Pub Date : 2026-02-01Epub Date: 2025-11-08DOI: 10.1016/j.ijpvp.2025.105700
Davoud Shahgholian-Ghahfarokhi , Mohsen Abyani , Mohammad Karimi
This research examines the failure assessment of high-pressure, high-temperature offshore pipelines made of API 5L X65 steel with the outer diameter and wall thickness equal to 32” and 20.6 mm, subjected to complex-shaped corrosion defects. An efficient algorithm generated 700 randomly shaped defect geometries representing a more realistic corrosion morphology. Nonlinear Finite Element Analyses (FEA) determined failure pressures for each geometry. Grayscale images of defect cross-sections, annotated with FEA results, are used to train a Convolutional Neural Network (CNN) model. The CNN achieves high accuracy in predicting failure pressures, reducing error and training time compared to traditional machine learning methods by effectively extracting spatial features from images. Additionally, the defects are assessed using the DNVGL-RP-F101 code-based method. The results show a strong correlation (R2 = 97.83 %) between FEA and CNN predictions. In 578 of 700 cases, numerical failure pressures exceeded those from the code, indicating that the code-based approach is generally conservative.
{"title":"Evaluation of offshore pipeline failure due to complex shaped corrosion defects using deep learning methods","authors":"Davoud Shahgholian-Ghahfarokhi , Mohsen Abyani , Mohammad Karimi","doi":"10.1016/j.ijpvp.2025.105700","DOIUrl":"10.1016/j.ijpvp.2025.105700","url":null,"abstract":"<div><div>This research examines the failure assessment of high-pressure, high-temperature offshore pipelines made of API 5L X65 steel with the outer diameter and wall thickness equal to 32” and 20.6 mm, subjected to complex-shaped corrosion defects. An efficient algorithm generated 700 randomly shaped defect geometries representing a more realistic corrosion morphology. Nonlinear Finite Element Analyses (FEA) determined failure pressures for each geometry. Grayscale images of defect cross-sections, annotated with FEA results, are used to train a Convolutional Neural Network (CNN) model. The CNN achieves high accuracy in predicting failure pressures, reducing error and training time compared to traditional machine learning methods by effectively extracting spatial features from images. Additionally, the defects are assessed using the DNVGL-RP-F101 code-based method. The results show a strong correlation (R<sup>2</sup> = 97.83 %) between FEA and CNN predictions. In 578 of 700 cases, numerical failure pressures exceeded those from the code, indicating that the code-based approach is generally conservative.</div></div>","PeriodicalId":54946,"journal":{"name":"International Journal of Pressure Vessels and Piping","volume":"219 ","pages":"Article 105700"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2025-10-01DOI: 10.1016/j.ijpvp.2025.105673
Manish Kumar
Piping systems carry fluid from one location to another and pipe bends are one of the most critical component due to its large deformation and high stress bearing nature. Collapse moment is one the criteria that helps to determine the strength of the pipe bend. The present study carries extensive three dimensional analyses to calculate the collapse moment of pipe bends (30° to 180° with interval of 30°) using twice-elastic-slope (TES) method. This paper focuses on the strain hardening (SH) effect on TES collapse moment based on elastic perfectly-plastic (EPP) material model for different bend angle and piping thickness under in-plane (closing (IPC) and opening (IPO)) and out-of-plane (OP) bending modes. From the results, it is clear that pipe thickness has significant role on hardening effect. Under IPC and OP bending modes, thicker pipe bend shows maximum hardening effect whereas under IPO mode least pipe thickness shows maximum hardening behavior due to its deformation pattern. Strain hardening effect changes maximum when bend angle changes from 30° to 60° for all bending modes. The hardening effect does not changes much for bend angle 60° to 180° under IPC and OP bending modes. Under IPO bending, hardening effect depends on bend angle for thinner pipe bends and for thicker pipe bends, it is independent of bend angle. This study helps to evaluate the bend angle hardening behavior influence on TES collapse moment which ultimately help determine the SH material model TES collapse moment based on the EPP material model.
