY THE SUBSTRATE BOARD ORIENTATION EFFECT FOR THE COOLING OF THE IC CHIPS UNDER FORCED CONVECTION 5.1 Introduction. 5.2 Different IC chips combinations considered for the experimentation 5.3 Results and discussion 5.3.1 Temperature variation of the IC chips for different substrate board orientations 5.3.2 Temperature variation of IC chips for different air velocities 5.3.3 Maximum temperature variation of the configurations for different substrate board orientations 5.3.4 Variation of maximum heat transfer coefficient of the configurations for different substrate board orientations 5.4 Empirical Correlation 5.4.1 Correlation for
in terms of
5.4.2 Correlation for
i in terms of the IC chip positions on the substrate board (Z), non-dimensional board orientation (
) and IC chip sizes (S) 5.4.3 Correlation for Nusselt number of the IC chips in terms of fluid Reynolds number and IC chip's size 5.5 Hybrid optimization strategy to identify the optimal board orientation and optimal configuration of the IC chips 5.5.1 Artificial Neural Network 5.5.2 Genetic algorithm 5.5.3 Combination of ANN and GA 5.6 Numerical investigation for the cooling of the seven asymmetric IC chips under the laminar forced convection 5.6.1 Computational model with governing equations 5.6.2 Boundary conditions 5.6.3 Mesh independence study 5.7 Numerical analysis for the IC chip's temperature under the different substrate board orientations 5.8 Conclusions CHAPTER 6 NUMERICAL AND EXPERIMENTAL INVESTIGATIONS OF PARAFFIN WAX-BASED MINI-CHANNELS FOR THE COOLING OF IC CHIPS. 6.1 Introduction 6.2 Experiment set-up 6.3 Results and discussion 6.3.1 Temperature variation of IC chips without PCM based mini-channels (WPMC) 6.3.2 Temperature variation of IC chips for case 1 with and without the PCM based mini-channels 6.3.3 Temperature variation of IC chips for case 4 with and without the PCM based mini-channels 6.3.4 Temperature variation of IC chips for all cases with PCM based mini-channels (PMC) 6.3.5 Convective heat transfer coefficient variation for all cases with PCM based mini- channels (PMC) 6.3.6 Correlation 6.4 Numerical simulation of PCM based mini-channels under natural convection 6.5 Conclusions CHAPTER 7 CONCLUSIONS AND SCOPE FOR FUTURE WORK 7.1 Introduction 7.2 Major conclusions of the present study 7.3 Scope for future work REFERENCES Appendix A MATLAB programme for generating all the possible configurations for the arrangement of 7 non-identical rectangular IC chips on a substrate board. Appendix B Calculation of Mixed convection considered for numerical study Appendix C Sample calculation for non-dimensional temperature (
) and Fourier number (Fo) Index
) 4.3 Numerical framework 4.3.1 Governing equations 4.3.2 Boundary conditions 4.3.3 Grid independence study 4.4 Results and discussion 4.4.1 Maximum temperature excess variation of different configurations with
4.4.2 Temperature variation for the IC chips of the lower (
= 0.25103) and the upper extreme (
= 1.87025) configurations 4.4.3 Empirical correlation 4.5 Hybrid optimization strategy 4.5.1 Artificial neural network (ANN) 4.5.2 Genetic algorithm (GA) 4.5.3 Combination of ANN and GA 4.6 Conclusions CHAPTER 5 HYBRID OPTIMIZATION STRATEGY TO STUDY THE SUBSTRATE BOARD ORIENTATION EFFECT FOR THE COOLING OF THE IC CHIPS UNDER FORCED CONVECTION 5.1 Introduction. 5.2 Different IC chips combinations considered for the experimentation 5.3 Results and discussion 5.3.1 Temperature variation of the IC chips for different substrate board orientations 5.3.2 Temperature variation of IC chips for different air velocities 5.3.3 Maximum temperature variation of the configurations for different substrate board orientations 5.3.4 Variation of maximum heat transfer coefficient of the configurations for different substrate board orientations 5.4 Empirical Correlation 5.4.1 Correlation for
in terms of
5.4.2 Correlation for
i in terms of the IC chip positions on the substrate board (Z), non-dimensional board orientation (
) and IC chip sizes (S) 5.4.3 Correlation for Nusselt number of the IC chips in terms of fluid Reynolds number and IC chip's size 5.5 Hybrid optimization strategy to identify the optimal board orientation and optimal configuration of the IC chips 5.5.1 Artificial Neural Network 5.5.2 Genetic algorithm 5.5.3 Combination of ANN and GA 5.6 Numerical investigation for the cooling of the seven asymmetric IC chips under the laminar forced convection 5.6.1 Computational model with governing equations 5.6.2 Boundary conditions 5.6.3 Mesh independence study 5.7 Numerical analysis for the IC chip's temperature under the different substrate board orientations 5.8 Conclusions CHAPTER 6 NUMERICAL AND EXPERIMENTAL INVESTIGATIONS OF PARAFFIN WAX-BASED MINI-CHANNELS FOR THE COOLING OF IC CHIPS. 6.1 Introduction 6.2 Experiment set-up 6.3 Results and discussion 6.3.1 Temperature variation of IC chips without PCM based mini-channels (WPMC) 6.3.2 Temperature variation of IC chips for case 1 with and without the PCM based mini-channels 6.3.3 Temperature variation of IC chips for case 4 with and without the PCM based mini-channels 6.3.4 Temperature variation of IC chips for all cases with PCM based mini-channels (PMC) 6.3.5 Convective heat transfer coefficient variation for all cases with PCM based mini- channels (PMC) 6.3.6 Correlation 6.4 Numerical simulation of PCM based mini-channels under natural convection 6.5 Conclusions CHAPTER 7 CONCLUSIONS AND SCOPE FOR FUTURE WORK 7.1 Introduction 7.2 Major conclusions of the present study 7.3 Scope for future work REFERENCES Appendix A MATLAB programme for generating all the possible configurations for the arrangement of 7 non-identical rectangular IC chips on a substrate board. Appendix B Calculation of Mixed convection considered for numerical study Appendix C Sample calculation for non-dimensional temperature (
) and Fourier number (Fo) Index