Gas turbine performance prediction via machine learning

Citations

Cited by:

1. Cheng, Xianda & Zheng, Haoran & Yang, Qian & Zheng, Peiying & Dong, Wei, 2023. "Surrogate model-based real-time gas path fault diagnosis for gas turbines under transient conditions," Energy, Elsevier, vol. 278(PA).
2. Li, Yunzhu & Liu, Tianyuan & Wang, Yuqi & Xie, Yonghui, 2022. "Deep learning based real-time energy extraction system modeling for flapping foil," Energy, Elsevier, vol. 246(C).
3. Wang, Yuqi & Liu, Tianyuan & Meng, Yue & Zhang, Di & Xie, Yonghui, 2022. "Integrated optimization for design and operation of turbomachinery in a solar-based Brayton cycle based on deep learning techniques," Energy, Elsevier, vol. 252(C).
4. Mingliang Bai & Jinfu Liu & Yujia Ma & Xinyu Zhao & Zhenhua Long & Daren Yu, 2020. "Long Short-Term Memory Network-Based Normal Pattern Group for Fault Detection of Three-Shaft Marine Gas Turbine," Energies, MDPI, vol. 14(1), pages 1-22, December.
5. Park, Yeseul & Choi, Minsung & Choi, Gyungmin, 2022. "Fault detection of industrial large-scale gas turbine for fuel distribution characteristics in start-up procedure using artificial neural network method," Energy, Elsevier, vol. 251(C).
6. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Syed Muhammad Arafat & Sher Afghan & Ahmad Hassan Kamal & Muhammad Asim & Muhammad Haider Khan & Muhammad Waqas Rafique & Uwe Naumann & Sajawal Gul Niazi &, 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency," Energies, MDPI, vol. 13(21), pages 1-33, October.
7. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Muhammad Farooq & Fahid Riaz & Hassan Afroze Ahmad & Ahmad Hassan Kamal & Saqib Anwar & Ahmed M. El-Sherbeeny & Muhammad Haider Khan & Noman Hafeez & Arman, 2021. "Construction of Operational Data-Driven Power Curve of a Generator by Industry 4.0 Data Analytics," Energies, MDPI, vol. 14(5), pages 1-18, February.
8. Zhou, Taotao & Tang, Peng & Ye, Taohong, 2023. "Machine learning based heat release rate indicator of premixed methane/air flame under wide range of equivalence ratio," Energy, Elsevier, vol. 263(PE).
9. Du, Qiuwan & Li, Yunzhu & Yang, Like & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Performance prediction and design optimization of turbine blade profile with deep learning method," Energy, Elsevier, vol. 254(PA).
10. Thanh Dam Mai & Jaiyoung Ryu, 2021. "Effects of Damaged Rotor Blades on the Aerodynamic Behavior and Heat-Transfer Characteristics of High-Pressure Gas Turbines," Mathematics, MDPI, vol. 9(6), pages 1-21, March.
11. Wang, Yuqi & Du, Qiuwan & Li, Yunzhu & Zhang, Di & Xie, Yonghui, 2022. "Field reconstruction and off-design performance prediction of turbomachinery in energy systems based on deep learning techniques," Energy, Elsevier, vol. 238(PB).
12. Żymełka, Piotr & Szega, Marcin, 2020. "Issues of an improving the accuracy of energy carriers production forecasting in a computer-aided system for monitoring the operation of a gas-fired cogeneration plant," Energy, Elsevier, vol. 209(C).
13. Cheng, Xianda & Zheng, Haoran & Dong, Wei & Yang, Xuesen, 2023. "Performance prediction of marine intercooled cycle gas turbine based on expanded similarity parameters," Energy, Elsevier, vol. 265(C).
14. Masood, Zahid & Khan, Shahroz & Qian, Li, 2021. "Machine learning-based surrogate model for accelerating simulation-driven optimisation of hydropower Kaplan turbine," Renewable Energy, Elsevier, vol. 173(C), pages 827-848.
15. Wang, Qi & Yang, Li & Huang, Kang, 2022. "Fast prediction and sensitivity analysis of gas turbine cooling performance using supervised learning approaches," Energy, Elsevier, vol. 246(C).
16. Li, Jian & Wang, Zhitao & Li, Shuying & Ming, Liang, 2022. "A SDNN-MPC method for power distribution of COGAG propulsion system," Energy, Elsevier, vol. 254(PB).
17. Jiang, Chiju & Zhang, Weihao & Li, Ya & Li, Lele & Wang, Yufan & Huang, Dongming, 2023. "Multi-scale Pix2Pix network for high-fidelity prediction of adiabatic cooling effectiveness in turbine cascade," Energy, Elsevier, vol. 265(C).
18. Park, Yeseul & Choi, Minsung & Kim, Kibeom & Li, Xinzhuo & Jung, Chanho & Na, Sangkyung & Choi, Gyungmin, 2020. "Prediction of operating characteristics for industrial gas turbine combustor using an optimized artificial neural network," Energy, Elsevier, vol. 213(C).
19. Cheng, Hongzhi & Zhou, Chuangxin & Lu, Xingen & Zhao, Shengfeng & Han, Ge & Yang, Chengwu, 2023. "Robust aerodynamic optimization and design exploration of a wide-chord transonic fan under geometric and operational uncertainties," Energy, Elsevier, vol. 278(PB).
20. Szega, Marcin & Żymełka, Piotr & Janda, Tomasz, 2022. "Improving the accuracy of electricity and heat production forecasting in a supervision computer system of a selected gas-fired CHP plant operation," Energy, Elsevier, vol. 239(PE).
21. Vedran Mrzljak & Igor Poljak & Maro Jelić & Jasna Prpić-Oršić, 2023. "Thermodynamic Analysis and Improvement Potential of Helium Closed Cycle Gas Turbine Power Plant at Four Loads," Energies, MDPI, vol. 16(15), pages 1-26, July.
22. Wang, Qi & Yang, Li & Rao, Yu, 2021. "Establishment of a generalizable model on a small-scale dataset to predict the surface pressure distribution of gas turbine blades," Energy, Elsevier, vol. 214(C).
23. Li, Jinxing & Liu, Tianyuan & Wang, Yuqi & Xie, Yonghui, 2022. "Integrated graph deep learning framework for flow field reconstruction and performance prediction of turbomachinery," Energy, Elsevier, vol. 254(PC).
24. Martí de Castro-Cros & Manel Velasco & Cecilio Angulo, 2021. "Machine-Learning-Based Condition Assessment of Gas Turbines—A Review," Energies, MDPI, vol. 14(24), pages 1-27, December.
25. Li, Jinxing & Liu, Tianyuan & Zhu, Guangya & Li, Yunzhu & Xie, Yonghui, 2023. "Uncertainty quantification and aerodynamic robust optimization of turbomachinery based on graph learning methods," Energy, Elsevier, vol. 273(C).
26. Du, Qiuwan & Yang, Like & Li, Liangliang & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Aerodynamic design and optimization of blade end wall profile of turbomachinery based on series convolutional neural network," Energy, Elsevier, vol. 244(PA).