Prediction of performance parameters of a hermetic reciprocating compressor applying an artificial neural network

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dc.authorid0000-0001-9297-8134en_US
dc.contributor.authorBacak, Aykut
dc.contributor.authorÇolak, Andaç Batur
dc.contributor.authorDalkılıç, Ahmet Selim
dc.date.accessioned2024-03-14T11:55:03Z
dc.date.available2024-03-14T11:55:03Z
dc.date.issued2024en_US
dc.departmentRektörlük, Bilişim Teknolojileri Uygulama ve Araştırma Merkezien_US
dc.description.abstractThe efficiency of the compressor's thermodynamic, mechanical, and electric motors can be increased by minimizing their losses. Based on the design characteristics of the compressor, the current study proposes an artificial neural network (ANN) model to forecast the mass flow rate, cooling capacity, compression power, and discharge line loss of the hermetic reciprocating compressor as outputs. This study uses experimental and numerical results obtained by the fluid–structure interaction (FSI) method, and the whole findings are used as inputs for the machine learning method. Using 36 numerical and 3 experimental data sets, a multilayer network is created with input parameters defining mass flow rate, cooling capacity, compression power, and discharge line energy loss. The input parameters for the network model are the compressor's design parameters, which include the compressor speed, discharge valve thickness, and discharge valve length. When comparing the experimental and FSI methods for compressor speeds of 1300, 2100, and 3000?rpm, the FSI study's mass flow rate and cooling capacity parameters converged at 11.9%, 9.1%, and 9.3%, respectively, to their actual values. The convergence rate to experimental compression power data was ?0.3%, 2.6%, and 11%. The numerical-experimental deviation for discharge line energy losses is 14.9%, ?3.6%, and 7.8%, respectively, for 1300, 2100, and 3000?rpm. With the Levenberg–Marquardt (LM) ANN model, which is used in this study, the average squared error value is calculated as 1.75E-02 and the R2 value is calculated as 0.99976. ANN model can predict outputs with average deviation rates of less than 0.88%. It is seen that results obtained with the ANN method provide high convergence on the experimental and FSI results, while the results obtained with the regression model deviate from the compressor exhaust line results by more than ±20%, and the ANN method is more successful than regression.en_US
dc.identifier.doi10.1177/09544089241233990en_US
dc.identifier.scopus2-s2.0-85186591286en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/7165
dc.identifier.urihttps://doi.org/10.1177/09544089241233990
dc.identifier.wosWOS:001176793300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSAGE Publications Ltden_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.subjectartificial neural network (ANN); efficiency; fluid-structure interaction (FSI); Reciprocating compressor; thermodynamic lossen_US
dc.titlePrediction of performance parameters of a hermetic reciprocating compressor applying an artificial neural networken_US
dc.typeArticleen_US

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