Calculation of Fatigue Threshold and Crack Expansion Rate of Natural Gas Pressure Vessel Steels and Yield Strength Dependence Analysis
Bibliographic record
Abstract
In this paper, the strain law of natural gas pressure vessel steel fatigue is firstly analyzed through the stress-strain curve and steel fatigue life curve, and the finite element model of natural gas pressure vessel is constructed by combining ABAQUS simulation software, and the fatigue performance of natural gas pressure vessel steel is analyzed from the cyclic softening behavior of the material and S-N curve.Then the stress intensity factor theory combined with Paris formula is introduced to calculate the crack expansion rate, and ABAQUS and FTANC3D are jointly simulated to study the crack expansion law.Finally, the fatigue yield strength of the natural gas pressure vessel was analyzed based on the elastic-viscoplastic constitutive model combined with the finite element model.It is found that the stress intensity factor along the path under hydrostatic loading is larger than that under stresscontaining loading, but the difference in stress intensity factor is only about 1.42%.When the cracks of the natural gas pressure vessel extended to the vicinity of 20 mm, its crack extension rate showed a sharp downward trend.When the temperature comes to 900°C, the yield strength value of the steel of natural gas pressure vessel is only 280.42 MPa.Exploring the steel fatigue performance and crack extension rate of natural gas pressure vessel can help to better ensure the stable and safe operation of natural gas pressure vessel.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".