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Keywords: deep learning
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Proceedings Papers

Proc. ASME. GT2022, Volume 10D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Turbomachinery General Interest; Unsteady Flows in Turbomachinery, V10DT34A021, June 13–17, 2022
Paper No: GT2022-83063
... forecast has been evaluated through CFD simulations carried out on the optimal sample. The results related to the optimized sample have been presented and highlight all the benefits of the proposed approach. aerodynamic optimization deep learning Rotor37 Proceedings of ASME Turbo Expo 2022...
Proceedings Papers

Proc. ASME. GT2022, Volume 11: Wind Energy, V011T38A018, June 13–17, 2022
Paper No: GT2022-82624
... methodologies are proposed as alternatives for fault detection. Deep learning tools have been introduced in the research field of wind turbines’ monitoring for the purpose of higher detection accuracy. In this work, a deep learning-based anomaly detection method, the Deep Support Vector Data Description (Deep...
Proceedings Papers

Proc. ASME. GT2021, Volume 8: Oil and Gas Applications; Steam Turbine, V008T22A020, June 7–11, 2021
Paper No: GT2021-60355
... segment start-up process, generating training data for the deep learning method. Next, data of only 15 temperature measurement points N Number of samples in the dataset are arranged to predict the stress distribution in critical area of the rotor surface, with the accuracy (R2-score) reaching 0.997. P0...
Proceedings Papers

Proc. ASME. GT2021, Volume 8: Oil and Gas Applications; Steam Turbine, V008T22A019, June 7–11, 2021
Paper No: GT2021-60247
... are the normal state of rotor failure. In recent years, more methods that rely on signal processing experience, and has and more attention has been paid to the fault detection method based on deep learning, which takes rotating the characteristics of high precision and strong robustness. machinery as the object...
Proceedings Papers

Proc. ASME. GT2021, Volume 8: Oil and Gas Applications; Steam Turbine, V008T22A018, June 7–11, 2021
Paper No: GT2021-60049
... Proceedings of ASME Turbo Expo 2021 Turbomachinery Technical Conference and Exposition GT2021 June 7-11, 2021, Virtual, Online GT2021-60049 MULTI-PARAMETER PREDICTION FOR STEAM TURBINE BASED ON REAL-TIME DATA USING DEEP LEARNING APPROACHES Lei Sun1 Tianyuan Liu School of Energy and Power School...
Proceedings Papers

Proc. ASME. GT2021, Volume 10: Supercritical CO2, V010T30A026, June 7–11, 2021
Paper No: GT2021-60056
... learning technology, the research of surrogate models based on neural network has received extensive attention. In order to improve the inefficiency in traditional off-design analyses, this research establishes a data-driven deep learning off-design aerodynamic prediction model for a S-CO 2 centrifugal...
Proceedings Papers

Proc. ASME. GT2020, Volume 1: Aircraft Engine; Fans and Blowers, V001T01A022, September 21–25, 2020
Paper No: GT2020-14661
... patterns in an open-source database of one-hundred- eighty-three production and research turbofan engines, and built predictive analytics for use in predicting system performance of new turbofan designs. Specifically, the author developed deep-learning analytics to predict turbofan system weight, using...
Proceedings Papers

Proc. ASME. GT2020, Volume 1: Aircraft Engine; Fans and Blowers, V001T10A009, September 21–25, 2020
Paper No: GT2020-15338
... approach of adding noise to the mean velocity profile. Among all the different artificial intelligence branches, deep learning is particularly suitable to study and understand very complex processes. In recent years researchers applied deep learning in various fields, such as medicine, robotics...