Inverse study of double-glazed solar collector using hybrid evolutionary algorithm

This paper presents an application of the hybrid evolutionary-nonlinear programming based optimization algorithm for solving an inverse problem of a double-glazed flat-plate solar collector. For a given configuration, the performance of the solar collector can be represented by heat loss factor. In this work, four parameters such as plate-inner glass spacing, inner-outer glass spacing, thickness of the glass cover and the emissivity of the absorber plate have been estimated by the hybrid algorithm for satisfying a given heating requirement in a double-glazed flat-plate solar collector. Hybrid algorithm is used to search different unknowns within a prescribed feasible range of various parameters. Many feasible and nearly unique combinations of the four unknowns are observed to satisfy the same heating requirement, which is confirmed by satisfactory reconstruction of the heat loss factor fields.