{"title":"Strain hardening effect on TES collapse moment of different angled pipe bends subjected to bending moments","authors":"Manish Kumar","doi":"10.1016/j.ijpvp.2025.105673","DOIUrl":"10.1016/j.ijpvp.2025.105673","url":null,"abstract":"<div><div>Piping systems carry fluid from one location to another and pipe bends are one of the most critical component due to its large deformation and high stress bearing nature. Collapse moment is one the criteria that helps to determine the strength of the pipe bend. The present study carries extensive three dimensional analyses to calculate the collapse moment of pipe bends (30° to 180° with interval of 30°) using twice-elastic-slope (TES) method. This paper focuses on the strain hardening (SH) effect on TES collapse moment based on elastic perfectly-plastic (EPP) material model for different bend angle and piping thickness under in-plane (closing (IPC) and opening (IPO)) and out-of-plane (OP) bending modes. From the results, it is clear that pipe thickness has significant role on hardening effect. Under IPC and OP bending modes, thicker pipe bend shows maximum hardening effect whereas under IPO mode least pipe thickness shows maximum hardening behavior due to its deformation pattern. Strain hardening effect changes maximum when bend angle changes from 30° to 60° for all bending modes. The hardening effect does not changes much for bend angle 60° to 180° under IPC and OP bending modes. Under IPO bending, hardening effect depends on bend angle for thinner pipe bends and for thicker pipe bends, it is independent of bend angle. This study helps to evaluate the bend angle hardening behavior influence on TES collapse moment which ultimately help determine the SH material model TES collapse moment based on the EPP material model.</div></div>","PeriodicalId":54946,"journal":{"name":"International Journal of Pressure Vessels and Piping","volume":"219 ","pages":"Article 105673"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2025-09-19DOI: 10.1016/j.ijpvp.2025.105663
Banoth Shivakumar, Ajoy Kumar Pandey
Hastelloy C-276, a high-performance nickel-molybdenum-chromium superalloy, is widely employed in pressure vessels, heat exchangers, and piping systems operating under aggressive thermal and chemical environments. Components made from this alloy frequently experience intense thermal cycles and mechanical loading during service, making a robust understanding of low-cycle fatigue (LCF) essential for safe and optimised design. In this study, the LCF behaviour of Hastelloy C-276 was systematically investigated at 550 °C, 650 °C, and 700 °C, under total strain amplitudes of ±0.25 %, ±0.4 %, and ±0.6 %. Findings reveal a substantial decline in fatigue life with both rising temperature and strain amplitude; the number of cycles to failure (Nf) decreased from 324,660 cycles at 550 °C (±0.25 %) to 282 cycles at 700 °C (±0.6 %). Dynamic strain ageing (DSA) phenomena, most prominent at 550 °C, manifested as stress–strain curve serrations and cyclic hardening, impacting fatigue performance. Fatigue life predictions using Coffin–Manson–Basquin and Ostergren energy models closely matched experimental results, with all predictions falling within a factor of 1.5 of the measured data. Fractographic analysis unveiled transgranular fracture modes with secondary cracks as temperature increased and quantified striation spacing with respect to loading conditions. These insights clarify the interplay between operational parameters and fatigue mechanisms in C-276, providing actionable guidance for the life assessment and reliable, high-temperature design of critical pressure-retaining components.
{"title":"Low-cycle fatigue behaviour of Hastelloy C-276: Cyclic hardening, life prediction, and fracture mechanisms","authors":"Banoth Shivakumar, Ajoy Kumar Pandey","doi":"10.1016/j.ijpvp.2025.105663","DOIUrl":"10.1016/j.ijpvp.2025.105663","url":null,"abstract":"<div><div>Hastelloy C-276, a high-performance nickel-molybdenum-chromium superalloy, is widely employed in pressure vessels, heat exchangers, and piping systems operating under aggressive thermal and chemical environments. Components made from this alloy frequently experience intense thermal cycles and mechanical loading during service, making a robust understanding of low-cycle fatigue (LCF) essential for safe and optimised design. In this study, the LCF behaviour of Hastelloy C-276 was systematically investigated at 550 °C, 650 °C, and 700 °C, under total strain amplitudes of ±0.25 %, ±0.4 %, and ±0.6 %. Findings reveal a substantial decline in fatigue life with both rising temperature and strain amplitude; the number of cycles to failure (N<sub>f</sub>) decreased from 324,660 cycles at 550 °C (±0.25 %) to 282 cycles at 700 °C (±0.6 %). Dynamic strain ageing (DSA) phenomena, most prominent at 550 °C, manifested as stress–strain curve serrations and cyclic hardening, impacting fatigue performance. Fatigue life predictions using Coffin–Manson–Basquin and Ostergren energy models closely matched experimental results, with all predictions falling within a factor of 1.5 of the measured data. Fractographic analysis unveiled transgranular fracture modes with secondary cracks as temperature increased and quantified striation spacing with respect to loading conditions. These insights clarify the interplay between operational parameters and fatigue mechanisms in C-276, providing actionable guidance for the life assessment and reliable, high-temperature design of critical pressure-retaining components.</div></div>","PeriodicalId":54946,"journal":{"name":"International Journal of Pressure Vessels and Piping","volume":"219 ","pages":"Article 105663"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2025-11-04DOI: 10.1016/j.ijpvp.2025.105699
