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Proceedings Papers

Artificial Neural Network Aided Source Locating for Hazardous Gas Leakage Based on Simulation

Min Liu, Sheng Chen, Fei Li, Jiarui Shi, Meng He, Jiangyun Wang, Guoshan Xie, Haiyi Jiang
Proc. ASME. PVP2023, Volume 2: Computer Technology & Bolted Joints; Design & Analysis, V002T02A022, July 16–21, 2023
Publisher: American Society of Mechanical Engineers
Paper No: PVP2023-106011
https://doi.org/10.1115/PVP2023-106011
View Paper titled, Artificial Neural Network Aided Source Locating for Hazardous Gas Leakage Based on Simulation
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Topics: Artificial neural networks, Leakage, Simulation, Turbulence, Vortices
Proceedings Papers

Development of a Numerical Framework for Microstructure Sensitive Fatigue Life Investigations

Manuel Henrich, Michael Dölz, Sebastian Münstermann
Proc. ASME. PVP2023, Volume 2: Computer Technology & Bolted Joints; Design & Analysis, V002T02A006, July 16–21, 2023
Publisher: American Society of Mechanical Engineers
Paper No: PVP2023-106419
https://doi.org/10.1115/PVP2023-106419
View Paper titled, Development of a Numerical Framework for Microstructure Sensitive Fatigue Life Investigations
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Topics: Fatigue life, Safety, Artificial neural networks, Modeling, Residual stresses, Steel, Surface roughness, Algorithms, Engineering simulation, Fatigue
Proceedings Papers

Response of Buried Pipelines Under Permanent Ground Movements: Physics-Informed Deep Neural Network Approach

Pouya Taraghi, Yong Li, Nader Yoosef-Ghodsi, Matt Fowler, Muntaseer Kainat, Samer Adeeb
Proc. ASME. PVP2023, Volume 2: Computer Technology & Bolted Joints; Design & Analysis, V002T03A020, July 16–21, 2023
Publisher: American Society of Mechanical Engineers
Paper No: PVP2023-106201
https://doi.org/10.1115/PVP2023-106201
View Paper titled, Response of Buried Pipelines Under Permanent Ground Movements: Physics-Informed Deep Neural Network Approach
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Topics: Artificial neural networks, Displacement, Geohazards, Physics, Pipelines
Proceedings Papers

Crack Size Measurements on Fracture Surface Images Using Deep Neural Networks for Semantic Segmentation

Johannes Rosenberger, Johannes Tlatlik, Sebastian Münstermann
Proc. ASME. PVP2023, Volume 2: Computer Technology & Bolted Joints; Design & Analysis, V002T02A019, July 16–21, 2023
Publisher: American Society of Mechanical Engineers
Paper No: PVP2023-106773
https://doi.org/10.1115/PVP2023-106773
View Paper titled, Crack Size Measurements on Fracture Surface Images Using Deep Neural Networks for Semantic Segmentation
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Topics: Artificial neural networks, Fracture (Materials), Image segmentation, Semantics, Size measurement
Proceedings Papers

Artificial Neural Networks for Predicting Burst Strength of Thick and Thin-Walled Pressure Vessels

William R. Johnson, Xian-Kui Zhu, Robert Sindelar, Bruce Wiersma
Proc. ASME. PVP2023, Volume 5: Materials & Fabrication, V005T06A087, July 16–21, 2023
Publisher: American Society of Mechanical Engineers
Paper No: PVP2023-106471
https://doi.org/10.1115/PVP2023-106471
View Paper titled, Artificial Neural Networks for Predicting Burst Strength of Thick and Thin-Walled Pressure Vessels
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Topics: Artificial neural networks, Pipelines, Pressure, Pressure vessels
Proceedings Papers

Convolutional Neural Network Models for Reconstruction and Sizing of Three-Dimensional Metal-Loss Defects in Oil and Gas Pipelines Based on Magnetic Flux Leakage Signals

Yufei Shen, Wenxing Zhou
Proc. ASME. PVP2023, Volume 5: Materials & Fabrication, V005T06A085, July 16–21, 2023
Publisher: American Society of Mechanical Engineers
Paper No: PVP2023-106001
https://doi.org/10.1115/PVP2023-106001
View Paper titled, Convolutional Neural Network Models for Reconstruction and Sizing of Three-Dimensional Metal-Loss Defects in Oil and Gas Pipelines Based on Magnetic Flux Leakage Signals
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Topics: Artificial neural networks, Corrosion, Leakage, Magnetic flux, Metals, Neural network models, Pipelines, Signals
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

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

Prediction of Depth Defects During Pipeline Magnetic Flux Leakage Detection Based on the AVOA-BPNN

Lushuai Xu, Shaohua Dong, Donghua Peng, Haotian Wei, Yundong Ma
Proc. ASME. PVP2023, Volume 7: Seismic Engineering; ASME Nondestructive Evaluation, Diagnosis and Prognosis (NDPD) Division, V007T09A011, July 16–21, 2023
Publisher: American Society of Mechanical Engineers
Paper No: PVP2023-106300
https://doi.org/10.1115/PVP2023-106300
View Paper titled, Prediction of Depth Defects During Pipeline Magnetic Flux Leakage Detection Based on the AVOA-BPNN
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Topics: Artificial neural networks, Leakage, Magnetic flux, Optimization algorithms, Pipelines
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

