Efficient design optimization of a miniaturized thermoelectric generator for electrically active implants based on parametric model order reduction,International Journal for Numerical Methods in Biomedical Engineering

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Efficient design optimization of a miniaturized thermoelectric generator for electrically active implants based on parametric model order reduction
International Journal for Numerical Methods in Biomedical Engineering ( IF 2.2 ) Pub Date : 2021-08-01 , DOI: 10.1002/cnm.3517
Yongchen Rao _{1,

2} , Chengdong Yuan _{1,

2} , Gunasheela Sadashivaiah ₂ , Dennis Hohlfeld ₂ , Tamara Bechtold _{1,

2}

Affiliation

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.

中文翻译：

基于参数化模型降阶的电活性植入体微型热电发电机高效设计优化

这项研究的重点是用于电活性植入物的微型热电发电机 (TEG) 的设计。其设计优化是使用有限元方法进行的。通过用单个具有代表性的热电堆代替热电偶阵列获得简化的 TEG 模型，该模型将热电偶的数量和填充因子视为参数。简化模型不是重建具有多个热电偶的详细模型的几何形状，而是调整其代表性热电堆的材料属性，从而促进设计优化。我们通过将简化的 TEG 与人体组织模型内的外壳集成在一起来扩展模型，以进行热电分析。为了模型降阶 (MOR) 的计算效率和适用性，从热电模型推导出热模型，通过有效的热导率考虑珀尔帖效应。通过参数MOR，分别从全尺寸热电模型和热模型生成两个参数降阶模型。此外，我们展示了 TEG 在全尺寸和降阶模型中的设计优化，以实现最大功率输出和足够的电压输出。

更新日期：2021-10-09

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