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Keywords: machine learning
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Proceedings Papers

Proc. ASME. GT2023, Volume 6: Education; Electric Power; Energy Storage; Fans and Blowers, V006T08A003, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-101937
... partial-load operation gas turbine compressor full-annulus computation machine learning self-organizing map (SOM) Abstract In Japan, gas turbine combined cycle (GTCC) power plants are currently operated under rapid start-up, shutdown, and partial-load conditions to maintain a stable...
Proceedings Papers

Proc. ASME. GT2023, Volume 11B: Structures and Dynamics — Emerging Methods in Engineering Design, Analysis, and Additive Manufacturing; Fatigue, Fracture, and Life Prediction; Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V11BT25A005, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-104043
... physics discovery genetic programming machine learning symbolic regression optimization Abstract Discovering physics from data have the potential to advance our understanding and prediction of a system where the governing physics are unknown but experimental data are available...
Proceedings Papers

Proc. ASME. GT2023, Volume 11B: Structures and Dynamics — Emerging Methods in Engineering Design, Analysis, and Additive Manufacturing; Fatigue, Fracture, and Life Prediction; Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V11BT25A001, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-100532
... data of the turbine blade model without using an evaluation mesh. Good agreement is obtained with the approximation of a new parameter set using this model. It is shown that the DNN is a promising surrogate model for probabilistic analysis. machine learning structural mechanics probabilistic...
Proceedings Papers

Proc. ASME. GT2023, Volume 1: Aircraft Engine, V001T01A017, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-102024
... Abstract Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts...
Proceedings Papers

Proc. ASME. GT2023, Volume 1: Aircraft Engine, V001T01A018, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-102054
... and hyperparameters of Machine Learning (ML) models is explained. The uncertainty quantification techniques are considered. The developed ML-powered methods for compressor geometry prediction are discussed. The ML models' accuracy values and representations of typical geometry and performance predictions are given...
Proceedings Papers

Proc. ASME. GT2023, Volume 3A: Combustion, Fuels, and Emissions, V03AT04A036, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-101631
... broadly applicable model nvPM pathways and mechanisms and their dependencies must be better understood helping to identify relevant parameters, e.g. characteristic AFR in the nearfield of the fuel spray nozzle. Keywords: statistical data analysis, machine learning, big data, multidimensional non-linear...
Proceedings Papers

Proc. ASME. GT2023, Volume 3A: Combustion, Fuels, and Emissions, V03AT04A025, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-101380
... droplet evaporation sustainable aviation fuels long-distance microscopy machine learning diffusive evaporation Abstract With aviation’s dependence on the high volumetric energy density offered by liquid fuels, Sustainable Aviation Fuels (SAFs) could offer the fastest path towards...
Proceedings Papers

Proc. ASME. GT2023, Volume 3A: Combustion, Fuels, and Emissions, V03AT04A028, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-101460
... thermoacoustics neural networks machine learning data assimilation flame transfer function Abstract We assimilate the parameters of a low order physics-based model of a bluff-body-stabilized premixed flame by observing OH PLIF and PIV images of a 1.1 MW flame. The model is a five...
Proceedings Papers

Proc. ASME. GT2023, Volume 4: Controls, Diagnostics, and Instrumentation, V004T05A032, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-104214
... of chemometric models, which use machine learning to correlate infrared spectra of fuels to fuel properties like DCN, density, and C/H ratio, amongst many others. These techniques have certain advantages over the ASTM methods, and previous studies performed on samples of diesel fuels have shown high accuracies...
Proceedings Papers

Proc. ASME. GT2023, Volume 13C: Turbomachinery — Deposition, Erosion, Fouling, and Icing; Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions, V13CT32A010, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-101238
... training data required. Based on these results, future potential work should focus on reconstructing the wake loss region of a linear cascade with sparse experimental data. Keywords: Turbulence, Transition, Physics-informed neural networks, Low-Pressure Turbine, Closure Modelling, Machine Learning...
Proceedings Papers

