Parametric model order reduction of a thermoelectric generator for electrically active implants

Yuan, Cheng; Jadhav, Onkar; Rudnyi, Evgenii B.; Hohlfeld, Dennis; Bechtold, Tamara

doi:10.1109/eurosime.2018.8369946

Cited by 6 publications

(8 citation statements)

References 20 publications

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“…Therefore, it is essential to generate a reduced model which preserves the parameters in the environmental boundary conditions. In the previous research, 22,27,28 the authors have shown that through the multivariate moment-matching pMOR methods, [23][24][25][26] it is possible to extract the convection film coefficient and the ambient temperature as the parameters in the reduced model. In this paper, based on Equation (10), the parametrized full-scale human torso linear thermal model was obtained as follows:…”

Section: Parametric Model Order Reductionmentioning

confidence: 99%

“…To obtain a parametric reduced order model, a parameter-independent projection subspaceṼ was constructed based on the transfer function of (16), which contains two physically-independent variables (s and h) 22 :…”

Section: Parametric Model Order Reductionmentioning

confidence: 99%

“…It had been proposed and demonstrated that 22,23,27,28 an accurate parameter-independent reduced model would be generated through neglecting the mixed moments and matching the first n 1 and n 2 moments with respect to s and h separately.…”

Section: Parametric Model Order Reductionmentioning

confidence: 99%

“…The conventional MOR algorithms, [18][19][20] in which the original full-scale FE model is projected onto a lower subspace constructed by the orthonormalized Krylov-subspace, have been successfully applied to linear thermal models of various microsystems. 21 Further, Yuan et al 22 applied parametric MOR (pMOR) to generate a parametrically reduced simplified human tissue model for investigating the impact of environmental parameters on the temperature distribution inside the human tissue. In accordance with the multivariate moment-matching algorithms, [23][24][25][26] a parameterindependent reduced order model of human tissue could be obtained.…”

Section: Introductionmentioning

confidence: 99%

“…pMOR has been successfully applied in various case studies. 22,27,28 Through the application of MOR and pMOR algorithms, a highly accurate and compact human tissue model could be generated for efficient simulations together with TEG model. Such simulations study a simplified human tissue model, which consist of three layers for the muscle, fat, and skin section.…”

Section: Introductionmentioning

confidence: 99%

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Towards efficient design optimization of a miniaturized thermoelectric generator for electrically active implants via model order reduction and submodeling technique

Yuan

Kreß

Sadashivaiah

et al. 2020

Numer Methods Biomed Eng

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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, projectionbased 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.bioheat modeling, energy harvesting, finite element method, implantable biomedical devices, model order reduction, thermoelectric generator ABBREVIATIONS: MOR, model order reduction; pMOR, parametric model order reduction; TEG, thermoelectric generator.

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Section: Parametric Model Order Reductionmentioning

confidence: 99%

Section: Parametric Model Order Reductionmentioning

confidence: 99%

Section: Parametric Model Order Reductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Towards efficient design optimization of a miniaturized thermoelectric generator for electrically active implants via model order reduction and submodeling technique

Yuan

Kreß

Sadashivaiah

et al. 2020

Numer Methods Biomed Eng

Self Cite

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Efficient design optimization of a miniaturized thermoelectric generator for electrically active implants based on parametric model order reduction

Rao

Yuan

Sadashivaiah

et al. 2021

Numer Methods Biomed Eng

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This research focuses on the design of a miniaturized thermoelectric generator (TEG) for electrically active implants. Its design optimization is performed using the finite element method. A simplified TEG model is obtained by replacing the thermocouple array with a single representative thermopile, which considers the number and fill factor of the thermocouples as parameters. Instead of rebuilding the geometry of a detailed model with multiple thermocouples, the simplified model adapts the material properties of its representative thermopile, facilitating design optimization. We extend the model by integrating the simplified TEG together with a housing inside a human tissue model for thermoelectric analysis. For computation efficiency and applicability of model order reduction (MOR), a thermal model is derived from the thermoelectric one, with the Peltier effect being considered through an effective thermal conductivity. Through parametric MOR, two parametric reduced‐order models are generated from the full‐scale thermoelectric and thermal model, respectively. Furthermore, we demonstrate the design optimization of TEG both in full‐scale and reduced‐order model for maximal power output and sufficient voltage output.

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Compact Modelling of Wafer Level Chip-Scale Package via Parametric Model Order Reduction

Zawra,

Zaal,

Soestbergen

et al. 2024

Mathematics in Industry

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Parametric model order reduction of a thermoelectric generator for electrically active implants

Cited by 6 publications

References 20 publications

Towards efficient design optimization of a miniaturized thermoelectric generator for electrically active implants via model order reduction and submodeling technique

Towards efficient design optimization of a miniaturized thermoelectric generator for electrically active implants via model order reduction and submodeling technique

Efficient design optimization of a miniaturized thermoelectric generator for electrically active implants based on parametric model order reduction

Compact Modelling of Wafer Level Chip-Scale Package via Parametric Model Order Reduction

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