Multi-objective optimization of a double-layered microchannel heat sink with temperature-dependent fluid properties

Kulkarni, Kishor; Afzal, Arshad; Kim, Kwang‐Yong

doi:10.1016/j.applthermaleng.2016.01.039

Cited by 60 publications

(14 citation statements)

References 41 publications

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“…Park et al [27] evaluated design parameters in the wavy channel with grooves with the help of a multi-objective Genetic algorithm coupled with RSA surrogate model. Kulkarni et al [28] proposed a design methodology for the heat sink to obtain lower thermal resistance. They used RSA with a multi-objective genetic algorithm to obtain Pareto-optimal design parameters.…”

Section: Regression Based Surrogate Model Optimization Techniquesmentioning

confidence: 99%

Optimization of Microjet Location Using Surrogate Model Coupled with Particle Swarm Optimization Algorithm

Qidwai¹,

Badruddin

Khan

et al. 2021

Mathematics

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This study aimed to present the design methodology of microjet heat sinks with unequal jet spacing, using a machine learning technique which alleviates hot spots in heat sinks with non-uniform heat flux conditions. Latin hypercube sampling was used to obtain 30 design sample points on which three-dimensional Computational Fluid Dynamics (CFD) solutions were calculated, which were used to train the machine learning model. Radial Basis Neural Network (RBNN) was used as a surrogate model coupled with Particle Swarm Optimization (PSO) to obtain the optimized location of jets. The RBNN provides continuous space for searching the optimum values. At the predicted optimum values from the coupled model, the CFD solution was calculated for comparison. The percentage error for the target function was 0.56%, whereas for the accompanied function it was 1.3%. The coupled algorithm has variable inputs at user discretion, including gaussian spread, number of search particles, and number of iterations. The sensitivity of each variable was obtained. Analysis of Variance (ANOVA) was performed to investigate the effect of the input variable on thermal resistance. ANOVA results revealed that gaussian spread is the dominant variable affecting the thermal resistance.

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Section: Regression Based Surrogate Model Optimization Techniquesmentioning

confidence: 99%

Optimization of Microjet Location Using Surrogate Model Coupled with Particle Swarm Optimization Algorithm

Qidwai¹,

Badruddin

Khan

et al. 2021

Mathematics

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“…The viscosity function η of the honey is described by (9), where w is the water content percentage. This model is suitable over the interval of temperature [10,30]…”

Section: A: Mass Balancementioning

confidence: 99%

Optimum Nozzle Design for a Viscous Liquid by Using Multi-Objective Search Approaches

et al. 2020

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This work deals with the problem of a nozzle design addressed through a multi-objective optimization strategy where governing equations of fluid dynamics model the phenomena. The liquid flow rate and the nozzle length are considered as the design criteria. Two Differential Evolution variants are proposed to obtain a set of design configurations that presents numerous and different synergies between the design criteria. The first variant is based on the Hypervolume performance metric (MODE-HVR) and the second one includes the-dominance concept (MODE-HV R). A comparative study is performed with other optimizers from well-established search approaches for multi-objective optimization such as algorithms based on Pareto dominance (NSGA-II and SPEA2), decomposition (MOEA/D), metric (SMS-EMOA) and hybrid (NSGA-III). Based on the Spacing and Hypervolume indicators of the obtained Pareto fronts, the proposed optimization algorithms can provide more design solutions, promoting the reconfigurability (synergy) in the nozzle design. Hence, the multi-objective design strategy allows the designer to have a wide range of solutions and to choose the most suitable one for a particular application, compared with a traditional design where both design criteria are considered as a single aggregate function. INDEX TERMS Optimum nozzle design, multi-objective optimization, multi-objective search approaches, optimization.

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“…However, as expected, the thermal resistance is not reduced with these methods. The optimization method is a promising technique in microchannel heat sink designs, which has been widely used to ensure low thermal resistance and pressure drop of microchannel heat sinks [30][31][32][33]. Technically, an optimized design is only the tradeoff between the heat transfer and the pressure drop penalty, in which the lowest thermal resistance is always obtained with the restriction of an acceptable pressure drop.…”

Section: Introductionmentioning

confidence: 99%

A new scheme for reducing pressure drop and thermal resistance simultaneously in microchannel heat sinks with wavy porous fins

Zhao

Lin

et al. 2017

International Journal of Heat and Mass Transfer

123

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Multi-objective optimization of a double-layered microchannel heat sink with temperature-dependent fluid properties

Cited by 60 publications

References 41 publications

Optimization of Microjet Location Using Surrogate Model Coupled with Particle Swarm Optimization Algorithm

Optimization of Microjet Location Using Surrogate Model Coupled with Particle Swarm Optimization Algorithm

Optimum Nozzle Design for a Viscous Liquid by Using Multi-Objective Search Approaches

A new scheme for reducing pressure drop and thermal resistance simultaneously in microchannel heat sinks with wavy porous fins

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