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Proceedings Papers

Bridging Length Scales Efficiently Through Surrogate Modelling

David Knowles, Eralp Demir, Mike C. Smith, Maria S. Yankova, Anastasia Vasileiou, Dimitra Rissaki, Paul Wilcox, Dinesh Kumar, Mehdi Mokhtarishirazabad, Mahmoud Mostafavi
Proc. ASME. PVP2023, Volume 5: Materials & Fabrication, V005T06A030, July 16–21, 2023
Publisher: American Society of Mechanical Engineers
Paper No: PVP2023-101549
https://doi.org/10.1115/PVP2023-101549
View Paper titled, Bridging Length Scales Efficiently Through Surrogate Modelling
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Topics: Crystals, Machine learning, Metamodeling, Plasticity, Stress, Safety, Simulation, Algorithms, Chaos, Decision making
Proceedings Papers

What Best Correlates to High Temperature Failure: Strain, Stress, Dissipation, or Something Else?

Tianchen Hu, Mark C. Messner
Proc. ASME. PVP2023, Volume 5: Materials & Fabrication, V005T06A016, July 16–21, 2023
Publisher: American Society of Mechanical Engineers
Paper No: PVP2023-105949
https://doi.org/10.1115/PVP2023-105949
View Paper titled, What Best Correlates to High Temperature Failure: Strain, Stress, Dissipation, or Something Else?
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Topics: Energy dissipation, Failure, High temperature, Stress, Creep, Damage, Fatigue, Machine learning, Artificial neural networks, Computers
Proceedings Papers

Design of Centrifugally Cast Fe-Cr-Ni Heat-Resistant Alloys Driven by Material Genome Technology

Zhichao Fan, Shulin Xiang, Tao Chen
Proc. ASME. PVP2023, Volume 5: Materials & Fabrication, V005T06A017, July 16–21, 2023
Publisher: American Society of Mechanical Engineers
Paper No: PVP2023-106244
https://doi.org/10.1115/PVP2023-106244
View Paper titled, Design of Centrifugally Cast Fe-Cr-Ni Heat-Resistant Alloys Driven by Material Genome Technology
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Topics: Alloys, Design, Heat, Machine learning, Phase diagrams, Robots
Proceedings Papers

Data-Driven Stress Intensity Factor Solutions for Axial Outside Surface Cracks in Thin-Walled Cylinders

Xian-Kui Zhu, Jesse B. Zhu, Andrew J. Duncan
Proc. ASME. PVP2023, Volume 5: Materials & Fabrication, V005T06A024, July 16–21, 2023
Publisher: American Society of Mechanical Engineers
Paper No: PVP2023-106161
https://doi.org/10.1115/PVP2023-106161
View Paper titled, Data-Driven Stress Intensity Factor Solutions for Axial Outside Surface Cracks in Thin-Walled Cylinders
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Topics: American Petroleum Institute, ASME Boiler and Pressure Vessel Code, Cylinders, Machine learning, Stress, Surface cracks
Proceedings Papers

A Novel Machine Learning Based Framework for the Seismic Risk Assessment of Industrial Plant

Gianluca Quinci, Fabrizio Paolacci
Proc. ASME. PVP2023, Volume 7: Seismic Engineering; ASME Nondestructive Evaluation, Diagnosis and Prognosis (NDPD) Division, V007T08A010, July 16–21, 2023
Publisher: American Society of Mechanical Engineers
Paper No: PVP2023-106452
https://doi.org/10.1115/PVP2023-106452
View Paper titled, A Novel Machine Learning Based Framework for the Seismic Risk Assessment of Industrial Plant
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Topics: Artificial neural networks, Cities, Industrial plants, Machine learning, Principal component analysis, Seismic hazard assessment, Risk assessment, Finite element model, Hazards, Algorithms
Proceedings Papers

A Comparative Study on AI/ML-Based Transient Temperature Predictions and Real-Time Operational Transient Temperature Data of Coke Drum

Balaji Srinivasan, Srinivasan V.
Proc. ASME. PVP2023, Volume 7: Seismic Engineering; ASME Nondestructive Evaluation, Diagnosis and Prognosis (NDPD) Division, V007T09A012, July 16–21, 2023
Publisher: American Society of Mechanical Engineers
Paper No: PVP2023-106180
https://doi.org/10.1115/PVP2023-106180
View Paper titled, A Comparative Study on AI/ML-Based Transient Temperature Predictions and Real-Time Operational Transient Temperature Data of Coke Drum
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Topics: Artificial intelligence, Coke, Digital twin, Machine learning, Maintenance, Temperature, Transients (Dynamics), Algorithms, Damage, Fatigue life
Proceedings Papers

Data-Driven Stress Intensity Factor Solutions for Axial Outside Surface Cracks in Thick-Wall Cylinders

