Open AccessA gas turbine thermal performance prediction method based on dynamic neural network
Author(s) -
Yansong Hao,
Yunfeng Jin,
Chao Liu,
Jiangang Hao,
Huimin Huang,
Dongxiang Jiang
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1207/1/012014
Subject(s) - isentropic process , gas compressor , artificial neural network , computer science , reliability (semiconductor) , gas turbines , entropy (arrow of time) , turbine , series (stratigraphy) , set (abstract data type) , engineering , artificial intelligence , mechanical engineering , thermodynamics , paleontology , power (physics) , physics , biology , programming language
In order to ensure safety and reliability of energy transportation, it is necessary to understand and predict the performance of the gas turbine components. A prediction frame of the gas turbine compressor isentropic efficiency is established using the neural time series theory based on the Dynamic Neural Network. In order to obtain appropriate parameters for the network, a validation set is introduced to generalize the model. The compressor isentropic efficiency can be predicted based on the suggested model which provides an effective technical mean for the early warning of gas turbine performance. The experiment verified that the performance calculation model and the isentropic entropy efficiency prediction model based on the neural time series are effective.