Design of Selective TPV Thermal Emitters Based on Bayesian Optimization Nesting Simulated Annealing

It is vital to further improve the design of TPV thermal emitters since the energy efficiency of thermophotovoltaic (TPV) systems is still not adequately high. In this paper, we propose a novel evaluator for the optimization of TPV thermal emitters, namely the percentage of effective figure (PEF) to...

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Main Authors: Zejia Liu, Zigui Zhang, Peifeng Xie, Zibo Miao
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Energies
Subjects:
Online Access:https://www.mdpi.com/1996-1073/16/1/416
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author Zejia Liu
Zigui Zhang
Peifeng Xie
Zibo Miao
author_facet Zejia Liu
Zigui Zhang
Peifeng Xie
Zibo Miao
author_sort Zejia Liu
collection DOAJ
description It is vital to further improve the design of TPV thermal emitters since the energy efficiency of thermophotovoltaic (TPV) systems is still not adequately high. In this paper, we propose a novel evaluator for the optimization of TPV thermal emitters, namely the percentage of effective figure (PEF) to replace the figure of merit (FOM). The associated algorithm, Bayesian optimization nesting simulated annealing (BOnSA), is developed to achieve better performance. By searching throughout the whole parameter space and then optimizing in a reduced space, BOnSA can lead to a satisfactory solution numerically for GaSb photovoltaic (PV) cells. When designing the emitter, the aperiodic material structure with an anti-reflection substructure and Fabry–Perot etalon is constructed from the material candidates. In particular, one of the optimal structures determined by BOnSA is {SiO<sub>2</sub>, ZnS, Ge, MgF<sub>2</sub>, W, Si, SiO<sub>2</sub>, W} with the value of <inline-formula><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>PEF</mi><mo>=</mo><mn>0.822</mn></mrow></semantics></math></inline-formula>, which is better than the previous work by comparison. Moreover, by applying BOnSA to various structures, we have obtained higher values of PEF with less time cost, which thus verifies the efficiency and scalability of BOnSA. The results of our paper show that BOnSA provides an effective approach to the thickness optimization problem and that BOnSA is applicable in other relevant scenarios.
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spelling doaj.art-e0ac38394c794084a50f4d10a74bfd162023-11-16T15:18:52ZengMDPI AGEnergies1996-10732022-12-0116141610.3390/en16010416Design of Selective TPV Thermal Emitters Based on Bayesian Optimization Nesting Simulated AnnealingZejia Liu0Zigui Zhang1Peifeng Xie2Zibo Miao3School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, ChinaSchool of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, ChinaSchool of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, ChinaSchool of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, ChinaIt is vital to further improve the design of TPV thermal emitters since the energy efficiency of thermophotovoltaic (TPV) systems is still not adequately high. In this paper, we propose a novel evaluator for the optimization of TPV thermal emitters, namely the percentage of effective figure (PEF) to replace the figure of merit (FOM). The associated algorithm, Bayesian optimization nesting simulated annealing (BOnSA), is developed to achieve better performance. By searching throughout the whole parameter space and then optimizing in a reduced space, BOnSA can lead to a satisfactory solution numerically for GaSb photovoltaic (PV) cells. When designing the emitter, the aperiodic material structure with an anti-reflection substructure and Fabry–Perot etalon is constructed from the material candidates. In particular, one of the optimal structures determined by BOnSA is {SiO<sub>2</sub>, ZnS, Ge, MgF<sub>2</sub>, W, Si, SiO<sub>2</sub>, W} with the value of <inline-formula><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>PEF</mi><mo>=</mo><mn>0.822</mn></mrow></semantics></math></inline-formula>, which is better than the previous work by comparison. Moreover, by applying BOnSA to various structures, we have obtained higher values of PEF with less time cost, which thus verifies the efficiency and scalability of BOnSA. The results of our paper show that BOnSA provides an effective approach to the thickness optimization problem and that BOnSA is applicable in other relevant scenarios.https://www.mdpi.com/1996-1073/16/1/416selective TPV thermal emittersthe percentage of effective figureFabry–Perot etalonsimulated annealingBayesian optimization
spellingShingle Zejia Liu
Zigui Zhang
Peifeng Xie
Zibo Miao
Design of Selective TPV Thermal Emitters Based on Bayesian Optimization Nesting Simulated Annealing
Energies
selective TPV thermal emitters
the percentage of effective figure
Fabry–Perot etalon
simulated annealing
Bayesian optimization
title Design of Selective TPV Thermal Emitters Based on Bayesian Optimization Nesting Simulated Annealing
title_full Design of Selective TPV Thermal Emitters Based on Bayesian Optimization Nesting Simulated Annealing
title_fullStr Design of Selective TPV Thermal Emitters Based on Bayesian Optimization Nesting Simulated Annealing
title_full_unstemmed Design of Selective TPV Thermal Emitters Based on Bayesian Optimization Nesting Simulated Annealing
title_short Design of Selective TPV Thermal Emitters Based on Bayesian Optimization Nesting Simulated Annealing
title_sort design of selective tpv thermal emitters based on bayesian optimization nesting simulated annealing
topic selective TPV thermal emitters
the percentage of effective figure
Fabry–Perot etalon
simulated annealing
Bayesian optimization
url https://www.mdpi.com/1996-1073/16/1/416
work_keys_str_mv AT zejialiu designofselectivetpvthermalemittersbasedonbayesianoptimizationnestingsimulatedannealing
AT ziguizhang designofselectivetpvthermalemittersbasedonbayesianoptimizationnestingsimulatedannealing
AT peifengxie designofselectivetpvthermalemittersbasedonbayesianoptimizationnestingsimulatedannealing
AT zibomiao designofselectivetpvthermalemittersbasedonbayesianoptimizationnestingsimulatedannealing