Precise Derivations of Radiative Properties of Porous Media Using Renewal Theory

This work uses the mathematical machinery of Renewal/Ruin (surplus risk) theory to derive preliminary explicit estimations for the radiative properties of dilute and disperse porous media otherwise only computable accurately with Monte Carlo Ray Tracing (MCRT) simulations. Although random walk and Levy processes have been extensively used for modeling diffuse processes in various transport problems and porous media modeling, relevance to radiation heat transfer is scarce, as opposed to other problems such as probe diffusion and permeability modeling. Furthermore, closed form derivations that lead to tangible variance reduction in MCRT are widely missing. The particular angle of surplus risk theory provides a richer apparatus to derive directly related quantities. To the best of the authors' knowledge, the current work is the only work relating the surplus risk theory derivations to explicit computations of ray tracing results in porous media. The paper contains mathematical derivations of the radiation heat transfer estimates using the extracted machinery along with proofs and numerical validation using MCRT

Optimization, design and performance analysis of light trapping structures in thin film solar cells

textSolar cells are at the frontier of renewable energy technologies. Photovoltaic energy is clean, reusable, can be used anywhere in our solar system and can be very well integrated with power distribution grids and advanced technological systems. Thin film solar cells are a class of solar cells that offer low material cost, efficient fabrication process and compatibility with advanced electronics. However, as of now, the conversion efficiency of thin film solar cells is inferior to that of thick crystalline cells. Research efforts to improve the performance bottlenecks of thin film solar cells are highly motivated. A class of techniques towards this goal is called light trapping methods, which aims at improving the spectral absorptivity of a thin film cell by using surface texturing. The precise mathematical and physical characterization of these techniques is very challenging. This dissertation proposes a numerical and computational framework to optimize, design, and fabricate efficient light trapping structures in thin film solar cells, as well as methods to verify the fabricated designs. The numerical framework is based on the important "inverse optimization" technique, which is very is widely applicable to engineering design problems. An overview of the state-of-the-art thin film technology and light trapping techniques is presented in this thesis. The inverse problem is described in details with numerous examples in engineering applications, and is then applied to light trapping optimization. The proposed designs are studied for sensitivity analysis and fabrication error, as other aspects of the proposed computational framework. At the end, reports of fabrication, measurement and verification of some of the proposed designs are presented.Mechanical Engineerin

Extremely Efficient Design of Organic Thin Film Solar Cells via Learning-Based Optimization

Design of efficient thin film photovoltaic (PV) cells require optical power absorption to be computed inside a nano-scale structure of photovoltaics, dielectric and plasmonic materials. Calculating power absorption requires Maxwell’s electromagnetic equations which are solved using numerical methods, such as finite difference time domain (FDTD). The computational cost of thin film PV cell design and optimization is therefore cumbersome, due to successive FDTD simulations. This cost can be reduced using a surrogate-based optimization procedure. In this study, we deploy neural networks (NNs) to model optical absorption in organic PV structures. We use the corresponding surrogate-based optimization procedure to maximize light trapping inside thin film organic cells infused with metallic particles. Metallic particles are known to induce plasmonic effects at the metal–semiconductor interface, thus increasing absorption. However, a rigorous design procedure is required to achieve the best performance within known design guidelines. As a result of using NNs to model thin film solar absorption, the required time to complete optimization is decreased by more than five times. The obtained NN model is found to be very reliable. The optimization procedure results in absorption enhancement greater than 200%. Furthermore, we demonstrate that once a reliable surrogate model such as the developed NN is available, it can be used for alternative analyses on the proposed design, such as uncertainty analysis (e.g., fabrication error)

Specification of Micro-Nanoscale Radiative Patterns Using Inverse Analysis for Increasing Solar Panel Efficiency

International audienceThis work proposes a comprehensive and efficient optimization approach for designing surface patterning for increasing solar panel absorption efficiency using near-field radiation effects. Global and local optimization methods, such as the Broyden-Fletcher-Goldfarb-Shanno quasi-Newton (BFGS-QN) and simulated annealing (SA), are employed for solving the inverse near-field radiation problem. In particular, a thin amorphous silicon (a-Si) solar panel with periodic silver nanowire patterning is considered. The design of the silver patterned solar panel is optimized to yield maximum enhancement in photon absorption. The optimization methods reproduce results found in the previous literature but with reduced computational expense. Additional geometric parameters, which are not discussed in previous work, are included in the optimization analysis, further allowing for increased absorption enhancement. Both the BFGS-QN and the SA methods give efficient results, providing designs with enhanced absorption

Using inverse analysis to find optimum nano-scale radiative surface patterns to enhance solar cell performance

International audienceNano-scale surface patterning can provide highly spectral-directional absorption properties for thin film solar cells. Achieving optimal surface patterning is an inverse optimization problem that requires a numerical procedure capable of scrutinizing the space of geometry parameters to search for an optimal solution. In this paper, 2-D and 3-D thin film amorphous silicon (a-Si) solar cells with periodic structures are considered, whereby the surface of the solar cell is textured with rectangular metallic nano-patterns for enhancing the solar absorption spectrum. We use FDTD simulations to solve Maxwell's equations inside the cell area when subject to standard optical irradiation. By means of several numerical optimization techniques such as the Quasi-Newton BFGS method, random search Simulated Annealing, and Tabu Search, we determine optimal specifications for surface nano-patterns. Invoking constrained optimization tools, we incorporate various types of practical constraints into the optimization programs. The resulting cell structures found by the inverse solvers illustrate enhancement factors as high as 1.52 in solar absorption when silver nano-patterns are used, compared to bare a-Si cells. Results demonstrate the efficiency of the selected optimization techniques and their computational efficiency in contrast with the naive brute force alternative and provide a benchmark for comparing the performances of various local and global optimization methods. The global random search optimization techniques (Simulated Annealing and Tabu Search) tend to perform better than the local method (Quasi-Newton), especially for higher problem dimensions