Identification of the most influential variables on the power generation of an automotive thermoelectric generator through a global sensitivity analysis
A methodology to determine the most influential independent input variables on the maximum power generation of an automotive thermoelectric generator is developed. A validated numerical model is used to predict the maximum power generation of a thermoelectric generator composed of several thermoelec...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Case Studies in Thermal Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X23008900 |
| _version_ | 1797655093229649920 |
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| author | Ivan R. Cózar Albert Massaguer Eduard Massaguer Andreu Cabot Toni Pujol |
| author_facet | Ivan R. Cózar Albert Massaguer Eduard Massaguer Andreu Cabot Toni Pujol |
| author_sort | Ivan R. Cózar |
| collection | DOAJ |
| description | A methodology to determine the most influential independent input variables on the maximum power generation of an automotive thermoelectric generator is developed. A validated numerical model is used to predict the maximum power generation of a thermoelectric generator composed of several thermoelectric modules installed in the exhaust pipe of an internal combustion engine. The Morris sensitivity analysis and the least absolute shrinkage and selection operator feature selection approach are employed to identify the most influential variables. The amount of independent variables selected to carry out the analysis are 18 and they are embedded in different fields such as hydraulic, thermal, electrical, chemical, geometrical and design. Results show that the most influential variables are the inlet temperature of the hot fluid and the Seebeck coefficient and electric resistance of the thermoelectric modules. The thickness of the thermoelectric modules has the least influence on the maximum power generation. These findings could be useful to other researchers to develop simpler mathematical models without compromising the accuracy. |
| first_indexed | 2024-03-11T17:09:09Z |
| format | Article |
| id | doaj.art-3c34a2d0d2c54cbea23c7c4a9bfd390c |
| institution | Directory Open Access Journal |
| issn | 2214-157X |
| language | English |
| last_indexed | 2024-03-11T17:09:09Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj.art-3c34a2d0d2c54cbea23c7c4a9bfd390c2023-10-20T06:39:36ZengElsevierCase Studies in Thermal Engineering2214-157X2023-11-0151103584Identification of the most influential variables on the power generation of an automotive thermoelectric generator through a global sensitivity analysisIvan R. Cózar0Albert Massaguer1Eduard Massaguer2Andreu Cabot3Toni Pujol4Departament de Ciència de Materials i Química Física, Universitat de Barcelona, C/Martí i Franqués 1, 08028, Barcelona, Spain; Corresponding author.Department of Mechanical Engineering and Industrial Construction, University of Girona, 17003 Girona, SpainDepartment of Mechanical Engineering and Industrial Construction, University of Girona, 17003 Girona, SpainCatalonia Institute for Energy Research (IREC), Sant Adrià de Besòs, 08930 Barcelona, Spain; ICREA, Pg. Lluis Companys, 08010 Barcelona, Catalonia, SpainDepartment of Mechanical Engineering and Industrial Construction, University of Girona, 17003 Girona, SpainA methodology to determine the most influential independent input variables on the maximum power generation of an automotive thermoelectric generator is developed. A validated numerical model is used to predict the maximum power generation of a thermoelectric generator composed of several thermoelectric modules installed in the exhaust pipe of an internal combustion engine. The Morris sensitivity analysis and the least absolute shrinkage and selection operator feature selection approach are employed to identify the most influential variables. The amount of independent variables selected to carry out the analysis are 18 and they are embedded in different fields such as hydraulic, thermal, electrical, chemical, geometrical and design. Results show that the most influential variables are the inlet temperature of the hot fluid and the Seebeck coefficient and electric resistance of the thermoelectric modules. The thickness of the thermoelectric modules has the least influence on the maximum power generation. These findings could be useful to other researchers to develop simpler mathematical models without compromising the accuracy.http://www.sciencedirect.com/science/article/pii/S2214157X23008900Waste heat recoveryThermoelectric generatorSensitivity analysisLASSO analysisMorris analysis |
| spellingShingle | Ivan R. Cózar Albert Massaguer Eduard Massaguer Andreu Cabot Toni Pujol Identification of the most influential variables on the power generation of an automotive thermoelectric generator through a global sensitivity analysis Case Studies in Thermal Engineering Waste heat recovery Thermoelectric generator Sensitivity analysis LASSO analysis Morris analysis |
| title | Identification of the most influential variables on the power generation of an automotive thermoelectric generator through a global sensitivity analysis |
| title_full | Identification of the most influential variables on the power generation of an automotive thermoelectric generator through a global sensitivity analysis |
| title_fullStr | Identification of the most influential variables on the power generation of an automotive thermoelectric generator through a global sensitivity analysis |
| title_full_unstemmed | Identification of the most influential variables on the power generation of an automotive thermoelectric generator through a global sensitivity analysis |
| title_short | Identification of the most influential variables on the power generation of an automotive thermoelectric generator through a global sensitivity analysis |
| title_sort | identification of the most influential variables on the power generation of an automotive thermoelectric generator through a global sensitivity analysis |
| topic | Waste heat recovery Thermoelectric generator Sensitivity analysis LASSO analysis Morris analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X23008900 |
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