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dc.contributor.authorKishore, Ravi Ananten
dc.contributor.authorMahajan, Roop L.en
dc.contributor.authorPriya, Shashanken
dc.date.accessioned2018-09-21T16:42:27Zen
dc.date.available2018-09-21T16:42:27Zen
dc.date.issued2018-08-24en
dc.identifier.citationKishore, R.A.; Mahajan, R.L.; Priya, S. Combinatory Finite Element and Artificial Neural Network Model for Predicting Performance of Thermoelectric Generator. Energies 2018, 11, 2216.en
dc.identifier.urihttp://hdl.handle.net/10919/85074en
dc.description.abstractThermoelectric generators (TEGs) are rapidly becoming the mainstream technology for converting thermal energy into electrical energy. The rise in the continuous deployment of TEGs is related to advancements in materials, figure of merit, and methods for module manufacturing. However, rapid optimization techniques for TEGs have not kept pace with these advancements, which presents a challenge regarding tailoring the device architecture for varying operating conditions. Here, we address this challenge by providing artificial neural network (ANN) models that can predict TEG performance on demand. Out of the several ANN models considered for TEGs, the most efficient one consists of two hidden layers with six neurons in each layer. The model predicted TEG power with an accuracy of ±0.1 W, and TEG efficiency with an accuracy of ±0.2%. The trained ANN model required only 26.4 ms per data point for predicting TEG performance against the 6.0 minutes needed for the traditional numerical simulations.en
dc.format.mimetypeapplication/pdfen
dc.languageen_USen
dc.publisherMDPIen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleCombinatory Finite Element and Artificial Neural Network Model for Predicting Performance of Thermoelectric Generatoren
dc.typeArticle - Refereeden
dc.date.updated2018-09-21T07:13:27Zen
dc.contributor.departmentCenter for Energy Harvesting Materials and Systems (CEHMS)en
dc.contributor.departmentMechanical Engineeringen
dc.contributor.departmentInstitute for Critical Technology and Applied Science (ICTAS)en
dc.title.serialEnergiesen
dc.identifier.doihttps://doi.org/10.3390/en11092216en
dc.type.dcmitypeTexten


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Creative Commons Attribution 4.0 International
License: Creative Commons Attribution 4.0 International