Yuan-Xu Song , Kun Zhang , Jian-Ping Tan , Tao Wang , Xuan Zhang , Jie Su , Xuan Liu , Jing-Bo Yan , Peng Liu , Jian-Feng Wen , Ning Wang , Xian-Cheng Zhang
In this work the applicability of notched plate tension (NPT) and circumferentially notched tension (CNT) specimens to determine the multiaxial stress rupture criterion (MSRC) of creeping materials is evaluated by the direct and indirect methods based on the uniaxial and multiaxial (NPT and CNT specimens) creep data of a nickel-based alloy GH4169 at 650 °C. Also, the evaluation covers four commonly-used MSRCs, three of which (termed Type 1 hereafter) involve an adjustable factor, while the last (termed Type 2 hereafter) does not. Results indicate that the NPT specimen is probably not suitable for determining the MSRC for the alloy regardless of the determination method and the MSRC examined. This is because, for the Type 1 MSRC, the adjustable factor in each MSRC turns out to vary greatly depending on the notch size and the location on which relevant stresses are extracted. While for the Type 2 MSRC, the predicted multiaxial creep life depends on the location of stress extraction. Moreover, the determined MSRC based on the NPT specimen cannot be directly applied to predicting the creep life of CNT specimens, and vice versa. Interestingly, the use of CNT specimen with the same notch size of NPT specimen is found to be suitable for determining the MSRC. This is because when the skeletal point stresses of a CNT specimen with the same notch size of NPT specimen are used, the resulting adjustable factor becomes much less affected by the stress state and by the MSRC adopted. Moreover, the predicted lives of CNT specimens with different notch root radii agree well with the experimental counterparts when the determined adjustable factor is used.
{"title":"Applicability of two types of notched tension specimens to determine the multiaxial stress rupture criterion for GH4169 at 650 °C","authors":"Yuan-Xu Song , Kun Zhang , Jian-Ping Tan , Tao Wang , Xuan Zhang , Jie Su , Xuan Liu , Jing-Bo Yan , Peng Liu , Jian-Feng Wen , Ning Wang , Xian-Cheng Zhang","doi":"10.1016/j.ijpvp.2025.105699","DOIUrl":"10.1016/j.ijpvp.2025.105699","url":null,"abstract":"<div><div>In this work the applicability of notched plate tension (NPT) and circumferentially notched tension (CNT) specimens to determine the multiaxial stress rupture criterion (MSRC) of creeping materials is evaluated by the direct and indirect methods based on the uniaxial and multiaxial (NPT and CNT specimens) creep data of a nickel-based alloy GH4169 at 650 °C. Also, the evaluation covers four commonly-used MSRCs, three of which (termed Type 1 hereafter) involve an adjustable factor, while the last (termed Type 2 hereafter) does not. Results indicate that the NPT specimen is probably not suitable for determining the MSRC for the alloy regardless of the determination method and the MSRC examined. This is because, for the Type 1 MSRC, the adjustable factor in each MSRC turns out to vary greatly depending on the notch size and the location on which relevant stresses are extracted. While for the Type 2 MSRC, the predicted multiaxial creep life depends on the location of stress extraction. Moreover, the determined MSRC based on the NPT specimen cannot be directly applied to predicting the creep life of CNT specimens, and vice versa. Interestingly, the use of CNT specimen with the same notch size of NPT specimen is found to be suitable for determining the MSRC. This is because when the skeletal point stresses of a CNT specimen with the same notch size of NPT specimen are used, the resulting adjustable factor becomes much less affected by the stress state and by the MSRC adopted. Moreover, the predicted lives of CNT specimens with different notch root radii agree well with the experimental counterparts when the determined adjustable factor is used.</div></div>","PeriodicalId":54946,"journal":{"name":"International Journal of Pressure Vessels and Piping","volume":"219 ","pages":"Article 105699"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article mainly focuses on the hydrogen induced fracture behavior of austenitic stainless steel AISI 304. Experiments have shown that plastic deformation induces α′ martensite after entering the elastoplastic stage, which significantly affects hydrogen embrittlement sensitivity to varying degrees through the stress states caused by the specimen's geometries. The results indicate that stress concentration accelerates hydrogen diffusion, and the overall failure mode is more prone to hydrogen induced fracture. The lower the notch constraint, the greater the hydrostatic stress gradient, and the shear component increases the local hydrogen concentration by increasing the plastic strain. The hydrogen embrittlement sensitivity (IHE) increases firstly and then decreases with notch radius by considering both stress concentration and failure mode for normal notched specimen. The IHE of V-notched specimen is almost independent of the inclination angle, while the IHE of U-notched specimen is mainly affected by the failure mode by considering all the factors including stress concentration, failure mode, constraint and shear stress.
