Simplified Stress Linearization Method, Maintaining Accuracy
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Bibliographic record
Abstract
ASME PVP Code stress linearization is needed for assessment of primary and primary-plus-secondary stresses. The linearization process is not precisely defined by the Code; as a result, it may be interpreted differently by analysts. The most comprehensive research on stress linearization is documented in the work of Hechmer and Hollinger [1]. Recent non-mandatory recommendations on stress linearization are provided in Annex 5A of Section VIII, Division 2 of ASME PVP Code [2]. In the work of Kalnins [3] some linearization questions are discussed in two examples; the first is a plane-strain problem and the second is an axisymmetric analysis of primary plus secondary stress at a cylindrical-shell/flat-head juncture. Paper [3] concludes that for the second example the linearized stresses produced by Abaqus [5] diverge, therefore they should not be used for stress evaluation for this specific case. This paper revisits the axisymmetric analysis discussed in [3] and attempts to show that the linearization difficulties can be avoided. The paper explains in details the reason for the divergence; the Abaqus program does not linearize all stress components in axisymmetric elements; two stress components are calculated from assumed formulas and all others are linearized. It is shown that when the axisymmetric structure from [3] is modeled with 3D elements, the linearization results are convergent. Further, it is demonstrated that both axisymmetric and 3D modeling produce the same and correct stress Tresca stress, if the stress is evaluated from all stress components linearized, without any further modification. The stress evaluation of the axisymmetric model of [3] is the primary-plus-secondary-stresses evaluation for which the limit analysis described in [4] cannot be used. The paper shows how the original primary-plus-secondary-stresses problem can be converted into the equivalent primary-stress problem, that is for a problem for which limit analysis can be used; it is further shown how the limit analysis had been used for verification of the linearization results.
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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 it