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 IC chips mounted at different positions on an SMPS board are considered in the present study that is supplied with non-uniform heat fluxes.
Key Features:
- Provides guidance on performance enhancement and reliability of IC chips
- Provides a detailed hybrid optimization strategy for the optimal arrangement of IC chips on a board
- The MATLAB program for the hybrid optimization strategy along with its stability analysis is carried out in a detailed manner
- Enables thermal design engineers to identify the positioning of IC chips on the board to increase their reliability and working cycle
Author(s): Mathew V. K., Tapano Kumar Hotta
Series: Advances in Metaheuristics
Publisher: CRC Press/Chapman & Hall
Year: 2022
Language: English
Pages: 174
City: Boca Raton
Cover
Half Title
Series Page
Title Page
Copyright Page
Contents
About the Authors
Preface
Acknowledgement
Nomenclature
CHAPTER 1: Introduction to Electronic Cooling
1.1. INTRODUCTION
1.2. NEED FOR ELECTRONIC COOLING
1.3. PRINTED CIRCUIT BOARD AND INTEGRATED CIRCUIT CHIPS
1.4. VARIOUS COOLING TECHNIQUES
1.4.1. Air Cooling
1.4.2. Phase Change Material-Based Cooling
1.5. OPTIMIZATION IN HEAT TRANSFER
CHAPTER 2: State-of-the-Art Studies in Electronic Cooling
2.1. INTRODUCTION
2.2. STUDIES PERTAINING TO NATURAL CONVECTION COOLING OF DISCRETE IC CHIPS
2.3. STUDIES RELEVANT TO FORCED AND MIXED CONVECTION COOLING OF DISCRETE IC CHIPS
2.4. STUDIES PERTAINING TO THE PHASE CHANGE MATERIAL-BASED COOLING OF DISCRETE INTEGRATED CIRCUIT CHIPS
2.5. SUMMARY OF THE LITERATURE SURVEY
2.6. SCOPE FOR DEVELOPMENT
2.7. DIFFERENT PARAMETERS CONSIDERED FOR THE STUDY
CHAPTER 3: Experimental Facility
3.1. INTRODUCTION
3.2. SELECTION OF THE INTEGRATED CIRCUIT CHIPS AND THE SWITCH-MODE POWER SUPPLY BOARD
3.3. DESIGN OF THE INTEGRATED CIRCUIT CHIP AND THE SWITCH–MODE POWER SUPPLY BOARD
3.3.1. Design of Integrated Circuit Chips
3.3.2. Design of the Switch-Mode Power Supply (Substrate) Board
3.3.2.1. Substrate Board Design to Carry Out the Laminar Forced Convection Experiments
3.3.2.2. Substrate Board Design to Carry Out the Experiments Using the Phase Change Material-Filled Mini-Channels
3.4. EXPERIMENTAL SETUP AND INSTRUMENTATION
3.4.1. Instruments Used for the Experimental Analysis
3.4.1.1. Direct Current Power Source
3.4.1.2. Hot Wire Anemometer
3.4.1.3. Temperature Data-Logger
3.4.1.4. Digital Multimeter
3.4.1.5. Kapton Tape
3.5. EXPERIMENTAL METHODOLOGY
3.5.1. Procedure for Conducting Laminar Forced Convection Steady-State Experiments
3.5.2. Procedure for Conducting Transient Experiments on the Phase Change Material-Filled Mini-Channels under the Natural Convection
3.6. EXPERIMENTAL CALCULATIONS
3.6.1. Experimental Calculations under Laminar-Forced Convection Heat Transfer Mode
3.6.2. Experimental Calculations for the Phase Change Material-Filled Mini-Channels under the Natural Convection Heat Transfer Mode
3.7. ERROR ANALYSIS
CHAPTER 4: Hybrid Optimization Strategy for the Arrangement of IC Chips under the Mixed Convection
4.1. INTRODUCTION
4.2. NON-DIMENSIONAL GEOMETRIC DISTANCE PARAMETER (λ)
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 λ
4.4.2. Temperature Variation for the IC Chips of the Lower (λ = 0.25103) and the Upper Extreme (λ = 1.87025) Configurations
4.4.3. Empirical Correlation
4.5. HYBRID OPTIMIZATION STRATEGY
4.5.1. Artificial Neural Network
4.5.2. Genetic Algorithm
4.5.3. Combination of Artificial Neural Network and Genetic Algorithm
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 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 θ in Terms of λ
5.4.2. Correlation for θi in Terms of the IC Chip Positions on the Substrate Board (Z), Non-Dimensional Board Orientation (φ), 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. EXPERIMENTAL SET-UP
6.3. RESULTS AND DISCUSSION
6.3.1. Temperature Variation of IC Chips without PCM-Based Mini-Channels
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 Cases with PCM-Based Mini-Channels
6.3.5. Convective Heat Transfer Coefficient Variation for Cases with PCM-Based Mini-Channels (PMCs)
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
APPENDIX B
APPENDIX C
APPENDIX D
INDEX,