{"title":"Research on the factors of hydrogen induced fracture behavior of AISI 304: loading level, stress concentration and stress state","authors":"Xinting Miao , Hao Xin , Jinbo Zhang , Ping Tao , Jian Peng","doi":"10.1016/j.ijpvp.2025.105687","DOIUrl":"10.1016/j.ijpvp.2025.105687","url":null,"abstract":"<div><div>This article mainly focuses on the hydrogen induced fracture behavior of austenitic stainless steel AISI 304. Experiments have shown that plastic deformation induces α′ martensite after entering the elastoplastic stage, which significantly affects hydrogen embrittlement sensitivity to varying degrees through the stress states caused by the specimen's geometries. The results indicate that stress concentration accelerates hydrogen diffusion, and the overall failure mode is more prone to hydrogen induced fracture. The lower the notch constraint, the greater the hydrostatic stress gradient, and the shear component increases the local hydrogen concentration by increasing the plastic strain. The hydrogen embrittlement sensitivity (<em>I</em><sub>HE</sub>) increases firstly and then decreases with notch radius by considering both stress concentration and failure mode for normal notched specimen. The <em>I</em><sub>HE</sub> of V-notched specimen is almost independent of the inclination angle, while the <em>I</em><sub>HE</sub> of U-notched specimen is mainly affected by the failure mode by considering all the factors including stress concentration, failure mode, constraint and shear stress.</div></div>","PeriodicalId":54946,"journal":{"name":"International Journal of Pressure Vessels and Piping","volume":"219 ","pages":"Article 105687"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2025-09-22DOI: 10.1016/j.ijpvp.2025.105662
Harleen Kaur Sandhu , Joomyung Lee , Saran Srikanth Bodda , Abhinav Gupta , Nam Dinh
Next-generation nuclear power plants are advancing toward autonomous Online Monitoring (OLM) systems to ensure operational safety and efficiency. A critical factor in the reliability of OLM systems is the integrity of sensor data collected from the facility. Erroneous sensor data can compromise the OLM’s ability to accurately assess the plant’s condition, potentially leading to severe safety risks. For instance, in post-hazard scenarios like earthquakes, undetected faulty data may obscure degraded conditions in piping and equipment systems. Such degradation, if unchecked, can escalate into catastrophic events, including loss of coolant accidents (LOCAs). This study introduces an innovative False Signal Detection and Correction Model (FSDCM) designed to safeguard OLM data integrity. The FSDCM operates through a two-step mechanism: first, it employs statistical correlation analysis to detect false sensor data; second, it uses deep learning algorithms to correct these inaccuracies. By analyzing historical data and learning patterns, the deep learning component can overwrite erroneous sensor readings with validated data, enhancing reliability. A case study on a nuclear piping system demonstrates FSDCM’s effectiveness. Using finite element simulations, acceleration-time series signals are generated as sensor data, and random noise is introduced to simulate false signals. The FSDCM accurately identifies and corrects these anomalies across various test scenarios, showing robust detection and correction capabilities. This novel framework not only enhances operational accuracy but also plays a vital role in risk mitigation for nuclear facilities, paving the way for safer, more autonomous power plant management.
