Optimal Design of Plate-Fin Heat Sink under Natural Convection Using a Particle Swarm Optimization Algorithm

Liang, Chaofeng; Zeng, Shi; Li, Z.; Yang, Di; Sherif, S. A.

doi:10.18280/ijht.340217

Cited by 10 publications

(6 citation statements)

References 19 publications

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“…Two types of heat-sink designs were experimentally tested a heat sink with one cut and a heat sink without cut for different heat inputs, from the experimental testing it was concluded that the heat sink with the cut had more temperature variations when compared to the heat sink without the cut. Liang et al [12] focused on finding the optimal design parameters such as fin height, fin number, fin thickness of a plate-fin heat sink under natural convection using the particle swarm optimization (PSO) Algorithm minimization written in MATLAB. The optimized heat sink results for the fin height, number of fins, fin thickness, base temperature were 44.8 mm, 25, 0.6 mm and 342.6241 K respectively.…”

Section: Introductionmentioning

confidence: 99%

Modeling Temperature Response Profile of MOSFET Chip with Heat Sink Parameters in Power Inverters

Onoroh¹,

Ogbonnaya²,

Onochie³

2022

AMS

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Direct current to alternating current converter is applied in the conversion of renewable energy such as solar and wind energy. The switching operation is performed by metal oxide semiconductor field effect transistor chips (MOSFET), during the operation heat is generated and if the heat generated is not properly dissipated, it may lead to thermally induced failure. This research modelled the temperature profile of the MOSFET chip with geometrical properties of the heat sink to which they are attached. The model was implemented in a MATLAB environment to obtained optimal heat sink parameters which was solved numerically with ANSYS and experimentally tested in a 1.6 kW inverter. The maximum MOSFET temperature obtained analytically, numerically and experimentally are is 73.68°C, 81.44°C and 87.35°C at a pulse load of 1000W. The numerical and experimental results of the optimized heat sink temperatures show good correlation with 7% deviation at a pulse load of 1000W and an average deviation of 17% in the power range of 600W to 1000W which shows that the optimized heat sink for the MOSFET chips work well and the model can be deploy for rapid prototyping of power inverter.

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Section: Introductionmentioning

confidence: 99%

Modeling Temperature Response Profile of MOSFET Chip with Heat Sink Parameters in Power Inverters

Onoroh¹,

Ogbonnaya²,

Onochie³

2022

AMS

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“…Recent years saw the emergence of artificial intelligence methods like metaheuristic algorithm [15,16], local search algorithm [17], binary particle swarm optimization [18,19], and multi-objective tabu search algorithm [20,21]. In spite of some achievements, these methods are subject to certain limitations.…”

Section: Introductionmentioning

confidence: 99%

Optimization Algorithm Simulation for Dual-Resource Constrained Job-Shop Scheduling

Yue¹,

Tang²

2018

Int. j. simul. model.

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This research aims to optimize the job-shop scheduling constrained by manpower and machine under complex manufacturing conditions. To this end, a branch population genetic algorithm was presented based on compressed time-window scheduling strategy, and optimized with elite evolution and fanshaped roulette operator. Specifically, the compressed time-window scheduling strategy was proposed to meet the two optimization targets: the maximum makespan and the total processing cost. Then, the elite evolution and fan-shaped roulette operator were introduced to simplify the global and local search, enhance the capacity of branch population genetic algorithm, and suppress the early elimination of inferior solutions, thus preventing the algorithm from falling into the local optimal solution. Finally, the rationality and feasibility of the proposed algorithm were verified through a simulation test. The simulation results show that the proposed algorithm lowered the maximum makespan and total processing cost by 7.4 % and 4.7 %, respectively, from the level of the original branch population genetic algorithm. This means the compressed time-window scheduling strategy can significantly optimize the makespan and the cost, as well as the robustness and global search ability.

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“…Of course, the OPS raises a high demand for macro-optimization scheduling based on big data and features complex computation. Over the years, many intelligent optimization algorithms have been successfully applied to further improve the OPS's processing optimization ability, namely, competitive genetic algorithm (GA), directed ant colony algorithm (ACA), swarm intelligence (SI) information sharing algorithm, multiobjective simulated annealing algorithm [29][30][31][32][33][34]. Due to the high cost of field tests, computer simulation is the most common method for optimizing production line scheduling.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Multivariate Scheduling Optimization for Open Production Line Based on Improved Genetic Algorithm

Li¹,

Gui²,

Xiao³

2018

Int. j. simul. model.

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This paper attempts to overcome the defects of bottleneck recognition and scheduling optimization in open production line. For this purpose, the author analysed the impact mechanism of external disturbances and system configuration changes on the production line, and put forward a multibottleneck identification model for production line through computer simulation. Then, the proposed model was applied to optimize the scheduling of open production line. Specifically, the bottlenecks of production line were identified based on hierarchical clustering and multi-attribute decision-making, aiming to overcome the small candidate set and low accuracy of traditional bottleneck identification algorithms. The measured results show that the proposed algorithm has clear primary and secondary logics; the number of main bottleneck clusters decreased with the increase in the order; the number of machines in the main bottleneck cluster changed nonlinearly. The traditional genetic algorithm (GA) was improved in three aspects: the local optimum trap was avoided by enhancing population diversity; the iteration speed was accelerated with the introduction of adaptive crossover operator and genetic operator; without sacrificing the computing speed, the convergence quality was guaranteed through the addition of multivariate competition algorithm. The research findings provide new insights into the efficient operation of production line.

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Optimal Design of Plate-Fin Heat Sink under Natural Convection Using a Particle Swarm Optimization Algorithm

Cited by 10 publications

References 19 publications

Modeling Temperature Response Profile of MOSFET Chip with Heat Sink Parameters in Power Inverters

Modeling Temperature Response Profile of MOSFET Chip with Heat Sink Parameters in Power Inverters

Optimization Algorithm Simulation for Dual-Resource Constrained Job-Shop Scheduling

Simulation of Multivariate Scheduling Optimization for Open Production Line Based on Improved Genetic Algorithm

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