Energy analysis and optimization of a small-scale axial flow turbine for Organic Rankine Cycle application

Abstract

Increasing the cycle efficiency of Organic Rankine Cycles is an important R&D area. In this study, an effort has been made to optimize various parameters related to the axial flow turbine to maximize an ORC's efficiency. First, a numerical model for a small-scale single-stage axial flow turbine was developed and coupled with a 1D model of an existing ORC system. Then, a parametric study was undertaken for the system working under various turbine inlet conditions, such as turbine pressure ratios and working fluids. An optimization study was undertaken for the turbine flow profile using a low computational intensity Artificial Neural Network coupled with Genetic Algorithm optimization. Investigating the turbine losses revealed that the Mach Number is the most influential factor, which depends on the molar mass of the working fluid. Our study revealed that increasing the degree of superheat by up to 200% enhanced the turbine and overall cycle efficiency by 11% and 5%, respectively. Increasing the turbine total-to-static pressure ratio from 3 to 10 improved the turbine and cycle efficiency by up to 41% and 15%, respectively. Optimizing the turbine's flow profile enhanced the overall loss coefficient by 13.7%, the turbine's total-to-static efficiency by 5.2%, and the overall cycle efficiency from 8.78% to 9.02%.