Progressive Latin Hypercube sampling-based robust design optimisation (PLHS-RDO)

Abstract

The main purpose of this paper is to present a novel Robust Design Optimisation (RDO) strategy based on an efficient Uncertainty Analysis (UA) approach. To this end, a Progressive Latin Hypercube Sampling (PLHS) method was developed to derive the minimum samples for UA. The required sample size is calculated based on the convergence of the UA results. Therefore, UA is achieved by a variable sample size Design of Experiments (DOE). This systematic approach leads to an efficient, adaptive and fast framework for RDO. The proposed algorithm performance was validated by some numerical simulations methods on a benchmark function. In conclusion, the proposed methodology was utilised to the design of a hydrazine catalyst bed as a case study. The results of applied RDO in catalyst bed design parameters and also the corresponding value of objective functions demonstrates the performance of the developed framework in space applications.

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