Implementation of hot steam injection in steam turbine design: A novel mean-line method coupled with multi-objective optimization and neural network

Mehran Ansari, Vahid Esfahanian, Mohammad Javad Izadi, Hosein Bashi, Alireza Tavakoli and Mohammad Kordi

Energy, 2023, vol. 283, issue C

Abstract: Low-pressure turbines usually work in wet conditions which causes both lifetime and efficiency reduction. Hot steam injection (HSI) which has received great interest recently, is a suggested solution to reduce wetness. There is a research gap in implementing HSI in the mean-line procedure, which is a conventional method of designing and analyzing turbines. In this paper for the first time, an object-oriented in-house mean-line code is developed with the ability of HSI calculation for steam turbines. The validation of code is performed with the 3D simulation of a 2-stage axial steam turbine. In addition, considering that HSI may reduce efficiency of the turbine due to mixing entropy generation, a multi-objective genetic algorithm optimization and a neural network is used to redesign the steam turbine. Mass fraction and total temperature of injected flow from the trailing edge of blade rows are the decision variables and, liquid mass fraction and efficiency of turbine are the objective functions. The comparison of Pareto front points reveals that the maximum possible improvement of quality and power relative to baseline is 5% and 10%, respectively. Furthermore, if efficiency is the desired objective function, by enhancing 1% of steam quality, it can be increased by 0.1%.

Keywords: Mean-line; Steam turbine; Hot steam injection; Wet steam; Neural network optimization; Non-equilibrium condensation (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:283:y:2023:i:c:s0360544223025185

DOI: 10.1016/j.energy.2023.129124

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