Experimental performance of a photovoltaic-assisted solar parabolic dish thermoelectric system

Bamroongkhan, Pawatwong; Lertsatitthanakorn, C.; Sathapornprasath, Kitti; Soponronnarit, Somchart

doi:10.1016/j.csite.2021.101280

Cited by 12 publications

(3 citation statements)

References 22 publications

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“…Thermoelectric technology allows the conversion of temperature differences into electrical energy, which can be used for both power generation and cooling purposes. − In the application of flexible wearable thermoelectric (TE) devices, performance and flexibility are critical factors to consider. Therefore, extensive research has focused on utilizing heat sinks to enhance device performance. − For example, Eom et al developed a thermoelectric generators (TEG) with copper as a heat sink, which increased the output power of the TE device to 80 μW when worn by the human body . Cao et al utilized a water cooling technique to dissipate heat from the TEG, resulting in an increase in the temperature difference between the hot and cold ends from 2.0 to 5.6 °C, thereby enhancing the output power .…”

Section: Introductionmentioning

confidence: 99%

Long-Lasting Heat Dissipation of Flexible Heat Sinks for Wearable Thermoelectric Devices

Ding,

Sun,

Zhu

et al. 2024

ACS Appl. Mater. Interfaces

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Flexible wearable thermoelectric (TE) devices hold great promise for a wide range of applications in human thermal management and self-powered systems. Currently, the main challenge faced by flexible TE devices is the inadequate dissipation of heat, which hinders the maintenance of significant temperature differences over prolonged periods. Most existing heat sinks, being rigid in nature, compromise the overall flexibility of the device. Therefore, the challenge lies in maintaining device flexibility while ensuring effective heat dissipation. In this study, we developed a flexible phase-change material (FPCM) heat sink to address this issue and enhance the heat dissipation capabilities of TE devices (FPCM-TED). When used as a thermoelectric cooler (TEC), the FPCM heat sink efficiently absorbs heat from the hot end, enabling long-lasting and highperformance cooling of the TEC. This capability effectively reduces body temperature by up to 11.21 °C and can be sustained for at least 300 s. Additionally, when employed as a thermoelectric generator (TEG), the FPCM absorbs heat at the cold end, thereby increasing the temperature difference between the hot and cold ends and enhancing the output performance of the device. By integrating FPCM-TED into a fabric wristband, we successfully developed a self-powered wireless pedometer sensing system. This breakthrough lays a solid foundation for the application of wearable, smart clothing.

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Section: Introductionmentioning

confidence: 99%

Long-Lasting Heat Dissipation of Flexible Heat Sinks for Wearable Thermoelectric Devices

Ding,

Sun,

Zhu

et al. 2024

ACS Appl. Mater. Interfaces

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“…It was concluded that utilizing the beam splitter diminished the power generation of the thermoelectric module while increasing the power generation of the hybrid solar system. Bamroongkhan et al, [30] experimentally demonstrated the feasibility of a solar parabolic dish concentrating hybrid photovoltaicthermoelectric system to produce thermal and electrical energy. It was found that at ambient operating conditions of 37.8 °C and 874 W/m 2 , the proposed system could produce hot water at a capacity of 80 L per day at a heat transfer rate of 535.5 W/day.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Performance Prediction of a Solar Photovoltaic-Thermoelectric System with Various Crystalline Silicon Cell Types

Alghamdi

Maduabuchi

Albaker

et al. 2023

International Journal of Energy Research

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Since the solar photovoltaic-thermoelectric (PV-TE) is an upcoming technology, the current literature on PV-TEs have failed to thoroughly investigate the effects of different solar cell types on the PV-TE’s performance. Such parametric study becomes necessary since the properties of the solar cell, characterized by the cell temperature coefficient and reference efficiency, determine the overall performance of the PV-TE. Further, the design information obtained from numerical solvers is minimal due to the high time and computational energy required to optimize the photovoltaic-thermoelectric (PV-TE) performance. This problem severely affects the ease with which useful design information can be drawn from current methods to design high-performance PVs. Additionally, to reduce the complexity of the existing numerical model, the previous models have introduced some principal assumptions that have significantly affected the accuracy of the numerical results. Finally, the numerical model has neglected several real-life operating conditions since they increase the model complexity. For the first time, deep neural networks are proposed to predict the photovoltaic-thermoelectric performance designed with 3 different crystalline solar cells as a perfect replacement for the inefficient numerical methods used to analyze the hybrid system. After that, the data generated by the numerical solver is fed to an optimum 3-layer deep neural network with 20 neurons per layer to forecast the hybrid system’s performance efficiently. The training of the neural network is governed by Bayesian regularization and the performance of the model is evaluated using the mean squared error, coefficient of determination, and training time. Results show that the deep neural network accurately learned the results that took the conventional numerical solver 1,600 mins to generate in just 12 min and 27 s, making the proposed network 128.51 times faster than the traditional numerical method. Furthermore, the accuracy of the network is demonstrated by the low mean squared error of 3.34 × 10 − 8 and high training and testing regression of 100% and 99.98%, respectively.

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“…In a parallel vein, Bamroongkhan et al [19] made strides in demonstrating the dual functionality of the PV-TE systems. Through an experimental study, they showcased that a PV-TE system is capable of delivering hot water at 40 °C while concurrently generating 5.25 W of electrical power, given a temperature differential of 161.4 °C.…”

Section: Introductionmentioning

confidence: 99%

Smart Optimization of Semiconductors in Photovoltaic-Thermoelectric Systems Using Recurrent Neural Networks

Alghamdi

Maduabuchi

Okoli

et al. 2023

International Journal of Energy Research

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In the relentless pursuit of sustainable energy solutions, this study pioneers an innovative approach to integrating thermoelectric generators (TEGs) and photovoltaic (PV) modules within hybrid systems. Uniquely, it employs neural networks for an exhaustive analysis of a plethora of parameters, including a diverse spectrum of semiconductor materials, cooling film coefficients, TE leg dimensions, ambient temperature, wind speed, and PV emissivity. Leveraging a rich dataset, the neural network is meticulously trained, revealing intricate interdependencies among parameters and their consequential impact on power generation and the efficiencies of TEG, PV, and integrated PV-TE systems. Notably, the hybrid system witnesses a striking 23.1% augmentation in power output, escalating from 0.26 W to 0.32 W, and a 20% ascent in efficiency, from 14.68% to 17.62%. This groundbreaking research illuminates the transformative potential of integrating TEGs and PV modules and the paramountcy of multifaceted parameter optimization. Moreover, it exemplifies the deployment of machine learning as a powerful tool for enhancing hybrid energy systems. This study, thus, stands as a beacon, heralding a new chapter in sustainable energy research and propelling further innovations in hybrid system design and optimization. Through its novel approach, it contributes indispensably to the arsenal of clean energy solutions.

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Experimental performance of a photovoltaic-assisted solar parabolic dish thermoelectric system

Cited by 12 publications

References 22 publications

Long-Lasting Heat Dissipation of Flexible Heat Sinks for Wearable Thermoelectric Devices

Long-Lasting Heat Dissipation of Flexible Heat Sinks for Wearable Thermoelectric Devices

Machine Learning Performance Prediction of a Solar Photovoltaic-Thermoelectric System with Various Crystalline Silicon Cell Types

Smart Optimization of Semiconductors in Photovoltaic-Thermoelectric Systems Using Recurrent Neural Networks

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