{"title":"False sensor-data detection strategy for post-hazard condition monitoring of nuclear systems using statistical approaches and long short-term memory","authors":"Harleen Kaur Sandhu , Joomyung Lee , Saran Srikanth Bodda , Abhinav Gupta , Nam Dinh","doi":"10.1016/j.ijpvp.2025.105662","DOIUrl":"10.1016/j.ijpvp.2025.105662","url":null,"abstract":"<div><div>Next-generation nuclear power plants are advancing toward autonomous Online Monitoring (OLM) systems to ensure operational safety and efficiency. A critical factor in the reliability of OLM systems is the integrity of sensor data collected from the facility. Erroneous sensor data can compromise the OLM’s ability to accurately assess the plant’s condition, potentially leading to severe safety risks. For instance, in post-hazard scenarios like earthquakes, undetected faulty data may obscure degraded conditions in piping and equipment systems. Such degradation, if unchecked, can escalate into catastrophic events, including loss of coolant accidents (LOCAs). This study introduces an innovative False Signal Detection and Correction Model (FSDCM) designed to safeguard OLM data integrity. The FSDCM operates through a two-step mechanism: first, it employs statistical correlation analysis to detect false sensor data; second, it uses deep learning algorithms to correct these inaccuracies. By analyzing historical data and learning patterns, the deep learning component can overwrite erroneous sensor readings with validated data, enhancing reliability. A case study on a nuclear piping system demonstrates FSDCM’s effectiveness. Using finite element simulations, acceleration-time series signals are generated as sensor data, and random noise is introduced to simulate false signals. The FSDCM accurately identifies and corrects these anomalies across various test scenarios, showing robust detection and correction capabilities. This novel framework not only enhances operational accuracy but also plays a vital role in risk mitigation for nuclear facilities, paving the way for safer, more autonomous power plant management.</div></div>","PeriodicalId":54946,"journal":{"name":"International Journal of Pressure Vessels and Piping","volume":"219 ","pages":"Article 105662"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2025-10-04DOI: 10.1016/j.ijpvp.2025.105669
Mohammad Tabiee, Alireza Khaloo
Pipelines have long been used for the transportation of strategic fluids worldwide. However, exposure to environmental conditions and extended service life significantly increase the risk of corrosion, which in turn reduces the structural strength of pipelines against external loads, particularly blast loading. This study numerically investigates the behavior of pipelines with internal and external corrosion subjected to blast loads. The use of fiber-reinforced polymers (FRP) is also examined as a retrofitting technique to enhance the blast resistance of corroded pipelines. A parametric study was performed to assess the influence of corrosion geometry and FRP thickness on the structural response. The results demonstrate that FRP effectively improves the blast performance of corroded pipelines under various conditions. Specifically, employing CFRP with half the pipe thickness reduces the maximum stress by over 30 % and decreases the maximum longitudinal and hoop strains by more than 55 % in certain cases. This study integrates blast–corrosion interaction with FRP retrofitting strategies within a validated Coupled Eulerian–Lagrangian (CEL) framework, providing a novel approach to assessing the dynamic performance of corroded pipelines. The findings offer quantitative engineering guidance for the protection and strengthening of buried pipelines subjected to extreme explosion scenarios.
