Potential of adaptive neuro-fuzzy methodology for investigation of heat transfer enhancement of a minichannel heat sink

Jović, Srđan; Kalaba, Dragan; Živković, Milutin; Virijevic, Aleksandar

doi:10.1016/j.physa.2019.02.019

Cited by 6 publications

(1 citation statement)

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“…Gerdroodbary [68] formulated a model using neural networking to predict the heat flux for magnetohydrodynamic nanofluid flow. Jovic et al [69] explored the potentiality of adaptive neuro-fuzzy methodology in the predicting of heat transfer enhancement for the mini channel heat sink with higher accuracy. Machine learning techniques, such as fuzzy inference system (FIS), support vector machine (SVM) and artificial neural network (ANN), have found application in predicting thermal properties, such as effective thermal conductivity [70][71][72][73], thermal boundary resistance [74], recapitulate entropy [75], specific heat [76], dynamic viscosity [77][78][79][80] etc.…”

Section: Introductionmentioning

confidence: 99%

Heat and Fluid Flow Analysis and ANN-Based Prediction of A Novel Spring Corrugated Tape

et al. 2021

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A circular tube fitted with novel corrugated spring tape inserts has been investigated. Air was used as the working fluid. A thorough literature review has been done and this geometry has not been studied previously, neither experimentally nor theoretically. A novel experimental investigation of this enhanced geometry can, therefore, be treated as a new substantial contribution in the open literature. Three different spring ratio and depth ratio has been used in this study. Increase in thermal energy transport coefficient is noticed with increase in depth ratio. Corrugated spring tape shows promising results towards heat transfer enhancement. This geometry performs significantly better (60% to 75% increase in heat duty at constant pumping power and 20% to 31% reduction in pumping power at constant heat duty) than simple spring tape. This paper also presented a statistical analysis of the heat transfer and fluid flow by developing an artificial neural network (ANN)-based machine learning (ML) model. The model is evaluated to have an accuracy of 98.00% on unknown test data. These models will help the researchers working in heat transfer enhancement-based experiments to understand and predict the output. As a result, the time and cost of the experiments will reduce. The results of this investigation can be used in designing heat exchangers.

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