Prediction of performance parameters of a hermetic reciprocating compressor under different discharge lift limiter heights by machine learning

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dc.authorid0000-0003-3157-1992en_US
dc.authorid0000-0001-9297-8134en_US
dc.contributor.authorBacak, Aykut
dc.contributor.authorÇolak, Andaç Batur
dc.contributor.authorDalkılıç, Ahmet Selim
dc.date.accessioned2024-05-20T08:44:41Z
dc.date.available2024-05-20T08:44:41Z
dc.date.issued2024en_US
dc.departmentRektörlük, Bilişim Teknolojileri Uygulama ve Araştırma Merkezien_US
dc.description.abstractThe research examines the complex correlation between discharge valve properties in severe temperature circumstances, ranging from 54.4 degrees C to -23.3 degrees C, in accordance with ASHRAE operational guidelines. The design parameters include examining valve thicknesses of 0.127, 0.152, 0.178, and 0.2 mm, together with lengths of 14.722, 16.222, and 17.722 mm, at compressor speeds of 1300, 2100, and 3000 rpm. An artificial neural network (ANN) is used to replicate the output properties of a hermetic reciprocating compressor, which include the ratio of cooling capacity to compression power and volumetric efficiency. One hundred and eleven numerically recorded datasets are used to train the developed ANN model. The model is trained using 77 datasets, validated using 17 datasets, and tested using 17 datasets. The LM-type ANN approach is used to train the multilayer perception neural network, which consists of a hidden layer with 15 neurons. Given the proximity of the margin of deviations (MoDs) to the 0% deviation line, the variances between the ANN and fluid-structure interaction outcomes for the cooling capacity to compression power ratio and volumetric efficiency are insignificant. The average figures for the MoD output have been calculated as -0.18% and 0.06, respectively. Not only do the data points lie on the line, indicating a 0% error, but they also fall inside the interval, indicating a 10% error. In addition, the mean squared error and correlation coefficient values for the ANN model that was created are 2.04E-03 and 0.99853, respectively.en_US
dc.identifier.doi10.1177/09544089241249854en_US
dc.identifier.scopus2-s2.0-85192247916en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/7273
dc.identifier.urihttps://doi.org/10.1177/09544089241249854
dc.identifier.wosWOS:001214427000001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSAGE Publications Ltd.en_US
dc.relation.ispartofProceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Başka Kurum Yazarıen_US
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_US
dc.subjectMachine learning; Artificial neural network; Hermetic reciprocating compressor; Energy efficiency; Refrigeratoren_US
dc.titlePrediction of performance parameters of a hermetic reciprocating compressor under different discharge lift limiter heights by machine learningen_US
dc.typeArticleen_US

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