Multi-objective genetic algorithm for the optimization of a flat-plate solar thermal collector

Mayer, A.; Gaouyat, Lucie; Nicolay, Delphine; Carletti, Timotéo; Deparis, Olivier

doi:10.1364/oe.22.0a1641

Cited by 14 publications

(12 citation statements)

References 24 publications

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“…Four parameters have to be optimized: the period P UC of the unit cell which corresponds to the pitch of the pattern, the filling fraction f UC of the holes at the top c-Si surface, and the thicknesses of the ARC and BR layers, respectively t ARC and t BR . The optimization of these parameters was performed by a Genetic Algorithm (GA) method [46][47][48][49] thanks to a home-made code (see [50] for details). The idea consists in working with a population of 100 individuals, each individual being representative of a given set of parameters which are actually represented by a string of binary digits (DNA).…”

Section: Resultsmentioning

confidence: 99%

A fair comparison between ultrathin crystalline-silicon solar cells with either periodic or correlated disorder inverted pyramid textures

Müller¹,

Herman²,

Mayer³

et al. 2015

Opt. Express

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Fabrication of competitive solar cells based on nano-textured ultrathin silicon technology is challenging nowadays. Attention is paid to the optimization of this type of texture, with a lot of simulation and experimental results published in the last few years. While previous studies discussed mainly the local features of the surface texture, we highlight here the importance of their filling fraction. In this work, we focus on a fair comparison between a technologically realizable correlated disorder pattern of inverted nano-pyramids on an ultrathin crystalline-silicon layer, and its periodically patterned counterpart. A fair comparison is made possible by defining an equivalent periodic structure for each hole filling fraction. Moreover, in order to be as realistic as possible, we consider patterns that could be fabricated by standard patterning techniques: hole-mask colloidal lithography, nanoimprint lithography and wet chemical etching. Based on numerical simulations, we show that inverted nano-pyramid patterns with correlated disorder provide typically greater efficiency than their periodic counterparts. However, the hole filling fraction of the etched pattern plays a crucial role and may limit the benefits of the correlated disorder due to experimental restrictions on pattern fabrication.

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Section: Resultsmentioning

confidence: 99%

A fair comparison between ultrathin crystalline-silicon solar cells with either periodic or correlated disorder inverted pyramid textures

Müller¹,

Herman²,

Mayer³

et al. 2015

Opt. Express

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“…Off the strength of those results, the main goal of this study is to further optimize the rational structure of PSCs to be closer to experimental reality by using a genetic algorithm (GA). The GA finds a global optimum or a high-quality solution within a multi-dimensional parameter space for a dedicated problem, such as light absorption [34][35][36]. This procedure enables the smart exploration of a larger range of parameters and is particularly relevant for the absorption inside the PSCs (e.g., the thicknesses of the different layers or the radius of the spheres).…”

Section: Introductionmentioning

confidence: 99%

Opal-Like Photonic Structuring of Perovskite Solar Cells Using a Genetic Algorithm Approach

et al. 2020

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Light management is an important area of photovoltaic research, but little is known about it in perovskite solar cells. The present work numerically studies the positive effect of structuring the photo-active layer of perovskite material. This structuration consists of a hybrid absorbing layer made of an uniform part and an opal-like part. A genetic algorithm approach allows us to determine the optimal combination among more than 1.4 × 10 9 potential combinations. The optimal combination provides an internal quantum efficiency of 98.1%, nearly 2% higher than for an equivalent unstructured photo-active layer. The robustness of the optimum against potential experimental deviations, as well as the angular dependency of the proposed structure, are examined in the present study.

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“…In previous work [11,[17][18], we used genetic algorithms to address optical engineering problems. In Ref.…”

Section: Resultsmentioning

confidence: 99%

“…In Ref. 17, we used a multi-objective genetic algorithm to improve the performance of a solar thermal collector that consists of an aluminum waffle-shaped structure, with conformal coatings of NiCrO x (cermet) and SnO 2 (anti-reflection coating). This problem was characterized by two objectives: (i) maximizing the absorption of solar radiation, and (ii) minimizing the emission of thermal radiation.…”

Section: Resultsmentioning

confidence: 99%

Genetic Algorithms: An Evolutionary Approach to Optical Engineering

Mayer

2018

JST

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We present a Genetic Algorithm that we developed to address optimization problems in optical engineering. Our objective is to determine the global optimum of a problem ideally by a single run of the genetic algorithm. We want also to achieve this objective with a reasonable use of computational resources. In order to accelerate the convergence of the algorithm, we establish generation after generation a quadratic approximation of the fitness in the close neighborhood of the best-so-far individual. We then inject in the population an individual that corresponds to the optimum of this approximation. We also use randomly-shifted Gray codes when applying mutations in order to achieve a better exploration of the parameter space. We provide automatic settings for the technical parameters of our algorithm and apply it to typical benchmark problems in 5, 10 and 20 dimensions. We show that the global optimum of these problems can be determined with a probability of success in one run of the order of 95-97 % and an average number of fitness evaluations of the order of 400-750×n, where n refers to the number of parameters to determine. We finally comment some applications of this algorithm to real-world engineering problems.

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Multi-objective genetic algorithm for the optimization of a flat-plate solar thermal collector

Cited by 14 publications

References 24 publications

A fair comparison between ultrathin crystalline-silicon solar cells with either periodic or correlated disorder inverted pyramid textures

A fair comparison between ultrathin crystalline-silicon solar cells with either periodic or correlated disorder inverted pyramid textures

Opal-Like Photonic Structuring of Perovskite Solar Cells Using a Genetic Algorithm Approach

Genetic Algorithms: An Evolutionary Approach to Optical Engineering

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