Six sigma robust multi-objective design optimization of flat plate collector system under uncertain design parameters

Flat plate collectors (FPCs) can be considered as an important alternative to reduce the high costs of electrical energy used in heating water. Therefore, the development of FPCs with optimal performance has been much focused upon in recent research initiatives. However, due to the existence of uncertainties in material properties, geometry and environmental conditions, it is imperative to incorporate uncertainty analysis into the design optimization to obtain reliable results. In this work, a six sigma robust multi-objective optimization (SSRMO) of the FPC is developed where uncertain design parameters (UDP) are considered. A combined multi-objective modified imperialist competitive algorithm (MOMICA) and Monte Carlo simulation method (MCSM) is developed and used for the SSRMO. The SSRMO considers simultaneously three objectives functions including the FPC efficiency, the FPC cost and their sensitivities to UDP. The obtained results showed that the FPC design obtained by determinist optimization presents 31% sensitivity in terms of efficiency and 27% sensitivity in terms of total cost. However, the SSRMO provides a robust FPC design with the same optimal performances comparable to those obtained by deterministic method but with much lower sensitivity to UDP. Moreover, the robust FPC performances sensitivities were reduced by 60% compared to literature results. (c) 2021 Published by Elsevier Ltd.

Automated summary

The study focuses on developing FPCs with optimal performance, considering uncertainties and using a combined algorithm for multi-objective optimization. Results show that robust optimization design performs comparably to deterministic design with lower sensitivity to uncertainties.