Usage of Isotropic Hardening Model Enhanced by Power Law in Simulation of Pipe Material
Bibliographic record
Abstract
Abstract Analysis of in-service pressure vessels, pressurized components, and buried pipelines made from carbon steels with high tensile properties and a narrow range between their ultimate stress and yield stress are subject to crack-like flaws. Analysis of these structures requires consideration of various mechanical loads and environmental factors such as temperature, internal pressure, soil pressure, and ground movement. These structures often are subject to design features that can generate or enlarge stress concentrations or deformation over time, potentially resulting in failure. Current standards and assessment procedures offer methodologies for evaluating structural integrity and addressing flaws. The finite element method (FEM) is a powerful tool used for analyzing these structures, but its capability to predict fracture onset, especially in ductile and brittle materials, remains limited. To overcome these limitations, the extended finite element method (XFEM) has been developed, allowing for more accurate modelling of crack propagation and interaction with the surrounding material. However, widespread adoption is hindered by challenges in obtaining experimentally verified material properties tailored for XFEM analysis. This study aims to enhance traditional plasticity models and employ XFEM coupled with the cohesive zone model (CZM) to accurately simulate fracture criteria, providing a promising approach for evaluating flaw behaviour in pipeline steel specimens with different geometric configurations. Force-displacement data were obtained from previous experimental and numerical investigations of pipeline materials using X65 material with round and flat specimens. We integrated various specimen geometries into the Abaqus program with dimensions identical to the published data. Symmetry was utilized whenever appropriate to reduce computational time. Our results indicate that the isotropic hardening model can replicate the force-displacement behaviour of a variety of specimens with various groove radii, up to a particular strain limit, which is dependent on the specimen geometry and stress state. Beyond this limit, the simulations deviate from experimental observations. However, by enhancing the material data through the power law, a higher limit can be obtained. Furthermore, we demonstrate that through the established plasticity platform and by using XFEM-CZM, the necking section can be modelled correctly without the need for damage mechanics models, providing a verification base for the XFEM-CZM methodology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".