Electronics system thermal management optimization using finite element and Nelder-Mead method

The demand for high-performance, smaller-sized, and multi-functional electronics component poses a great challenge to the thermal management issues in a printed circuit board (PCB) design. Moreover, this thermal problem can affect the lifespan, performance, and the reliability of the electronic system.  This project presents the simulation of an optimal thermal distribution for various samples of electronics components arrangement on PCB. The objectives are to find the optimum components arrangement with minimal heat dissipation and cover small PCB area. Nelder-Mead Optimization (NMO) with Finite Element method has been used to solve these multi-objective problems. The results show that with the proper arrangement of electronics components, the area of PCB has been reduced by 26% while the temperature of components is able to reduce up to 40%. Therefore, this study significantly benefits for the case of thermal management and performance improvement onto the electronic product and system

Electronics System Thermal Management Optimization Using Finite Element And Nelder-Mead Method

The demand for high-performance, smaller-sized, and multi-functional electronics component poses a great challenge to the thermal management issues in a printed circuit board (PCB) design. Moreover, this thermal problem can affect the lifespan, performance, and the reliability of the electronic system. This project presents the simulation of an optimal thermal distribution for various samples of electronics components arrangement on PCB. The objectives are to find the optimum components arrangement with minimal heat dissipation and cover small PCB area. Nelder-Mead Optimization (NMO) with Finite Element method has been used to solve these multi-objective problems. The results show that with the proper arrangement of electronics components, the area of PCB has been reduced by 26% while the temperature of components is able to reduce up to 40%. Therefore, this study significantly benefits for the case of thermal management and performance improvement onto the electronic product and system

Optimal Thermal Distribution by using Inverse Genetic Algorithm Optimization Technique

Optimal arrangement of components on printed circuit board (PCB) has become a basic necessity so as to have effective management of heat generation and dissipation. In this work, Inverse Genetic Algorithm (IGA) optimization has been adopted in order to achieve this objective. This paper proposes IGA search engine to optimize the thermal profile of components based on thermal resistance network and to minimize the area of PCB. Comparison between the proposed IGA and the conventional GA (FGA) performances are extensively analyzed. Unlike the conventional FGA, the IGA approach allows the user to set the desired fitness, so that the GA process will try to approach these set values. A reduction in the overall computational time and the freedom of choosing a desired fitness are the major advantages of IGA over FGA. From the simulation results, the IGA has successfully minimized the thermal profile and area of PCB by 0.78% and 1.28% respectively. The computational time has also been minimized by 15.56%

Thermal and area optimization for component placement on PCB design using inverse genetic algorithm

Considering the current trend of compact designs which are mostly multiobjective in nature, proper arrangement of components has become a basic necessity so as to have optimal management of heat generation and dissipation. In this work, Inverse Genetic Algorithm (IGA) optimization has been adopted in order to achieve optimal placement of components on printed circuit board (PCB). The objective functions are the PCB area and temperature of each component while the constraint parameters are; to avoid the overlapping of components, the maximum allowable PCB area is 2(120193.4)mm2 , thermal connections were internally set, and the manufacturer allowable temperature for the ICs must be more than the components optimal temperature. In the conventional Forward Genetic Algorithm (FGA) optimization, the individual fitness of components are generated through the GA process. The IGA approach on the other hand, allows the user to set the desired fitness, so that the GA process will try to approach these set values. Hence, the IGA has two major advantages over FGA; the first being a reduction in the overall computational time and the other is the freedom of choosing the desired fitness (i.e. ability to manipulate the GA output). The objectives of this work includes; development of an IGA search Engine, minimization of the thermal profile of components based on thermal resistance network and the area of PCB, and comparison of the proposed IGA and FGA performances. From the simulation results, the IGA has successfully minimized the thermal profile and area of PCB by 0.78% and 1.28% respectively. The CPU-time has also been minimised by 15.56%

Machine learning thermal circuit network model for thermal design optimization of electronic circuit board layout with transient heating chips

This paper describes a method combining Bayesian optimization (BO) and a lamped-capacitance thermal circuit network model that is effective for speeding up the thermal design optimization of an electronic circuit board layout with transient heating chips. As electronic devices have become smaller and more complex, the importance of thermal design optimization to ensure heat dissipation performance has increased. However, such thermal design optimization is difficult because it is necessary to consider various trade-offs associated with packaging and transient temperature changes of heat-generating components. This study aims to improve the performance of thermal design optimization by artificial intelligence. BO using a Gaussian process was combined with the lamped-capacitance thermal circuit network model, and its performance was verified by case studies. As a result, BO successfully found the ideal circuit board layout as well as particle swarm optimization (PSO) and genetic algorithm (GA) could. The CPU time for BO was 1/5 and 1/4 of that for PSO and GA, respectively. In addition, BO found a non-intuitive optimal solution in approximately 7 minutes from 10 million layout patterns. It was estimated that this was 1/1000 of the CPU time required for analyzing all layout patterns.Comment: 13 pages, 7 figure

Improvement of electronic components arrangement on printed circuit board based on thermal distribution profile using comsol multiphysics

The increasing demand for high speed, high performance, robust and smaller packaging and high-power density leads to the need for an optimal electronic system design. These features will increase the thermal distribution of electronics components, which directly affect the lifespan of the electronic product, performance, and the reliability. This project presents the 3D simulation of the optimal thermal profile of component placement on Printed Circuit Board (PCB) using COMSOL Multiphysics software package. The simulation was carried out for various model of component arrangement on (PCB). The objectives are to find an optimum component arrangement with minimal heat dissipation and smaller area of PCB. Nelder-Mead Optimization tools have been used to solve the multi-objective problems. The result shows that with the proper arrangement, the area of PCB able reduces up to 26% while the temperature of components able to reduce up to 40%. Therefore, this research will significantly benefit for the case of thermal performance improvement onto the electronic product and package size

