Prediction of heat transfer coefficient, pressure drop, and overall cost of double-pipe heat exchangers using the artificial neural network

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dc.authoridAcikgoz, Ozgen/0000-0002-0095-829X
dc.authoridWongwises, Somchai/0000-0003-2648-6814
dc.authoridMercan, Hatice/0000-0002-3445-3441
dc.authoridDalkilic, Ahmet Selim/0000-0002-5743-3937
dc.authoridColak, Andac Batur/0000-0001-9297-8134
dc.contributor.authorColak, Andac Batur
dc.contributor.authorAcikgoz, Ozgen
dc.contributor.authorMercan, Hatice
dc.contributor.authorDalkilic, Ahmet Selim
dc.contributor.authorWongwises, Somchai
dc.date.accessioned2024-11-07T13:34:40Z
dc.date.available2024-11-07T13:34:40Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractTypically, success in the estimation of machine learning is expected to rise with increasing input parameters, whereas the noise issue may rarely arise owing to redundant input factors undesirably influencing the learning algorithm. The parameters such as overall heat transfer coefficient, pressure drop, and overall cost have been determined by two different artificial neural networks evaluated by a multi-layer perceptron model. Using the Levenberg-Marquardt training algorithm, in the first model input layer, a total of 10 input parameters rho, n(p), k(1), Re-1, f(i), Re-2, f(o), n(s), P-1 and P-2 have been defined, while the second involves 8 input parameters by subtracting pumping powers from the first one, thus the noise issue has been investigated using unnecessary input parameters. Overall heat transfer coefficient, tube/annulus sides pressure drops, and overall cost have been estimated with deviations of 0.16%, 0.23%, 0.02%, and 0.003% via Model 1, 0.02%, 0.18%, 0.16%, and 0.15% via Model 2, respectively. Moreover, Model 1 results in the best mean squared errors for annulus side pressure drop and overall cost with the values of 2.54E-04 and 1.93E-04, correspondingly, whereas Model 2 yields the best values of 1.11E-04 and 1.90E-04 for overall heat transfer coefficient and tube side pressure drop, sequentially.
dc.description.sponsorshipNational Science and Technology Development Agency (NSTDA); Thailand Science Research and Innovation (TSRI) under Fundamental Fund [2022]; KMUTT; Thailand Science Research and Innovation (TSRI)
dc.description.sponsorshipThe fourth author acknowledges the Visiting Professorship from KMUTT. The fifth author acknowledges the National Science and Technology Development Agency (NSTDA) under the ?Research Chair Grant?, and the Thailand Science Research and Innovation (TSRI) under Fundamental Fund 2022.
dc.identifier.doi10.1016/j.csite.2022.102391
dc.identifier.issn2214-157X
dc.identifier.scopus2-s2.0-85138404455
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.csite.2022.102391
dc.identifier.urihttps://hdl.handle.net/11480/16107
dc.identifier.volume39
dc.identifier.wosWOS:000860488800009
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofCase Studies in Thermal Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectArtificial neural network
dc.subjectMulti-layer perceptron
dc.subjectLevenberg-Marquardt
dc.subjectOptimum velocity
dc.subjectDouble-pipe heat exchanger
dc.titlePrediction of heat transfer coefficient, pressure drop, and overall cost of double-pipe heat exchangers using the artificial neural network
dc.typeArticle

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