Xian-Kui Zhu, Jesse B. Zhu, Andrew J. Duncan
Proc. ASME. PVP2022, Volume 4A: Materials and Fabrication, V04AT06A032, July 17–22, 2022
Publisher: American Society of Mechanical Engineers
Paper No: PVP2022-86164
https://doi.org/10.1115/PVP2022-86164
View Paper titled, Data-Driven Stress Intensity Factor Solutions for Axial Outside Surface Cracks in Thick-Wall Cylinders
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Topics: American Petroleum Institute, ASME Boiler and Pressure Vessel Code, Cylinders, Machine learning, Stress, Surface cracks
Proceedings Papers

Machine Learning Models of Burst Strength for Defect-Free Pipelines

Xian-Kui Zhu, William R. Johnson, Robert Sindelar, Bruce Wiersma
Proc. ASME. PVP2022, Volume 4B: Materials and Fabrication, V04BT06A043, July 17–22, 2022
Publisher: American Society of Mechanical Engineers
Paper No: PVP2022-84908
https://doi.org/10.1115/PVP2022-84908
View Paper titled, Machine Learning Models of Burst Strength for Defect-Free Pipelines
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Topics: Artificial neural networks, Machine learning, Pipelines
Proceedings Papers

A Recurrent Neural Network Method for Condition Monitoring and Predictive Maintenance of Pressure Vessel Components

Chris Halliday, Iain Palmer, Nigel Pready, Mark Joyce
Proc. ASME. PVP2022, Volume 5: Operations, Applications, and Components; Seismic Engineering; ASME Nondestructive Evaluation, Diagnosis and Prognosis (NDPD) Division, V005T07A003, July 17–22, 2022
Publisher: American Society of Mechanical Engineers
Paper No: PVP2022-84696
https://doi.org/10.1115/PVP2022-84696
View Paper titled, A Recurrent Neural Network Method for Condition Monitoring and Predictive Maintenance of Pressure Vessel Components
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Topics: Artificial intelligence, Artificial neural networks, Condition monitoring, Machine learning, Maintenance, Pressure vessels
Proceedings Papers

Predicting the Projectile Velocity of a Two-Stage Gas Gun Using Machine Learning

Pouya Shojaei, Mohamed Trabia, Brendan O’Toole, Richard Jennings
Proc. ASME. PVP2022, Volume 3: Fluid-Structure Interaction; High Pressure Technology, V003T05A014, July 17–22, 2022
Publisher: American Society of Mechanical Engineers
Paper No: PVP2022-79815
https://doi.org/10.1115/PVP2022-79815
View Paper titled, Predicting the Projectile Velocity of a Two-Stage Gas Gun Using Machine Learning
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Topics: Machine learning, Projectiles
Proceedings Papers

Machine Learning Modeling of Dynamic Strength of Resistance Spot Welds in High Strength Steels

Jesse B. Zhu, Xian-Kui Zhu, Wei Zhang
Proc. ASME. PVP2021, Volume 4: Materials and Fabrication, V004T06A020, July 13–15, 2021
Publisher: American Society of Mechanical Engineers
Paper No: PVP2021-63093
https://doi.org/10.1115/PVP2021-63093
View Paper titled, Machine Learning Modeling of Dynamic Strength of Resistance Spot Welds in High Strength Steels
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Topics: Artificial neural networks, Electrical resistance, High strength steel, Machine learning, Modeling, Welded joints, Welding
Proceedings Papers

Coupling of Neutron Noise and Dynamic Finite Element Analyses to Perform Remote Condition Monitoring for Reactor Vessel Internals

Gregory A. Banyay, Matthew J. Palamara, Stephen D. Smith
Proc. ASME. PVP2021, Volume 3: Fluid Structure Interaction; High Pressure Technology, V003T04A006, July 13–15, 2021
Publisher: American Society of Mechanical Engineers
Paper No: PVP2021-62201
https://doi.org/10.1115/PVP2021-62201
View Paper titled, Coupling of Neutron Noise and Dynamic Finite Element Analyses to Perform Remote Condition Monitoring for Reactor Vessel Internals
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Topics: Condition monitoring, Finite element analysis, Heat shielding, Machine learning, Neutrons, Noise (Sound), Reactor vessels, Structural dynamics, Structural health monitoring
Proceedings Papers

Application of Artificial Neural Network to Multi-Variables Regression for Estimations of J-Integral for Surface Cracked Pipes

Youn-Young Jang, Nam-Su Huh, Ik-Joong Kim, Cheol-Man Kim, Young-Pyo Kim
Proc. ASME. PVP2020, Volume 6: Materials and Fabrication, V006T06A098, August 3, 2020
Publisher: American Society of Mechanical Engineers
Paper No: PVP2020-21668
https://doi.org/10.1115/PVP2020-21668
View Paper titled, Application of Artificial Neural Network to Multi-Variables Regression for Estimations of J-Integral for Surface Cracked Pipes
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Topics: Artificial neural networks, Pipes, Materials properties, Finite element analysis, Fracture (Materials), Design, Displacement, Interpolation, Machine learning, Multilayer perceptron
Proceedings Papers

