Performance prediction of gas turbine blade with multi-source random factors using active learning-based neural network-Reference-Cited by-同舟云学术

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Performance prediction of gas turbine blade with multi-source random factors using active learning-based neural network

Published:2024-04 Issue: Volume:242 Page:122481
ISSN:1359-4311
Container-title:Applied Thermal Engineering
language:en
Short-container-title:Applied Thermal Engineering

Author:

Qiu Zhilong,Wang Yuqi,Li Jinxing,Xie Yonghui,Zhang Di

Funder

National Major Science and Technology Projects of China

Xi'an Jiaotong University

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Reference68 articles.

1. Experimental and computational assessment into the heat transfer for the blade multicavity tips;Li;Appl. Therm. Eng.,2023

2. Research on active modulation of gas turbine cooling air flow;Long;Appl. Therm. Eng.,2023

3. Impact of gas turbine flexibility improvements on combined cycle gas turbine performance;Abudu;Appl. Therm. Eng.,2021

4. An automated strategy for gas turbines off-design predictions with a CFD-based throughflow method;Ricci;Appl. Therm. Eng.,2021

5. Uncertainty analysis in fatigue life prediction of gas turbine blades using Bayesian inference;Li;Int. J. Turbo Jet-Engines,2015

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