Inverse Design and Optimization Methods for Thermophotovoltaic Emitters made of Tungsten Gratings

Abstract

Periodic gratings utilized as emitters increase the efficiency of thermophotovoltaic (TPV) systems. These gratings work by altering the emittance spectrum incident on the photovoltaic cell to better match the band gap of the cell. Photons at slightly higher energies than the band gap are the most efficient as they generate electron-hole pairs while minimizing thermalization losses. This prompts the use of gratings to be used as selective emitters. Even for a one-dimensional (1D) grating, millions of possible geometries exist, and simulating even a fraction is infeasible. This prompts the use of metaheuristics. It should be noted that due to the stochastic nature of these optimization methods, a globally optimal solution is not guaranteed, and instead, these methods seek to provide “close enough” solutions. Generally, metaheuristic algorithms have been extensively studied and compared with each other; according to the “no free lunch” (NFL) theorem, all optimization algorithms are equivalent when averaged over all possible problems. Therefore, a comparison of existing algorithms for the optimization of a system, composed of a 2,000 K 1D tungsten binary grating paired with a 300 K InGaSb cell, was performed. After using the comparison, a hyper-heuristic optimization was used to algorithmically develop a purpose-built metaheuristic algorithm. Rigorous coupled-wave analyses (RCWA) take too long to natively perform for the hyper-heuristic search. Fully connected neural nets (FCNN) solve this problem when used as surrogate models. The new optimization algorithm created in this way showed significantly better performance than all the existing algorithms it was compared against. Then, this algorithm was used to optimize emitters for a normalized emittance spectrum, maximum efficiency, and maximum power.