{"title":"Investigation of corrosion effects on the performance of underground steel pipelines under blast loading and a solution for retrofitting","authors":"Mohammad Tabiee, Alireza Khaloo","doi":"10.1016/j.ijpvp.2025.105669","DOIUrl":"10.1016/j.ijpvp.2025.105669","url":null,"abstract":"<div><div>Pipelines have long been used for the transportation of strategic fluids worldwide. However, exposure to environmental conditions and extended service life significantly increase the risk of corrosion, which in turn reduces the structural strength of pipelines against external loads, particularly blast loading. This study numerically investigates the behavior of pipelines with internal and external corrosion subjected to blast loads. The use of fiber-reinforced polymers (FRP) is also examined as a retrofitting technique to enhance the blast resistance of corroded pipelines. A parametric study was performed to assess the influence of corrosion geometry and FRP thickness on the structural response. The results demonstrate that FRP effectively improves the blast performance of corroded pipelines under various conditions. Specifically, employing CFRP with half the pipe thickness reduces the maximum stress by over 30 % and decreases the maximum longitudinal and hoop strains by more than 55 % in certain cases. This study integrates blast–corrosion interaction with FRP retrofitting strategies within a validated Coupled Eulerian–Lagrangian (CEL) framework, providing a novel approach to assessing the dynamic performance of corroded pipelines. The findings offer quantitative engineering guidance for the protection and strengthening of buried pipelines subjected to extreme explosion scenarios.</div></div>","PeriodicalId":54946,"journal":{"name":"International Journal of Pressure Vessels and Piping","volume":"219 ","pages":"Article 105669"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2025-10-11DOI: 10.1016/j.ijpvp.2025.105679
Georg Veile , Jürgen Rudolph , Nina Grözinger , Martin Herzig , Michael Grimm , Stefan Weihe
{"title":"Corrigendum to “Improving fatigue testing of AISI 304L stainless steel in high temperature water regarding their complex hardening and softening material behaviour” [Int. J. Pres. Ves. Pip. Volume 218 Part B (2025) 105612]","authors":"Georg Veile , Jürgen Rudolph , Nina Grözinger , Martin Herzig , Michael Grimm , Stefan Weihe","doi":"10.1016/j.ijpvp.2025.105679","DOIUrl":"10.1016/j.ijpvp.2025.105679","url":null,"abstract":"","PeriodicalId":54946,"journal":{"name":"International Journal of Pressure Vessels and Piping","volume":"219 ","pages":"Article 105679"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2025-10-22DOI: 10.1016/j.ijpvp.2025.105690
Muhammad Bilal Jan, Mengyu Chai
Creep deformation is a critical concern in high-temperature materials and structural components operating under prolonged stress, such as those used in pressure vessels. Accurate prediction of creep rupture life is essential for ensuring the safety and longevity of components in aerospace, power generation, and other high-temperature industries. Traditionally, creep life has been predicted by widely-used empirical models such as the Larson–Miller parameter, the Monkman–Grant relationship, and the θ projection method. These conventional empirical methods suffer from inherent limitations, including over-simplified assumptions, poor generalizability, an inability to capture complex, non-linear relationships, and restricted use of input features, which limits their applicability to modern material systems. To address these limitations, Machine learning (ML), being data-driven, can simultaneously handle many input features, learn nonlinear and complex interactions, capture dependencies among multiple features, and exhibit robust generalizability. To support this capability, this review outlines a comprehensive ML workflow—from data acquisition to deployment—by summarizing existing studies in the literature, tailored for creep life prediction to guide future efforts in data-driven creep modeling. To provide clarity and depth, the literature is systematically reviewed and grouped into four key themes: hybrid modeling approaches, creep mechanism-aware models, feature selection techniques for enhanced creep life, and alloy design strategies for improved creep resistance. Finally, this review identifies key challenges such as data scarcity, a lack of physics integration and constraints in ML models, unavailability of a real-time in-situ creep life prediction framework, and difficulties in interpretability and explainability, and proposes potential future directions for these challenges, informed by both existing literature and original analytical insights. The field ML-based creep rupture life prediction lacks a review that synthesizes the available literature, outlines the limitations of existing approaches, and identifies future research directions. This review paper addresses this need by providing a clear overview to guide researchers and support further progress in the field.
{"title":"Machine learning approaches for creep rupture life prediction of metallic materials: A comprehensive review","authors":"Muhammad Bilal Jan, Mengyu Chai","doi":"10.1016/j.ijpvp.2025.105690","DOIUrl":"10.1016/j.ijpvp.2025.105690","url":null,"abstract":"<div><div>Creep deformation is a critical concern in high-temperature materials and structural components operating under prolonged stress, such as those used in pressure vessels. Accurate prediction of creep rupture life is essential for ensuring the safety and longevity of components in aerospace, power generation, and other high-temperature industries. Traditionally, creep life has been predicted by widely-used empirical models such as the Larson–Miller parameter, the Monkman–Grant relationship, and the θ projection method. These conventional empirical methods suffer from inherent limitations, including over-simplified assumptions, poor generalizability, an inability to capture complex, non-linear