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Keywords: machine learning
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Proceedings Papers

Proc. ASME. GT2021, Volume 2D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V02DT37A009, June 7–11, 2021
Paper No: GT2021-59473
... Proceedings of ASME Turbo Expo 2021 Turbomachinery Technical Conference and Exposition GT2021 June 7-11, 2021, Virtual, Online GT2021-59473 CENTRIFUGAL COMPRESSOR AERO-MECHANICAL DESIGN: A MACHINE LEARNING APPROACH Dario Barsi Andrea Perrone Luca Ratto DIME - University of Genova NSI - Numerical...
Proceedings Papers

Proc. ASME. GT2021, Volume 4: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage; Education; Electric Power, V004T09A003, June 7–11, 2021
Paper No: GT2021-58933
... that the fusion of total plant data, Keywords: Data Fusion, Combustion Turbines, Gas from three primary sources, with the ability to transform, analyze, and act based on integrating subject matter expertise is Turbines, Machine Learning, Power Generation, Reliability essential for effectively managing assets...
Proceedings Papers

Proc. ASME. GT2021, Volume 4: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage; Education; Electric Power, V004T06A016, June 7–11, 2021
Paper No: GT2021-59615
...Abstract Abstract This study proposes machine learning models to predict the performance of a multi-stage ammonia-water radial turbine using variable nozzle operation under different operating conditions. A 1.2 MW four-stage ammonia-water radial turbine is firstly designed. Then, the one...
Proceedings Papers

Proc. ASME. GT2021, Volume 1: Aircraft Engine; Fans and Blowers; Marine; Wind Energy; Scholar Lecture, V001T10A006, June 7–11, 2021
Paper No: GT2021-59277
... Proceedings of ASME Turbo Expo 2021 Turbomachinery Technical Conference and Exposition GT2021 June 7-11, 2021, Virtual, Online GT2021-59277 CASCADE WITH SINUSOIDAL LEADING EDGES: IDENTIFICATION AND QUANTIFICATION OF DEFLECTION WITH UNSUPERVISED MACHINE LEARNING Alessandro Corsini, Giovanni Delibra...
Proceedings Papers

Proc. ASME. GT2021, Volume 8: Oil and Gas Applications; Steam Turbine, V008T22A018, June 7–11, 2021
Paper No: GT2021-60049
... and predictive maintenance which can help the improvement of power system. In this study, Keywords: Multi-parameter prediction; Deep learning; deep-learning models including recurrent neural network (RNN) and convolutional neural network (CNN) for multi-parameter Machine learning; Recurrent neural network...
Proceedings Papers

Proc. ASME. GT2021, Volume 9B: Structures and Dynamics — Fatigue, Fracture, and Life Prediction; Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V09BT27A003, June 7–11, 2021
Paper No: GT2021-58842
.... This is exemplified in the process of designing the individual components of the IGT resulting in a potential unrealized efficiency. To overcome the aforementioned challenges, we demonstrate a probabilistic inverse design machine learning framework, namely Pro-ML IDeAS, to carry out an explicit inverse design. Pro-ML...
Proceedings Papers

Proc. ASME. GT2021, Volume 4: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage; Education; Electric Power, V004T05A006, June 7–11, 2021
Paper No: GT2021-59249
... Proceedings of ASME Turbo Expo 2021 Turbomachinery Technical Conference and Exposition GT2021 June 7-11, 2021, Virtual, Online GT2021-59249 A LESSON ON OPERATIONALIZING MACHINE LEARNING FOR PREDICTIVE MAINTENANCE OF GAS TURBINES Paolo Pileggi Elena Lazovik Ron Snijders Lars-Uno Axelsson TNO TNO...
Proceedings Papers

Proc. ASME. GT2020, Volume 7B: Heat Transfer, V07BT12A030, September 21–25, 2020
Paper No: GT2020-14811
... fail to accurately predict heat transfer in film cooling flows. Recent work suggests the use of machine learning models to improve turbulent closure in these flows. In the present work, a machine learning model for spatially varying turbulent Prandtl number previously described in the literature...
Proceedings Papers

Proc. ASME. GT2020, Volume 4A: Combustion, Fuels, and Emissions, V04AT04A025, September 21–25, 2020
Paper No: GT2020-14483
... achieved by application of the latest numerical design tools, advanced statistical machine learning techniques and utilization of additive manufacturing. Results from testing and validation of the modifications at the Siemens Clean Energy Center (CEC) for stand-alone combustion tests and Berlin Test...
Proceedings Papers

Proc. ASME. GT2020, Volume 4B: Combustion, Fuels, and Emissions, V04BT04A020, September 21–25, 2020
Paper No: GT2020-15676
... or signal processing. Successful identification of wave behavior using image classification serves as a stepping stone for further machine learning integration in RDE research and comprehensive real-time diagnostics. rotating detonation engine machine learning modal classification high-speed...
Proceedings Papers

Proc. ASME. GT2020, Volume 5: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage, V005T05A027, September 21–25, 2020
Paper No: GT2020-16062
... outputs and control inputs from pilots. More accurate trend monitoring of the relationship among engine parameters enables earlier anomaly detection with high accuracy. Machine learning techniques such as clustering, nonlinear regression, classification and optimization are used in STM. The input data...
Proceedings Papers

Proc. ASME. GT2020, Volume 4A: Combustion, Fuels, and Emissions, V04AT04A056, September 21–25, 2020
Paper No: GT2020-15020
...Email: lisuhui@tsinghua.edu.cn Tel: +86-10-62796575 PREDICTION OF THE AUTOIGNITION OF A FUEL JET IN A CONFINED TURBULENT HOT COFLOW USING MACHINE LEARNING METHODS Suhui Li1*, Wenkai Qian1, Haoyang Liu1, Min Zhu1, Christos N. Markides 2 1 Key Laboratory for Thermal Science and Power Engineering...
Proceedings Papers

Proc. ASME. GT2019, Volume 1: Aircraft Engine; Fans and Blowers; Marine; Honors and Awards, V001T01A022, June 17–21, 2019
Paper No: GT2019-91432
...Abstract Abstract With the rise in big data and analytics, machine learning is transforming many industries. It is being increasingly employed to solve a wide range of complex problems, producing autonomous systems that support human decision-making. For the aircraft engine industry, machine...