Surrogate based modeling and optimization of plasmonic thin film organic solar cells

Kaya, Mine; Hajimirza, Shima

doi:10.1016/j.ijheatmasstransfer.2017.11.044

Cited by 13 publications

(4 citation statements)

References 39 publications

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“…Specifically, if the utility function is known to be a sum of another sub-function over a range of a few input parameters, then a more efficient approach is to approximate the sub-function. This happens to be the case for TFSCs; the number of absorbed photons is the weighted integral of optical generation rate (absorptivity) at individual wavelengths, therefore single frequency optical characteristics can be modeled and approximated 13 – 15 .…”

Section: Introductionmentioning

confidence: 99%

Rapid Optimization of External Quantum Efficiency of Thin Film Solar Cells Using Surrogate Modeling of Absorptivity

Kaya

Hajimirza

2018

Sci Rep

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This paper uses surrogate modeling for very fast design of thin film solar cells with improved solar-to-electricity conversion efficiency. We demonstrate that the wavelength-specific optical absorptivity of a thin film multi-layered amorphous-silicon-based solar cell can be modeled accurately with Neural Networks and can be efficiently approximated as a function of cell geometry and wavelength. Consequently, the external quantum efficiency can be computed by averaging surrogate absorption and carrier recombination contributions over the entire irradiance spectrum in an efficient way. Using this framework, we optimize a multi-layer structure consisting of ITO front coating, metallic back-reflector and oxide layers for achieving maximum efficiency. Our required computation time for an entire model fitting and optimization is 5 to 20 times less than the best previous optimization results based on direct Finite Difference Time Domain (FDTD) simulations, therefore proving the value of surrogate modeling. The resulting optimization solution suggests at least 50% improvement in the external quantum efficiency compared to bare silicon, and 25% improvement compared to a random design.

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Section: Introductionmentioning

confidence: 99%

Rapid Optimization of External Quantum Efficiency of Thin Film Solar Cells Using Surrogate Modeling of Absorptivity

Kaya

Hajimirza

2018

Sci Rep

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“…Ye et al [34] proposed a novel ANN model for predicting convective heat transfer in sCO 2 . Kaya and Hajimirza designed a two-layer ANN surrogate model to estimate the optical absorptivity of the solar ultrathin organic cells [35,36]. Additional investigations of heat transfer using ANNs have also been reported [37,38].…”

Section: Brief Review Of the Monte Carlo Ray-trace Methodsmentioning

confidence: 99%

Artificial Neural Networks in Radiation Heat Transfer Analysis

Yarahmadi

Mahan

McFall

2020

Journal of Heat Transfer

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Abstract In the Monte Carlo ray-trace (MCRT) method, millions of rays are emitted and traced throughout an enclosure following the laws of geometrical optics. Each ray represents the path of a discrete quantum of energy emitted from surface element i and eventually absorbed by surface element j. The distribution of rays absorbed by the n surface elements making up the enclosure is interpreted in terms of a radiation distribution factor matrix whose elements represent the probability that energy emitted by element i will be absorbed by element j. Once obtained, the distribution factor matrix may be used to compute the net heat flux distribution on the walls of an enclosure corresponding to a specified surface temperature distribution. It is computationally very expensive to obtain high accuracy in the heat transfer calculation when high spatial resolution is required. This is especially true if a manifold of emissivities is to be considered in a parametric study in which each value of surface emissivity requires a new ray-trace to determine the corresponding distribution factor matrix. Artificial neural networks (ANNs) offer an alternative approach whose computational cost is greatly inferior to that of the traditional MCRT method. Significant computational efficiency is realized by eliminating the need to perform a new ray trace for each value of emissivity. The current contribution introduces and demonstrates through case studies estimation of radiation distribution factor matrices using ANNs and their subsequent use in radiation heat transfer calculations.

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“…Therefore intense computational FDTD simulations must be used for function evalaution, and hence the design is a time consuming global optimization 31 – 33 . Recently, we have shown that surrogate-based optimization methods can be used to solve optimization problems of this sort, and we have established their efficacy in several thin film design problems 34 – 36 . Consequently, the computational costs for completing optimizations were significantly reduced compared to traditional search methods.…”

Section: Introductionmentioning

confidence: 99%

Using a Novel Transfer Learning Method for Designing Thin Film Solar Cells with Enhanced Quantum Efficiencies

Kaya

Hajimirza

2019

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In this study a new method for design optimization is proposed that is based on “transfer learning”. The proposed framework improves the accuracy and efficiency of surrogate-based optimization. A surrogate model is an approximation to a costly black-box function that can be used for more efficient search of optimal points. When design specifications change, the objective function changes too. Therefore, there is a need for a new surrogate model. However, the concept of transfer learning can be applied to refit the new surrogate more efficiently. In other words insights from previous experiences can be applied to learning and optimizing the new function. We use the proposed method in a particular problem pertaining to the design of “thin film multilayer solar cells”, where the goal is to maximize the external quantum efficiency of photoelectric conversion. The results show that the accuracy of the surrogate model is improved by 2–3 times using the transfer learning approach, using only half as many training data points as the original model. In addition, by transferring the design knowledge from one particular set of materials to another similar set of materials in the thin film structure, the surrogate-based optimization is improved, and is it obtained with far less computational time.

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Surrogate based modeling and optimization of plasmonic thin film organic solar cells

Cited by 13 publications

References 39 publications

Rapid Optimization of External Quantum Efficiency of Thin Film Solar Cells Using Surrogate Modeling of Absorptivity

Rapid Optimization of External Quantum Efficiency of Thin Film Solar Cells Using Surrogate Modeling of Absorptivity

Artificial Neural Networks in Radiation Heat Transfer Analysis

Using a Novel Transfer Learning Method for Designing Thin Film Solar Cells with Enhanced Quantum Efficiencies

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