relationships, and restricted use of input features, which limits their applicability to modern material systems. To address these limitations, Machine learning (ML), being data-driven, can simultaneously handle many input features, learn nonlinear and complex interactions, capture dependencies among multiple features, and exhibit robust generalizability. To support this capability, this review outlines a comprehensive ML workflow—from data acquisition to deployment—by summarizing existing studies in the literature, tailored for creep life prediction to guide future efforts in data-driven creep modeling. To provide clarity and depth, the literature is systematically reviewed and grouped into four key themes: hybrid modeling approaches, creep mechanism-aware models, feature selection techniques for enhanced creep life, and alloy design strategies for improved creep resistance. Finally, this review identifies key challenges such as data scarcity, a lack of physics integration and constraints in ML models, unavailability of a real-time in-situ creep life prediction framework, and difficulties in interpretability and explainability, and proposes potential future directions for these challenges, informed by both existing literature and original analytical insights. The field ML-based creep rupture life prediction lacks a review that synthesizes the available literature, outlines the limitations of existing approaches, and identifies future research directions. This review paper addresses this need by providing a clear overview to guide researchers and support further progress in the field.</div></div>","PeriodicalId":54946,"journal":{"name":"International Journal of Pressure Vessels and Piping","volume":"219 ","pages":"Article 105690"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2025-11-11DOI: 10.1016/j.ijpvp.2025.105702
Tairui Zhang , Weiwei Zheng , Xiandong Shang , Yide Li , Lei Xu , Wenhuan Dou , Aliaksandr Kren , Ji Lang
To promote the indentation tests integrated with multi-source data acquisitions, this study systematically investigates the influence of speckle pattern preparation methodology, surface roughness, and indenter tilt angle on the acquisition of indentation parameters, including the plastic zone radius, load-depth curves, and residual indentation profile. Influence of indentation parameters variations on tensile property predictions has also been extensively investigated. Comparisons between natural and artificial speckles demonstrate high consistency in the strain contours, with plastic zone radius differences smaller than 2 %. Therefore, in practical engineering applications, method for speckle preparation can be flexibly selected based on testing conditions. Surface roughness exhibits negligible effects on all indentation data, and the accuracy for data acquisition can be roughly ensured as long as the surface roughness Ra remains smaller than 0.8 μm. In comparison, the tilt angle has pronounced effects on the load-displacement curves and indentation profiles, as well as induces elliptical deformation of the plastic zone. In engineering practice, the tilt angle should be maintained smaller than 5° whenever possible. Additionally, using indentation profiles (pile-up coefficients) and plastic zone radius extracted perpendicular to the tilt direction can mitigate the impact of tilt on test results. Furthermore, comparative analysis of different tensile property models indicates that the incremental indentation energy model, incorporating multi-source data acquisition, delivers more stable and reliable prediction results under adverse conditions involving high surface roughness and large tilt angles.
{"title":"Analysis of influencing factors on multi-source data acquisition and tensile performance prediction for spherical indentation tests (SITs)","authors":"Tairui Zhang , Weiwei Zheng , Xiandong Shang , Yide Li , Lei Xu , Wenhuan Dou , Aliaksandr Kren , Ji Lang","doi":"10.1016/j.ijpvp.2025.105702","DOIUrl":"10.1016/j.ijpvp.2025.105702","url":null,"abstract":"<div><div>To promote the indentation tests integrated with multi-source data acquisitions, this study systematically investigates the influence of speckle pattern preparation methodology, surface roughness, and indenter tilt angle on the acquisition of indentation parameters, including the plastic zone radius, load-depth curves, and residual indentation profile. Influence of indentation parameters variations on tensile property predictions has also been extensively investigated. Comparisons between natural and artificial speckles demonstrate high consistency in the strain contours, with plastic zone radius differences smaller than 2 %. Therefore, in practical engineering applications, method for speckle preparation can be flexibly selected based on testing conditions. Surface roughness exhibits negligible effects on all indentation data, and the accuracy for data acquisition can be roughly ensured as long as the surface roughness <em>R</em><sub>a</sub> remains smaller than 0.8 μm. In comparison, the tilt angle has pronounced effects on the load-displacement curves and indentation profiles, as well as induces elliptical deformation of the plastic zone. In engineering practice, the tilt angle should be maintained smaller than 5° whenever possible. Additionally, using indentation profiles (pile-up coefficients) and plastic zone radius extracted perpendicular to the tilt direction can mitigate the impact of tilt on test results. Furthermore, comparative analysis of different tensile property models indicates that the incremental indentation energy model, incorporating multi-source data acquisition, delivers more stable and reliable prediction results under adverse conditions involving high surface roughness and large tilt angles.</div></div>","PeriodicalId":54946,"journal":{"name":"International Journal of Pressure Vessels and Piping","volume":"219 ","pages":"Article 105702"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号