Multi‐dimensional optimization of a concentrated solar thermoelectric generator

Al‐Dahidi

Alnami

et al. 2022

Preprint

Self Cite

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.

Section: System Description and Materialsmentioning

confidence: 99%

“…where η o is the optical e ciency of the solar concentrator, assumed to vary in accordance with the expression provided in ref. [54], and A s is the surface area of the selective surface absorber, assumed ( ) ( )…”

Section: Hybrid Solar Thermoelectricmentioning

confidence: 99%

WITHDRAWN: A prediction model for a concentrating solar thermoelectric generator using artificial neural networks and extreme learning machines

Al‐Dahidi

Alnami

et al. 2022

Preprint

Self Cite

“…The incorporated system showing the solar photovoltaic module integrated with a thermoelectric module and the aluminum heat sink is shown in Figure 1(b). The thermoelectric module utilized in this work is a commercially available module produced by Melcor thermoelectric, HT 6-12-40, with geometry dimensions provided in our previous paper [43,44]. The thermoelectric legs are bismuth-telluride semiconductors [45], which convert the thermal energy sapped from the solar photovoltaic module back sheet to electrical power.…”

Section: Methodsmentioning

confidence: 99%

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

Alghamdi

International Journal of Energy Research

Albaker

et al. 2023

Self Cite

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.

“…The equations used to evaluate the TEG [36][37][38] and solar TEG [39][40][41] performances are shown in this sub-section. The equations governing thermoelectric phenomena [42][43][44] are also clearly presented.…”

Section: Governing and Performance Equationsmentioning

confidence: 99%

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

Intl J of Energy Research

Alobaid

2022

Self Cite

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.