On the Use of Artificial Neural Network Technique for Seismic Fragility Analysis of a Three-Dimensional Industrial Frame

Gianluca Quinci, Nam Hoang Phan, Fabrizio Paolacci
Proc. ASME. PVP2022, Volume 5: Operations, Applications, and Components; Seismic Engineering; ASME Nondestructive Evaluation, Diagnosis and Prognosis (NDPD) Division, V005T08A013, July 17–22, 2022
Publisher: American Society of Mechanical Engineers
Paper No: PVP2022-83874
https://doi.org/10.1115/PVP2022-83874
View Paper titled, On the Use of Artificial Neural Network Technique for Seismic Fragility Analysis of a Three-Dimensional Industrial Frame
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Topics: Artificial neural networks, Simulation
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

A Convolution Neural Network Based Fault Diagnosis Model for Slug Flow Condition of Oil-Gas Separator

Jiaquan Liu, Lei Hou, Xin Wang, Lei Xu, Rui Zhang, Kai Yang
Proc. ASME. PVP2021, Volume 5: Operations, Applications, and Components; Seismic Engineering; Non-Destructive Examination, V005T07A001, July 13–15, 2021
Publisher: American Society of Mechanical Engineers
Paper No: PVP2021-61681
https://doi.org/10.1115/PVP2021-61681
View Paper titled, A Convolution Neural Network Based Fault Diagnosis Model for Slug Flow Condition of Oil-Gas Separator
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Topics: Artificial neural networks, Fault diagnosis, Slug flows
Proceedings Papers

Digital Twin of Buried Oil Pipe in Permafrost Regions: A Multi-Source Monitoring and Numerical Simulation Model

Beilei Ji, Xiaoben Liu, Ning Shi, Jianping Liu, Pengchao Chen, Dong Zhang, Hong Zhang
Proc. ASME. PVP2021, Volume 5: Operations, Applications, and Components; Seismic Engineering; Non-Destructive Examination, V005T07A021, July 13–15, 2021
Publisher: American Society of Mechanical Engineers
Paper No: PVP2021-61991
https://doi.org/10.1115/PVP2021-61991
View Paper titled, Digital Twin of Buried Oil Pipe in Permafrost Regions: A Multi-Source Monitoring and Numerical Simulation Model
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Topics: Computer simulation, Optimization, Permafrost, Pipes, Pipelines, Computer software, Displacement, Algorithms, Artificial neural networks, China
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

A Hybrid PSO-BPNN Model Approach for Crude Oil Pipeline Electrical Energy Consumption Forecasting

Lei Xu, Lei Hou, Yu Li, Zhenyu Zhu, Jiaquan Liu, Ting Lei
Proc. ASME. PVP2020, Volume 8: Operations, Applications, and Components, V008T08A025, August 3, 2020
Publisher: American Society of Mechanical Engineers
Paper No: PVP2020-21227
https://doi.org/10.1115/PVP2020-21227
View Paper titled, A Hybrid PSO-BPNN Model Approach for Crude Oil Pipeline Electrical Energy Consumption Forecasting
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Topics: Artificial neural networks, Crude oil, Energy consumption, Particle swarm optimization, Pipelines, Principal component analysis
Proceedings Papers

CNN Based Defect Recognition Model for Phased Array Ultrasonic Testing Images of Electrofusion Joints

Jianfeng Shi, Yangji Tao, Weican Guo, Jinyang Zheng
Proc. ASME. PVP2020, Volume 6: Materials and Fabrication, V006T06A026, August 3, 2020
Publisher: American Society of Mechanical Engineers
Paper No: PVP2020-21272
https://doi.org/10.1115/PVP2020-21272
View Paper titled, CNN Based Defect Recognition Model for Phased Array Ultrasonic Testing Images of Electrofusion Joints
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Topics: Artificial neural networks, Ultrasonic testing
Proceedings Papers

How to Select the Optimized Time Step and Mesh Size for FEM Thermal Transients Simulations of PWR Vessels and Nozzles by Means of Artificial Neural Networks

Nicolas Santucho, Martin Chimenti, Jose Duo
Proc. ASME. PVP2019, Volume 2: Computer Technology and Bolted Joints, V002T02A025, July 14–19, 2019
Publisher: American Society of Mechanical Engineers
Paper No: PVP2019-93199
https://doi.org/10.1115/PVP2019-93199
View Paper titled, How to Select the Optimized Time Step and Mesh Size for FEM Thermal Transients Simulations of PWR Vessels and Nozzles by Means of Artificial Neural Networks
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Topics: Artificial neural networks, Engineering simulation, Finite element methods, Finite element model, Nozzles, Pressurized water reactors, Simulation, Transients (Dynamics), Vessels, Stress
Proceedings Papers

A Framework for Underground Gas Storage System Reliability Assessment Considering Functional Failure of Repairable Components

Lei He, Kai Wen, Jing Gong, Changchun Wu
Proc. ASME. PVP2019, Volume 7: Operations, Applications, and Components, V007T07A019, July 14–19, 2019
Publisher: American Society of Mechanical Engineers
Paper No: PVP2019-93066
https://doi.org/10.1115/PVP2019-93066
View Paper titled, A Framework for Underground Gas Storage System Reliability Assessment Considering Functional Failure of Repairable Components
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Topics: Artificial neural networks, Failure, Reliability, Simulation, Storage