“…Noteworthy fluctuations in electric potential are discerned across a temperature range spanning from 20 to 57.5 • C. The graph illustrates a comprehensive spectrum of electric potential, ranging from 3.67 to 7.125 volts, underscoring the nuanced interplay between temperature variations and electrical behavior. This inverse correlation between temperature and electric potential is of paramount importance in the quest to optimize the performance of TEGs [27]. This highlights how crucial temperature control is to optimizing TEG per-formance because lower temperatures increase electric potential outputs and improve energy conversion efficiency.…”
Section: Resultsmentioning
confidence: 99%
“…The above discovery highlights the necessity of accurately adjusting electric potential in order to optimize TEG conversion efficiency and achieve optimal performance. Understanding this relationship is crucial for achieving optimal TEG performance [27]. The performance of Bi 2 Te 2.70 Se 0.30 TEMs depends on several key parameters.…”
This research employs the COMSOL Multiphysics software (COMSOL 6.2) to conduct rigorous simulations and assess the performance of a thermoelectric module (TEM) meticulously crafted with alumina (Al2O3), copper (Cu), and Bi2Te2.70Se0.30 thermoelectric (TE) materials. The specific focus is on evaluating diverse aspects of the Bi2Te2.70Se0.30 thermoelectric generator (TEG). The TEM design incorporates Bi2Te2.70Se0.30 for TE legs of the p- and n-type positioned among the Cu layers, Cu as the electrical conductor, and Al2O3 serving as an electrical insulator between the top and bottom layers. A thorough investigation is conducted into critical parameters within the TEM, which include arc length, electric potential, normalized current density, temperature gradient, total heat source, and total net energy rate. The geometric configuration of the square-shaped Bi2Te2.70Se0.30 TEM, measuring 1 mm × 1 mm × 2.5 mm with a 0.25 mm Al2O3 thickness and a 0.125 mm Cu thickness, is scrutinized. This study delves into the transport phenomena of TE devices, exploring the impacts of the Seebeck coefficient (S), thermal conductivity (k), and electrical conductivity (σ) on the temperature differential across the leg geometry. Modeling studies underscore the substantial influence of S = ±2.41 × 10−3 V/K, revealing improved thermal conductivity and decreased electrical conductivity at lower temperatures. The findings highlight the Bi2Te2.70Se0.30 TEM’s high potential for TEG applications, offering valuable insights into design and performance considerations crucial for advancing TE technology.
“…Noteworthy fluctuations in electric potential are discerned across a temperature range spanning from 20 to 57.5 • C. The graph illustrates a comprehensive spectrum of electric potential, ranging from 3.67 to 7.125 volts, underscoring the nuanced interplay between temperature variations and electrical behavior. This inverse correlation between temperature and electric potential is of paramount importance in the quest to optimize the performance of TEGs [27]. This highlights how crucial temperature control is to optimizing TEG per-formance because lower temperatures increase electric potential outputs and improve energy conversion efficiency.…”
Section: Resultsmentioning
confidence: 99%
“…The above discovery highlights the necessity of accurately adjusting electric potential in order to optimize TEG conversion efficiency and achieve optimal performance. Understanding this relationship is crucial for achieving optimal TEG performance [27]. The performance of Bi 2 Te 2.70 Se 0.30 TEMs depends on several key parameters.…”
This research employs the COMSOL Multiphysics software (COMSOL 6.2) to conduct rigorous simulations and assess the performance of a thermoelectric module (TEM) meticulously crafted with alumina (Al2O3), copper (Cu), and Bi2Te2.70Se0.30 thermoelectric (TE) materials. The specific focus is on evaluating diverse aspects of the Bi2Te2.70Se0.30 thermoelectric generator (TEG). The TEM design incorporates Bi2Te2.70Se0.30 for TE legs of the p- and n-type positioned among the Cu layers, Cu as the electrical conductor, and Al2O3 serving as an electrical insulator between the top and bottom layers. A thorough investigation is conducted into critical parameters within the TEM, which include arc length, electric potential, normalized current density, temperature gradient, total heat source, and total net energy rate. The geometric configuration of the square-shaped Bi2Te2.70Se0.30 TEM, measuring 1 mm × 1 mm × 2.5 mm with a 0.25 mm Al2O3 thickness and a 0.125 mm Cu thickness, is scrutinized. This study delves into the transport phenomena of TE devices, exploring the impacts of the Seebeck coefficient (S), thermal conductivity (k), and electrical conductivity (σ) on the temperature differential across the leg geometry. Modeling studies underscore the substantial influence of S = ±2.41 × 10−3 V/K, revealing improved thermal conductivity and decreased electrical conductivity at lower temperatures. The findings highlight the Bi2Te2.70Se0.30 TEM’s high potential for TEG applications, offering valuable insights into design and performance considerations crucial for advancing TE technology.
“…The equations used to evaluate the TEG 36‐38 and solar TEG 39‐41 performances are shown in this sub‐section. The equations governing thermoelectric phenomena 42‐44 are also clearly presented. All variables are defined in the nomenclature table.…”
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.
“…Meanwhile, the cold junction of the thermoelectric module is cooled using an appropriate cooling media like a fan or water jet. The temperature gradient between the thermoelectric generator necessitates a power generation which contributes to the increased power generated by the photovoltaic module [15][16][17]. The power and efficiency of the hybrid system becomes the sum of the powers and efficiencies of the individual photovoltaic and thermoelectric module.…”
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
