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Proceedings Papers

Multi-Fidelity Machine Learning Analysis of Wind Patterns Around High-Rise Buildings

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Javad Mortazavian, Navid Goudarzi
Proc. ASME. POWER2024, ASME 2024 Power Conference, V001T06A008, September 15–18, 2024
Publisher: American Society of Mechanical Engineers
Paper No: POWER2024-138891
https://doi.org/10.1115/POWER2024-138891
... Abstract This study presents a multi-fidelity machine learning (ML) approach to predict the fluctuating components of surface pressure coefficients on high-rise buildings across the entire wind rose. Employing an artificial neural network (ANN), the model is trained using a combination of four...
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Topics: Engineering simulation, Machine learning, Pressure, Reynolds-averaged Navier–Stokes equations, Simulation, Structures, Wind
Proceedings Papers

A Design Study of an Elasto-Hydrodynamic Seal for sCO 2 Power Cycle by Using Physics Informed Neural Network

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Mohammad Towhidul Islam Rimon, Mohammad Fuad Hassan, Karthik Reddy Lyathakula, Sevki Cesmeci, Hanping Xu, Jing Tang
Proc. ASME. POWER2023, ASME Power Applied R&D 2023, V001T04A005, August 6–8, 2023
Publisher: American Society of Mechanical Engineers
Paper No: POWER2023-108802
https://doi.org/10.1115/POWER2023-108802
... analysis can be used to design EHD seals for specific cases when more comprehensive simulation models are not readily available or are deemed to be costly. supercritical CO 2 elasto-hydrodynamic (EHD) machine learning neural networks (NNs) Proceedings of the ASME Power Applied R&D 2023...
View Paper titled, A Design Study of an Elasto-Hydrodynamic Seal for sCO 2 Power Cycle by Using Physics Informed Neural Network
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Topics: Artificial neural networks, Design, Physics, Supercritical carbon dioxide, Thermodynamic power cycles
Proceedings Papers

Convolutional Neural Network Model for the Prediction of Plenum Temperature in a Waste Glass Melter

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A. W. Abboud, D. P. Guillen, R. Pokorny
Proc. ASME. POWER2020, ASME 2020 Power Conference, V001T11A008, August 4–5, 2020
Publisher: American Society of Mechanical Engineers
Paper No: POWER2020-16993
https://doi.org/10.1115/POWER2020-16993
...: Machine learning, computational fluid dynamics, convolutional neural network, vitrification NOMENCLATURE E total energy, J kg-1 fb body force, N m-3 radiation intensity, W sr-1 m-2 black body intensity, W sr-1 m-2 1 Contact author: [email protected] absorption coefficient, m-1 scattering coefficient...
View Paper titled, Convolutional Neural Network Model for the Prediction of Plenum Temperature in a Waste Glass Melter
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Topics: Artificial neural networks, Computational fluid dynamics, Glass, Neural network models, Temperature, Vitrification, Heat transfer, Ceramics, Filters, Fluid dynamics
Proceedings Papers

Using Machine Learning to Increase Model Performance for a Gas Turbine System

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Samuel M. Hipple, Zachary T. Reinhart, Harry Bonilla-Alvarado, Paolo Pezzini, Kenneth Mark Bryden
Proc. ASME. POWER2020, ASME 2020 Power Conference, V001T12A003, August 4–5, 2020
Publisher: American Society of Mechanical Engineers
Paper No: POWER2020-16580
https://doi.org/10.1115/POWER2020-16580
...Using Machine Learning to Increase Model Performance for a Gas Turbine System Samuel M. Hipple, Zachary T. Reinhart, Harry Bonilla-Alvarado, Paolo Pezzini, Kenneth Mark Bryden Simulation Modeling and Decision Science Program Ames Laboratory, 1620 Howe Hall, Ames, Iowa, 50011 ABSTRACT...
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Topics: Artificial neural networks, Gas turbines, Machine learning, Modeling, Turbomachinery