Performance comparison of TEGs for diverse variable leg geometry with the same leg volume

Khalil, ALkhadher; Elhassnaoui, Ahmed; Yadir, Said; Obbadi, Abdellatif; Errami, Youssef; Sahnoun, Smail

doi:10.1016/j.energy.2021.119967

Cited by 27 publications

(10 citation statements)

References 34 publications

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“…The trapezoidal leg geometry provided the highest performance, with a peak e ciency of 5.65% at a surface area of 110 110 mm 2 . Khalil et al, [20] conducted a performance evaluation of 5 thermoelectric leg geometries, just like Ibeagwu [21], under the same volume to nd which geometry provides the best performance. The study was conducted using a numerical model developed in COMSOL Multiphysics and all the computational models were subjected to the same temperature difference of 128 K. The authors reported that for a constant material volume in all the thermoelements, the rectangular-leg thermoelectric generator developed the highest power output and conversion e ciency of 0.11 W and 5.48%, respectively.…”

Section: 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WITHDRAWN: A prediction model for a concentrating solar thermoelectric generator using artificial neural networks and extreme learning machines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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Section: 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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“…Such CTE mismatch will result in large thermal stress, causing drastic breaks when subjected to frequent thermal cycling. Although thermal stress can be reduced somewhat by optimizing the structural factors such as the geometry of TE legs and the thickness of electrodes [ 27 , 28 ], this will increase the processing difficulty and assembly complexity of the module. In this regard, n- and p-type TE pellets with the same geometry are preferred and cuboidal TE legs are commonly used due to the ease of manufacturing.…”

Section: Resultsmentioning

confidence: 99%

High-efficiency and reliable same-parent thermoelectric modules using Mg3Sb2-based compounds

Jiang

Zhang

et al. 2023

National Science Review

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Thermoelectric modules can convert waste heat directly into useful electricity, providing a clean and sustainable way to use fossil energy more efficiently. Mg3Sb2-based alloys have recently attracted considerable interest from the thermoelectric community due to their nontoxic nature, abundance of constituent elements, and excellent mechanical and thermoelectric properties. However, robust modules based on Mg3Sb2 have progressed less rapidly. Here, we develop multiple-pair thermoelectric modules consisting of both n-type and p-type Mg3Sb2-based alloys. Thermoelectric legs based on the same parent fit in each other in terms of thermomechanical properties, facilitating module fabrication and ensuring low thermal stress. By adopting a suitable diffusion barrier layer and developing a new joining technique, an integrated all Mg3Sb2-based module demonstrates a high efficiency of 7.5% at a temperature difference of 380 K, exceeding the state-of-the-art same-parent thermoelectric modules. Moreover, the efficiency remains stable during 150 thermal cycling shocks (∼225 h), demonstrating excellent module reliability.

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“…For TEG applications under large temperature gradients, the concerns on the thermomechanical stresses and output performance motivate the efforts to exploit new architectures. [20,21] Various structures beyond the conventional π-shape have been investigated, including the linear design, [22][23][24] angled structure, [25] cylindrical-shaped [26] and tapered structures. [27] Recently, He et al [28] have made an excellent review relative to module architectures.…”

Section: Structural Designmentioning

confidence: 99%

Module-level design and characterization of thermoelectric power generator

Zhu¹,

Bai²,

Kim³

et al. 2022

Chinese Phys. B

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Thermoelectric power generation provides us the unique capability to explore the deep space and holds promise for harvesting the waste heat and providing a battery-free power supply for IoTs. The past years have witnessed massive progress in thermoelectric materials, while the module-level development is still lagged behind. We would like to shine some light on the module-level design and characterization of thermoelectric power generators (TEGs). In the module-level design, we review material selection, thermal management, and the determination of structural parameters. We also look into the module-level characterization, with particular attention on the heat flux measurement. Finally, the challenge in the optimal design and reliable characterization of thermoelectric power generators is discussed, together with a calling to establish a standard test procedure.

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Performance comparison of TEGs for diverse variable leg geometry with the same leg volume

Cited by 27 publications

References 34 publications

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

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

High-efficiency and reliable same-parent thermoelectric modules using Mg3Sb2-based compounds

Module-level design and characterization of thermoelectric power generator

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