Thermodynamic optimisation of solar thermal Brayton cycle models and heat exchangers using particle swarm algorithm

Abstract

In this work, three variants of the Brayton cycle incorporating concentrated solar technologies and dual regenerative systems are modeled. The first variant employs reheat, intercooling, and regeneration, the second applies intercooling and regeneration while the third case involves regeneration only. With the application of the entropy generation method and particle swarm algorithm (PSA), processes with the largest irreversibilities are noted, minimized and the geometric parameters of participating components are optimized. Results show that irreversibilities occurring in the systems were largely due to finite temperature differences within components. In all cases, the solar receiver and intercooler are the dominant and modest sources of entropy generation respectively. The regenerative system entropy generation is highest in the first case while decreasing in the second and third cases respectively. An improvement in the exergy/availability was observed in the first case, as the first and second law efficiency peaks at 44.9% and 59.68% respectively. Though, with a lower second law efficiency than the former, its percentage network output is equal to the first case at 43%. The aspect ratio, hydraulic diameter, and length of the receiver were observed to vary to enhance greater heat capture and increase the turbine inlet temperature (TIT). The high temperature (HT) regenerator had its geometric properties of a higher magnitude than the low temperature (LT) system as the waste heat recovery is aided by an enhanced heat transfer surface area. In comparison with the single regeneration system, the network output of the dual model was about 33.5% with a significant reduction in the entropy generated, creating a trade-off between operating the system for more power or less generation of irreversibilities.