A gas turbine thermal performance prediction method based on dynamic neural network. Issue 1 (November 2021)

• Record Type: Journal Article
• Authors: Hao, Yansong; Jin, Yunfeng; Liu, Chao; Hao, Jiangang; Huang, Haizhou; Jiang, Dongxiang
• Is Part Of: IOP conference series. Volume 1207:Issue 1(2021)
• Publication Date: 2021-11
• Subjects: Gas turbine -- State prediction -- Dynamic Neural Network -- Neural time series.; Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/1207/1/012014
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: 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.