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Towards efficient design optimization of a miniaturized thermoelectric generator for electrically active implants via model order reduction and submodeling technique
Author(s) -
Yuan Chengdong,
Kreß Stefanie,
Sadashivaiah Gunasheela,
Rudnyi Evgenii B.,
Hohlfeld Dennis,
Bechtold Tamara
Publication year - 2020
Publication title -
international journal for numerical methods in biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.741
H-Index - 63
eISSN - 2040-7947
pISSN - 2040-7939
DOI - 10.1002/cnm.3311
Subject(s) - finite element method , torso , thermoelectric generator , parametric statistics , heat transfer , computer science , materials science , bioheat transfer , reduction (mathematics) , mechanical engineering , optimal design , thermoelectric effect , engineering , mechanics , physics , mathematics , structural engineering , medicine , statistics , geometry , anatomy , thermodynamics , machine learning
Thermoelectric generators (TEG) convert the thermal energy into electrical energy and are under investigation as a power supply for medical implants. To improve the performance of TEG, the design optimization process through finite element model simulation is preferred by biomedical engineers. This paper aims to provide an efficient method of speeding up the design optimization process of TEG. A three‐dimensional realistic human torso model incorporating the TEG is investigated, where the internal heat transfer in human tissue is characterized by Pennes bioheat equation. In addition, convection, radiation, and evaporation effects at the skin surface are applied to identify the heat transfer effects between the human body and the environment. To speed up finite element simulation of the large‐scale human torso model, projection‐based model order reduction (MOR) is applied for generation of a compact but highly accurate model. Parametric MOR (pMOR) further enables generating a parameter‐independent compact model. For an efficient design optimization of TEG, this compact human torso model is applied within a thermal submodeling approach. Its temperature distribution results are back‐projected and used as boundary conditions for the TEG submodel. The achieved speed‐up in simulation time, demonstrated in this work, clearly indicates that the design optimization process of TEG is more efficient with the combination of MOR and submodeling techniques.