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Computational Optimization of Concentrating Solar Devices

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In terms of efficiency, perovskite solar cells have seen the most fastest increase in efficiency. While it took silicon over a decade to reach 20\% efficiency, perovskite cells reached similar efficiencies in a matter of years (\cite{NRELchart}), making them the fastest advancing solar technology in history. We will primarily focus on recent advancements in perovskite luminescent solar concentrators (LSCs) with the theoretically optimal values for perovskite LSCs of various compositions and thickness.

The Monte-Carlo simulation framework used to study the effectiveness of luminescent solar concentrators (LSCs) will be established in detail. We use a similar framework to that established by \cite{Sahin} for semiconductor nanoparticles. Computational efficiency is improved through implementation of a vectorized Monte Carlo simulation method. Perovskite LSCs are compared against previously used CdSe-CdTe quantum dots. We thereby establish perovskite as a viable, highly efficient LSC as published by \cite{Ghosh} and obtain ideal perovskite composition and thickness for optimal perovskite LSC performance as published by \cite{Boe}.

The other part of this thesis implements a deterministic ray tracing algorithm combined with appropriate optimization methods to evaluate the performance of segmented non-imaging solar thermal concentrators. Under user-specified parameters and constraints, new concentrator configurations are obtained which optimize theoretical efficiency. Such configurations have not yet been discussed in literature.

Pattern search, a gradient free optimization method, is employed to determine ideal segment positions. The presence of numerous local minima make gradient-based optimization solvers ineffective.

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