Using Machine Learning Tools for Forecasting Natural Gas Market Demand

Kai Yang, Lei Hou
Proc. ASME. PVP2020, Volume 8: Operations, Applications, and Components, V008T08A028, August 3, 2020
Publisher: American Society of Mechanical Engineers
Paper No: PVP2020-21378
https://doi.org/10.1115/PVP2020-21378
View Paper titled, Using Machine Learning Tools for Forecasting Natural Gas Market Demand
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Topics: Load forecasting, Machine learning, Natural gas, Pipelines, Support vector machines
Proceedings Papers

Application of Gaussian Process Regression for the Accuracy Assessment of a Three-Dimensional Strain-Based Model

Yueying Li, Yong Li, Sherif Hassanien, Chike Okoloekwe, Samer Adeeb
Proc. ASME. PVP2019, Volume 3: Design and Analysis, V003T03A098, July 14–19, 2019
Publisher: American Society of Mechanical Engineers
Paper No: PVP2019-94039
https://doi.org/10.1115/PVP2019-94039
View Paper titled, Application of Gaussian Process Regression for the Accuracy Assessment of a Three-Dimensional Strain-Based Model
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Topics: Computer simulation, Engineering simulation, Errors, Finite element analysis, Geometry, Machine learning, Pipelines, Pipes, Probability, Simulation
Proceedings Papers

Welding Simulation Integrated With Machine Learning to Train a Meta-Model for Fast Exploration of Various Weld Sequence Scenarios

Mahyar Asadi, Mohammad Mohseni, Majid Tanbakuei Kashani, Michael Fernandez, Mathew Smith
Proc. ASME. PVP2019, Volume 6B: Materials and Fabrication, V06BT06A061, July 14–19, 2019
Publisher: American Society of Mechanical Engineers
Paper No: PVP2019-93672
https://doi.org/10.1115/PVP2019-93672
View Paper titled, Welding Simulation Integrated With Machine Learning to Train a Meta-Model for Fast Exploration of Various Weld Sequence Scenarios
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Topics: Machine learning, Simulation, Trains, Welding, Algorithms, Welded joints, Engineers, Transients (Dynamics)
Proceedings Papers

Application of Machine Learning in Pipeline Integrity Reliability

Eunice Yin, Phil Fernandes, Janine Woo, Doug Langer, Sherif Hassanien
Proc. ASME. PVP2019, Volume 2: Computer Technology and Bolted Joints, V002T02A027, July 14–19, 2019
Publisher: American Society of Mechanical Engineers
Paper No: PVP2019-93623
https://doi.org/10.1115/PVP2019-93623
View Paper titled, Application of Machine Learning in Pipeline Integrity Reliability
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Topics: Machine learning, Pipeline integrity, Reliability, Pipelines, Safety, Engineers, Algorithms, Failure, Filtration, Inspection
Proceedings Papers

Estimating Collapse Pressure of Centralizer Subs From Machine Learning Models

Ishita Chakraborty
Proc. ASME. PVP2019, Volume 2: Computer Technology and Bolted Joints, V002T02A028, July 14–19, 2019
Publisher: American Society of Mechanical Engineers
Paper No: PVP2019-93638
https://doi.org/10.1115/PVP2019-93638
View Paper titled, Estimating Collapse Pressure of Centralizer Subs From Machine Learning Models
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Topics: Collapse, Machine learning, Pressure, Finite element analysis, String, Regression models, American Petroleum Institute, Natural gas wells, Testing, Trains
Proceedings Papers

The Role of Uncertainty in Machine Learning As an Element of Control for Material Systems and Structures

Muthu Ram Prabhu Elenchezhian, Md Rassel Raihan, Kenneth Reifsnider
Proc. ASME. PVP2018, Volume 6B: Materials and Fabrication, V06BT06A077, July 15–20, 2018
Publisher: American Society of Mechanical Engineers
Paper No: PVP2018-84930
https://doi.org/10.1115/PVP2018-84930
View Paper titled, The Role of Uncertainty in Machine Learning As an Element of Control for Material Systems and Structures
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Topics: Machine learning, Uncertainty, Composite materials, Physics, Aircraft, Artificial neural networks, Control systems, Failure, Algorithms, Density
Proceedings Papers

Identification of Two-Phase Flow Patterns Using Support Vector Classification

Ines Benito, Njuki W. Mureithi
Proc. ASME. PVP2017, Volume 4: Fluid-Structure Interaction, V004T04A027, July 16–20, 2017
Publisher: American Society of Mechanical Engineers
Paper No: PVP2017-65179
https://doi.org/10.1115/PVP2017-65179
View Paper titled, Identification of Two-Phase Flow Patterns Using Support Vector Classification
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Topics: Two-phase flow, Flow (Dynamics), Support vector machines, Boilers, Design, Flow measurement, Artificial intelligence, Dynamics (Mechanics), Flow visualization, Flow-induced vibrations