Combinatory Finite Element and Artificial Neural Network Model for Predicting Performance of Thermoelectric Generator

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dc.contributor.authorKishore, Ravi Ananten
dc.contributor.authorMahajan, Roop L.en
dc.contributor.authorPriya, Shashanken
dc.contributor.departmentCenter for Energy Harvesting Materials and Systems (CEHMS)en
dc.contributor.departmentMechanical Engineeringen
dc.contributor.departmentInstitute for Critical Technology and Applied Scienceen
dc.date.accessioned2018-09-21T16:42:27Zen
dc.date.available2018-09-21T16:42:27Zen
dc.date.issued2018-08-24en
dc.date.updated2018-09-21T07:13:27Zen
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.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
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.doihttps://doi.org/10.3390/en11092216en
dc.identifier.urihttp://hdl.handle.net/10919/85074en
dc.language.isoenen
dc.publisherMDPIen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectthermoelectricen
dc.subjectbismuth tellurideen
dc.subjectTEGen
dc.subjectneural networken
dc.subjectANNen
dc.titleCombinatory Finite Element and Artificial Neural Network Model for Predicting Performance of Thermoelectric Generatoren
dc.title.serialEnergiesen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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Journal Articles, Multidisciplinary Digital Publishing Institute (MDPI)
Scholarly Works, Center for Energy Harvesting Materials and Systems (CEHMS)
Scholarly Works, Institute for Critical Technology and Applied Science (ICTAS)
Scholarly Works, Mechanical Engineering
Strategic Growth Area: Economical and Sustainable Materials (ESM)