Optimization analysis of a segmented thermoelectric generator based on genetic algorithm

Zhu, Lei; Li, Huaqi; Chen, Sen; Tian, Xiaogeng; Kang, Xiaoya; Jiang, Xinbiao; Qiu, Suizheng

doi:10.1016/j.renene.2020.04.120

Cited by 51 publications

(18 citation statements)

References 26 publications

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“…Nevertheless, the optimized geometry parameters are just the total height of the n-and p-type legs in the segmented thermoelectric device in most cases. 19,21 Furthermore, the papers that considered the individual heights of the segmented pins 17,18,20 failed to give detailed insights on how the individual and combined cross-sections can maximize the device performance; thus, providing an incomprehensive optimization study of the device. Up till date, no considerations were given to the individual and combined heights/cross-sectional areas of the pins that make up the device to see their effects on the device performance and choose which of the optimization approaches is most effective in maximising the device performance.…”

Section: Segmented Tegmentioning

confidence: 99%

“…They found that at 13 Suns, the STEG with 50% BiTe and 50% SKT provided the highest improvement of 59.12% in the power of the traditional unsegmented TEG. Zhu et al 19 optimized the geometry of a STEG using genetic algorithm. The hot and cold junctions of the STEG were exposed to temperatures of 700 and 350 K, respectively, and the geometry optimization was conducted on four configurations of STEGs.…”

Section: Introductionmentioning

confidence: 99%

“…The current papers on segmented TEG geometry optimization have presented novel insights to this filed. Nevertheless, the optimized geometry parameters are just the total height of the n ‐ and p ‐type legs in the segmented thermoelectric device in most cases 19,21 . Furthermore, the papers that considered the individual heights of the segmented pins 17,18,20 failed to give detailed insights on how the individual and combined cross‐sections can maximize the device performance; thus, providing an incomprehensive optimization study of the device.…”

Section: Introductionmentioning

confidence: 99%

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Geometry and stage number optimization of a concentrating solar multistage segmented thermoelectric generator by exploiting different optimization schemes

Maduabuchi

Alobaid

2022

Intl J of Energy Research

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Summary To further enhance the performance of the multi‐stage thermoelectric generator (TEG), this work conducts a comprehensive geometry and stage number optimization of a concentrating solar two‐stage TEG made of segmented pins in the second‐stage. The geometry optimization of the proposed device is comprehensive enough to include the individual and combined effects of the segmented pin's heights and cross‐sectional areas which were neglected in previous studies so as to select the best possible optimization parameter. Furthermore, the accuracy of the numerical results is ensured by accounting for temperature dependency in all thermoelectric materials while factoring radiative and convective losses associated with solar concentrating systems. Furthermore, a detailed stage number optimization of the proposed device is carried out by varying the number of stages from 1 to 4 to determine which stage number optimizes the device performance. Major results show that for three possible ways of optimizing the segment heights in the device, simultaneously increasing the n‐ and p‐high temperature material heights is the most effective, yielding a maximum efficiency of 9.28%. Furthermore, for 12 possible ways of optimizing the segment cross‐sectional areas, increasing the n‐ and p‐high temperature material areas is the most effective method, producing an efficiency of 13.82% at 3 mm2. Finally, increasing the number of stages is detrimental to the power output of the system while increasing its efficiency when only high temperature materials are used in the lower stages.

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Section: Segmented Tegmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Geometry and stage number optimization of a concentrating solar multistage segmented thermoelectric generator by exploiting different optimization schemes

Maduabuchi

Alobaid

2022

Intl J of Energy Research

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“…GA is a search procedure that mimics the natural selection process to find optimal solutions. GA is capable of dealing with a large number of design variables and finding global optima, and it has been extensively used in the optimisation of engineering structures [25][26][27][28]. In the GA, the better solution is searched through the evolution of a population of individuals (also referred as candidate solutions).…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Reliability-based design optimisation framework for wind turbine towers

et al. 2021

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“…For example, Kim et al developed a simplified mathematical model for a STEG [7]. Zhu et al reported a mathematical model even more accurate by simplifying the thermoelectric generator to a 1-D model, specializing in the variation of parameters on the legs of STEGs [8]. However, the accuracies of these mathematical models are often limited because several physical factors are ignored in the modeling.…”

Section: Introductionmentioning

confidence: 99%