Abstract
Limited access to energy resources, air pollution and its subsequent detriments to ozone layer have accentuated the importance of green industry for recovering exergy from high-pressure flow to generate clean electrical power. Due to low maintenance costs, usage in two-phase fluids and longer lifespan, the twin screw expanders (TSEs) have attracted great attention though there is still much to be done to improve their performance. The complexity of computing in screw machines, as well as the time consuming of their simulation throughout the entire design space by the computational fluid dynamics (CFDs), requires the use of other complementary tools to optimise the performance of these types of machines. In this paper, based on laboratory model specification and experimental data, a TSE model was simulated and validated using CFD. Consequently, by helping design of experiments (DOE), a CFD alternative model is obtained called surrogate-based modelling (SBM) and the effect of three main geometric parameters on the expander's exergy efficiency were investigated in entire design space. Finally, a more efficient TSE with 86% exergy efficiency was obtained by using multi-objective genetic algorithm in the shortest time.
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