Geometric optimization for the thermoelectric generator with variable cross-section legs by coupling finite element method and optimization algorithm

He, Kui; Xiao, Liehui; Yuan, Wu-Zhi; Huang, Si-Min

doi:10.1016/j.renene.2021.11.016

Cited by 46 publications

(13 citation statements)

References 27 publications

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“…The reason may be attributed to the different materials with the distinct property of electrical and thermal resistances, which strongly changes the internal resistance. This is consistent with the results reported by Ge et al [35], where it is demonstrated that variable cross-section may cause internal resistance variation. The corresponding temperature and potential contours are shown in Figures 14 and 15, respectively.…”

Section: Physics Behind the Advantageous Performancesupporting

confidence: 93%

Advantage of a Thermoelectric Generator with Hybridization of Segmented Materials and Irregularly Variable Cross-Section Design

et al. 2022

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As a direct energy converter between heat and electricity, thermoelectric generators (TEGs) have potential applications including recovery of waste heat, and solar thermoelectric power generation. Geometric parameter and material are two critical factors to improve the TEG performance. However, the strategies base on structure design and material development are always separated. There are limited studies on the effects of consolidating them simultaneously. Here, an idea of segmented material coupled with irregularly variable cross-section design was conceived to further improve the TEG output power. The performance of TEGs with rectangular leg, segmented leg, variable cross-sectional leg, and the new design are compared. The coupling effects between various mechanisms are revealed, which are responsible for the superior performance provided by the developed design. Based on this knowledge, a multiparameters optimization was performed through the genetic algorithm to reach the optimal combination of design parameters. The results show that, with a constraint of certain material volume, the optimal performance of the TEG can be further enhanced by coupling segmented material and irregularly variable cross-section design. An improvement of 51.71% was achieved when compared with the conventional counterpart. This work offers a simple route to enhance the TEG performance when the device materials are specified, without an increase in the cost of manufacturing.

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Section: Physics Behind the Advantageous Performancesupporting

confidence: 93%

Advantage of a Thermoelectric Generator with Hybridization of Segmented Materials and Irregularly Variable Cross-Section Design

et al. 2022

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“…Although varying the thermoelectric leg geometry and segmenting the thermoelectric legs have been found to independently improve the performance of thermoelectric generators [32,33], very few research efforts have been made to develop a hybrid combination of both strategies to further enhance the e ciency and power output of the device. As far back as 2018, Liu et al, [34] was the rst to propose segmented thermoelectric generators with variable area leg geometry con guration, employing a numerical model developed in COMSOL Multiphysics to verify if the proposed device would outperform the conventional variable area leg thermoelectric generator.…”

Section: Segmented Variable Area Leg Thermoelectric Generatorsmentioning

confidence: 99%

WITHDRAWN: A prediction model for a concentrating solar thermoelectric generator using artificial neural networks and extreme learning machines

Maduabuchi

Al‐Dahidi

Alnami

et al. 2022

Preprint

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The current numerical simulation tools used to optimize the performance of concentrating solar thermoelectric generators are extremely time consuming, and consequently require expensive computational energies. Furthermore, they are incapable of considering the effects of diverse real-life operating conditions on the performance of the system. Additionally, they sometimes neglect temperature dependency in the thermoelectric semiconductors and base their studies on just unicouple thermoelectric cells to avoid the further complexity of the numerical computation. These factors limit the flexibility of optimization studies that can be conducted on solar thermoelectrics; hence, limiting the insights that can be drawn to design high performing solar thermoelectric generators. This work is the first of its kind to introduce artificial neural networks and extreme learning machines as a substitute to these numerical methods to accelerate and ease the design process of solar thermoelectric generators. The data generation process is conducted using a 3-dimensional numerical model developed in ANSYS numerical solver and the optimized parameters include the high-temperature material content, semiconductor height and area, concentrated solar irradiance, cooling film coefficient, wind speed, and ambient temperature – on the system performance. A full-scale customized thermoelectric module comprising 127 thermocouples is designed and integrated in an optical concentrator for solar power generation while considering temperature dependency in all thermoelectric materials. Results depict that the geometry and operating condition optimization improved the system power and efficiency by 42.02% and 82.23%, respectively. Furthermore, the artificial neural network had the highest regression of 95.82% with the least mean squared error of 2.71 \(\times\) 10− 5 in learning the numerical-generated data set while performing 389 and 203 times faster than the numerical method in forecasting the system power and efficiency, respectively. Finally, methods of manufacturing the optimized thermoelectric module using 3-dimensional printing are discussed.

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“…A thermal-electric coupled mixed-method algorithm that predicts TEG performance and optimizes the crosssectional area along the leg length in order to optimize power output or thermal conversion efficiency [2] This work proposes an optimization study to maximize the output power of variable cross-section TEGs for solar energy utilization by coupling finite element method (FEM) and optimization algorithm. Six geometric variables along with the external load resistance are optimized by genetic algorithm (GA) and particle swarm optimization (PSO).…”

Section: Reference Characteristics [1]mentioning

confidence: 99%

“…One parameter used in the geometric method is the cross-sectional area of the leg [1]. For example, it is possible to maximize the output power of the TEG as a function of the variable cross-section; in fact, in [2], it was stated that "The geometry of the TEG has a vital impact on the thermal resistance and the electrical resistance, influencing its integral performance".…”

Section: Introductionmentioning

confidence: 99%

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Design of Thermoelectric Generators and Maximum Electrical Power Using Reduced Variables and Machine Learning Approaches

Vargas-Almeida,

Olivares-Robles,

Andrade-Vallejo

2023

Energies

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This work aims to contribute to studies on the geometric optimization of thermoelectric generators (TEGs) through a combination of the reduced variables technique and supervised machine learning. The architecture of the thermoelectric generators studied, one conventional and the other segmented, was determined by calculating the cross-sectional area and length of the legs, and applying reduced variables approximation. With the help of a supervised machine learning algorithm, the values of the thermoelectric properties were predicted, as were those of the maximum electrical power for the other temperature values. This characteristic was an advantage that allowed us to obtain approximate results for the electrical power, adjusting the design of the TEGs when experimental values were not known. The proposed method also made it possible to determine the optimal values of various parameters of the legs, which were the ratio of the cross-sectional areas (Ap/An), the length of the legs (l), and the space between the legs (H). Aspects such as temperature-dependent thermoelectric properties (Seebeck coefficient, electrical resistivity, and thermal conductivity) and the metallic bridge that connects the legs were considered in the calculations for the design of the TEGs, obtaining more realistic models. In the training phase, the algorithm received the parameter (H) and an operating temperature value as input data, to predict the corresponding value of the maximum power produced. This calculation was performed for conventional and segmented systems. Recent advances have opened up the possibility of applying an algorithm for designing conventional and segmented thermocouples based on the reduced variables approach and incorporating a supervised machine learning computational technique.

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Geometric optimization for the thermoelectric generator with variable cross-section legs by coupling finite element method and optimization algorithm

Cited by 46 publications

References 27 publications

Advantage of a Thermoelectric Generator with Hybridization of Segmented Materials and Irregularly Variable Cross-Section Design

Advantage of a Thermoelectric Generator with Hybridization of Segmented Materials and Irregularly Variable Cross-Section Design

WITHDRAWN: A prediction model for a concentrating solar thermoelectric generator using artificial neural networks and extreme learning machines

Design of Thermoelectric Generators and Maximum Electrical Power Using Reduced Variables and Machine Learning Approaches

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