Proc. ASME. GT2023, Volume 13D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V13DT34A010, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-101938
... Alessi1, Dirk Wunsch1, Luca Zampieri2, Charles Hirsch1 1Cadence Design Systems Belgium, Brussels, Belgium 2Neural Concept, Lausanne, Switzerland ABSTRACT A Machine Learning approach is applied to the open domain test case Rotor 37 to predict the performance of a family of turbomachinery. A deep learning...
Proceedings Papers

Proc. ASME. GT2023, Volume 13D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V13DT34A016, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-102700
...Proceedings of ASME Turbo Expo 2023 Turbomachinery Technical Conference and Exposition GT2023 June 26-30, 2023, Boston, Massachusetts, USA GT2023-102700 RAPID ALGORITHMIC BLADE DESIGN APPLYING MACHINE LEARNING FROM SHAPE OPTIMIZATION TO SATISFY MULTIDISCIPLINARY CONSTRAINTS Kingshuk Dasadhikari...
Proceedings Papers

Proc. ASME. GT2023, Volume 13D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V13DT36A023, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-103914
...Proceedings of ASME Turbo Expo 2023 Turbomachinery Technical Conference and Exposition GT2023 June 26-30, 2023, Boston, Massachusetts, USA GT2023-103914 A MACHINE LEARNING APPROACH FOR THE PREDICTION OF TIME-AVERAGED UNSTEADY FLOWS IN TURBOMACHINERY Dominik Blechschmidt1,2 Dajan Mimic1,2...
Proceedings Papers

Proc. ASME. GT2022, Volume 1: Aircraft Engine; Ceramics and Ceramic Composites, V001T01A009, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-81215
... margins. The availability of such information enables the opportunity to make technical-economical decisions about the reasonability of implementation of independent variable vanes and their number during engine system analysis. Keywords: Axial Compressor, Performance Map, Machine Learning, Artificial...
Proceedings Papers

Proc. ASME. GT2022, Volume 10B: Turbomachinery — Axial Flow Turbine Aerodynamics; Deposition, Erosion, Fouling, and Icing; Radial Turbomachinery Aerodynamics, V10BT35A004, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-80186
... powertrains for different drive cycles, the current piece of work seeks to combine the advantages of machine learning techniques and physical meanline modelling to facilitate faster, more accurate predictions of complete turbocharger maps. This paper presents a novel methodology for turbocharger turbine...
Proceedings Papers

Proc. ASME. GT2022, Volume 7: Industrial and Cogeneration; Manufacturing Materials and Metallurgy; Microturbines, Turbochargers, and Small Turbomachines; Oil & Gas Applications, V007T17A029, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-84352
... austenitic stainless steel alloy design GPTIPS genetic programming machine learning stress-rupture Abstract This study outlines a machine learning approach for long-term stress-rupture (SR) prediction of high temperature austenitic stainless steel. Traditional methods of lifetime...
Proceedings Papers

Proc. ASME. GT2022, Volume 8B: Structures and Dynamics — Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V08BT25A008, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-83372
... that the discovered formulas can predict the future damage accurately. Our framework is flexible and easily applicable to all areas of science and engineering. With cutting-edge machine learning tools, researchers can simply input the experimental data and then the physics formulas are printed out automatically...
Proceedings Papers

Proc. ASME. GT2022, Volume 8B: Structures and Dynamics — Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V08BT25A003, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-82003
... the identified range. The simulation results were used as training and test data to create a model by machine learning methods. Different machine learning methods such as neural network, random forest tree and k-nearest neighbor were applied and compared to determine the best fitted model. Based...
Proceedings Papers

Proc. ASME. GT2022, Volume 2: Coal, Biomass, Hydrogen, and Alternative Fuels; Controls, Diagnostics, and Instrumentation; Steam Turbine, V002T05A010, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-82037
... gas turbine performance-based diagnostics artificial neural network fuzzy logic Kalman filter data analytics data filtering diagnostics multiple failures gas turbine health monitoring failure classification gas turbine diagnostics machine learning artificial intelligence...