Computational Optimal Design of Random Rough Surfaces in Thin-Film Solar Cells (Communications in Computational Physics, 2019)
Random rough textures can increase the absorbing efficiency of solar cellsby trapping the optical light and increasing the optical path of photons. In this paper,we are concerned with optimal design of random rough surfaces in thin-film solarcells. We formulate the design problem as a random PDE constrained optimizationproblem and employ gradient-based methods for solving the problem numerically. Toevaluate the gradient of the objective function, the Monte-Carlo method is used forsampling the probability space and the adjoint state method is employed to calculatethe gradient at each sample. Numerical examples are shown to test the efficiency of theproposed algorithm. It is demonstrated that optimally obtained random textures yieldan enormous absorption enhancement and a higher photon absorptance than that ofexisting random textures.