通过多方法热电耦合建模优化变截面积热电元件

Arsha K. Mamoozadeh, Sarah E. Wielgosz, Kevin Yu, F. Drymiotis, Matthew Barry
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引用次数: 1

摘要

为了最大限度地提高热电腿的热转换效率(η)或输出功率(P o),建立了热电耦合数学-数值模型来优化热电腿的单位长度截面积。为了采用这种优化,p型或n型支腿被分成均匀长度的段,其中电阻(R el)和导热系数(K)的乘积被最小化,以最大限度地提高每个单独分区的性能值(ZT)。R - el - K的最小值取决于在每个段上建立的温差,该温差使用TE一般能量方程(GEQ)的一维有限差分(FD)格式进行求解。TE GEQ包括所有相关现象-传导,焦耳,珀耳帖和汤姆逊效应-以及温度相关性质。通过一维热阻网络给出了FD格式的边界条件。单耦合器的电流输出由跨结的温度边界和TE分支的内阻决定,并将其显式耦合到TE GEQ以创建全耦合模型。该模型在ANSYS CFX中被验证为完全耦合的热电有限体积法模型。与传统的等面积优化工艺相比,该优化工艺的体积效率和体积功率输出分别提高了4.60%和3.75%。
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OPTIMIZATION OF VARIABLE CROSS-SECTIONAL AREA THERMOELECTRIC ELEMENTS THROUGH MULTI-METHOD THERMAL-ELECTRIC COUPLED MODELING
A well-posed thermal-electric coupled mathematical-numerical model to optimize the cross-sectional area per length of a thermoelectric (TE) leg is introduced to maximize thermal conversion efficiency ( η ) or power output ( P o ). To employ such optimization, the p - or n -type leg was divided into uniform length segments, wherein the product of the electrical resistance ( R el ) and thermal conductance ( K ) was minimized as to maximize the figure of merit ( ZT ) of each individual partition. The minimization of R el K was dependent upon the temperature difference established across each segment, which was resolved using a one-dimensional finite difference (FD) scheme of the TE general energy equation (GEQ). The TE GEQ included all pertinent phenomena —conduction, Joule, Peltier and Thomson effects —as well as temperature dependent properties. The boundary conditions of the FD scheme were provided via a one-dimensional thermal resistance network. The current output of the unicouple was determined by the temperature bounds across the junction and the internal resistance of the TE legs, and this was explicitly coupled to the TE GEQ to create a fully-coupled model. The proposed model was validated to a fully-coupled thermal-electric finite volume method model implemented in ANSYS CFX. The proposed optimization process yielded improvements in volumetric efficiency and volumetric power output of 4.60% and 3.75%, respectively, in comparison to conventional constant-area optimization processes.
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