Multi-objective optimum design of an alpha type Stirling engine using meta-models and co-simulation approach

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dc.authoridMugan, Ata/0000-0002-5293-7562|Yildiz, Cengiz/0000-0002-1000-9039
dc.authorwosidMugan, Ata/ABB-3728-2020
dc.contributor.authorYildiz, Cengiz
dc.contributor.authorBayata, Fatma
dc.contributor.authorMugan, Ata
dc.date.accessioned2024-07-18T20:42:35Z
dc.date.available2024-07-18T20:42:35Z
dc.date.issued2021
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractAn alpha type Stirling engine was optimized using meta-models considering uninterrupted electric power supply concurrently with natural gas combi boilers at homes during electricity interruptions. To predict and optimize the power and efficiency of the designed Stirling engine, an artificial neural network (ANN) model was trained as a meta-model. The ANN modeling method was used in solving a multi-objective Pareto optimization problem under some constraints to determine the optimum engine parameters. The design parameters were swept volume, hot and cold cylinder temperatures, gas constant, charge pressure and engine operation speed. Feed forward and Levenberg-Marquardt back propagation algorithms were evaluated to determine the best resulting network architecture that was found as 6-12-8-1. Subsequently, the fraction of variance (R-f) value was calculated close to 1 and the absolute mean error percentage (AMEP) was calculated as 6.07%. Trained ANN model was used in solving the multi-objective optimization problem. Using the optimum design parameters, the meta model predicted the power as 73.3 W and efficiency as 32.2%. Co-simulation approach was followed to verify the optimization results, and the nominal power output and corresponding efficiency were calculated using the Schmidt theory and the calibrated 1-D model created by the GT-Suite software that yield respectively, 144.6 W and 85.8 W for the power and 35% and 35.1% for the cycle efficiency. Consequently, the use of an ANN model in solving the associated optimization problem proved itself as a fast, accurate enough and powerful method to find the optimum design parameters and predict the engine performance.en_US
dc.identifier.doi10.1016/j.enconman.2021.113878
dc.identifier.issn0196-8904
dc.identifier.issn1879-2227
dc.identifier.scopus2-s2.0-85100692408en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.enconman.2021.113878
dc.identifier.urihttps://hdl.handle.net/11411/7348
dc.identifier.volume232en_US
dc.identifier.wosWOS:000623940800007en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofEnergy Conversion and Managementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectStirling Engine Simulationen_US
dc.subjectEngine Power Predictionsen_US
dc.subjectArtificial Neural Networken_US
dc.subjectMulti-Objective Optimum Engine Designen_US
dc.subjectSchmidt Theoryen_US
dc.titleMulti-objective optimum design of an alpha type Stirling engine using meta-models and co-simulation approachen_US
dc.typeArticleen_US

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