Thermal model for electronic components placement on printed circuit board

This dissertation is regarding the fuzzy-thermal method for the placement of electronic components on a printed circuit board. In the conventional quadrisection placement method, the placement is according to components with higher power dissipation will be placed separately from other components with lower power dissipation. This is done in order to control the overall temperature of the printed circuit board. This principle is different from fuzzy thermal method, where the placement of component is placed randomly on printed circuit board. The fitness function is then calculated to get the best placement. The fitness function is taking into consideration the minimization of area and the temperature. In order to get the best value of fitness function, the area function and the temperature function must minimum. Comparative analysis has been done to verify the results, which show that Fuzzy thermal method has perform 4% better than quadrisection method

Thermal Performance Analysis for Optimal Passive Cooling Heat Sink Design

Recent advances in semiconductor technology show the improvement of fabrication on electronics appliances in terms of performance, power density and even the size. This great achievement however led to some major problems on thermal and heat distribution of the electronic devices. This thermal problem could reduce the efficiency and reliability of the electronic devices. In order to minimize this thermal problem, an optimal cooling techniques need to be applied during the operation. There are various cooling techniques have been used and one of them is passive pin fin heat sink approach. This paper focuses on inline pin fin heat sink, which use copper material with different shapes of pin fin and a constant 5.5W heat sources. The simulation model has been formulated using COMSOL Multiphysics software to stimulate the pin fin design, study the thermal distribution and the maximum heat profile

Thermal performance analysis for optimal passive cooling heat sink design

Recent advances in semiconductor technology show the improvement of fabrication on electronics appliances in terms of performance, power density and even the size. This great achievement however led to some major problems on thermal and heat distribution of the electronic devices. This thermal problem could reduce the efficiency and reliability of the electronic devices. In order to minimize this thermal problem, an optimal cooling techniques need to be applied during the operation. There are various cooling techniques have been used and one of them is passive pin fin heat sink approach. This paper focuses on inline pin fin heat sink, which use copper material with different shapes of pin fin and a constant 5.5W heat sources. The simulation model has been formulated using COMSOL Multiphysics software to stimulate the pin fin design, study the thermal distribution and the maximum heat profile

END OF LIFE MANAGEMENT OF ELECTRONIC WASTE

Electronic products are becoming obsolete at a very high rate due to rapid changes in consumer demand and technological advancements. However, on other hand End-of-Life (EOL) management of electronic products is not effectively approached while these products offer huge opportunities for effective recycling. In this context, this thesis has highlighted the current practices and issues related to EOL management of electronic products focusing on their different material compositions, the uses of their raw materials in the circular economy perspective. The thesis proposes the introduction of digital technologies into the recycling process to improve efficiency. More specifically, this thesis has focused on the corona electrostatic separation process and the improvement of efficiency based on the simulation of the particle trajectories to identify the most effective parameters. Thus, in this frame, a numerical model to predict the particle trajectories in a corona electrostatic separator is developed using COMSOL Multiphysics and MATLAB software and validated with experimental trials. The recycling of electronic waste is becoming challenging due to its diverse and constantly changing material composition. In this regard, this thesis illustrates the use of non-destructive visible near-infrared hyperspectral imaging (VNIR-HSI) technique to identify material accurately; the effectiveness of VNIR-HSI is demonstrated through an experimental campaign combined with machine learning models, such as Support Vector Machine, K-Nearest Neighbors and Neural Network.Nonostante i prodotti elettronici diventino obsoleti ad un ritmo molto elevato, a causa dei rapidi cambiamenti nella domanda dei consumatori e dei progressi tecnologici, la gestione del loro fine vita (End-of-Life (EOL)) non viene affrontata in modo efficace benché offra, invece, grandi opportunità di riciclo. In questo contesto, questa tesi ha evidenziato le attuali pratiche e problematiche relative alla gestione del fine vita dei prodotti elettronici concentrandosi sulla loro diversa composizione, l’utilizzo delle materie prime seconde ricavabili in una prospettiva di economia circolare. La tesi propone l’introduzione di tecnologie digitali nel processo di riciclo per migliorarne l'efficienza. In particolare, questa tesi si è concentrata sul processo di separazione elettrostatica a corona e sul miglioramento dell'efficienza grazie alla simulazione delle traiettorie delle particelle per identificare i parametri più efficaci. Pertanto, in questo studio, utilizzando i software COMSOL Multiphysics e MATLAB, è stato sviluppato un modello numerico per prevedere le traiettorie delle particelle in un separatore elettrostatico a corona; il modello è stato poi validato con prove sperimentali. Il riciclo dei rifiuti elettronici sta diventando sempre più complesso a causa della presenza di mix di materiali diversificati e in continua evoluzione. A questo proposito, la tecnologia di visione iperspettrale non distruttiva basata su lunghezze d’onda nel visibile e nel vicino infrarosso (VNIR-HSI) è stata utilizzata in questo lavoro di tesi per identificare il materiale in modo preciso; l'efficacia di VNIR-HSI, combinato con modelli di apprendimento automatico, come la Support Vector Machine, K-Nearest Neighbors e Neural Network, viene dimostrata attraverso una campagna sperimentale