Performance Prediction of a Multi-Stage Ammonia-Water Turbine Under Variable Nozzle Operation via Machine Learning

Abstract

This study proposes machine learning models to predict the performance of a multi-stage ammonia-water radial turbine using variable nozzle operation under different operating conditions. A 1.2 MW four-stage ammonia-water radial turbine is firstly designed. Then, the one-dimensional off-design simulation model is developed based on the geometric parameters to mainly evaluate the effects of different nozzle outlet angles and turbine inlet temperatures on the turbine performance. A set of data, which consists of 10,000 training points based on one-dimensional model, is used to train the proposed two high-dimensional model representation (HDMR) methods. The forward HDMR model predicts the mass flow rate, turbine outlet temperature, turbine power and turbine efficiency for any combination of turbine nozzle outlet angle and turbine inlet temperature, while the reverse HDMR model predicts the mass flow rate, turbine outlet temperature, turbine efficiency and turbine nozzle outlet angle for any combination of turbine power and turbine inlet temperature. The two HDMR models are validated using 238 sets of separated test data. The results show that the minimum coefficients of determination (R2) of forward HDMR model and reverse HDMR model are 0.9837 and 0.9953, respectively. The maximum relative errors of two HDMR models are below 1.6822%, so the quality of the proposed machine learning methods is high. The overall performance maps of multi-stage ammonia-water radial turbine under the variable nozzle operation method are constructed based on the reverse HDMR model. The reverse HDMR model is helpful in monitoring the healthy operation state of turbine.