Incorporation of Friction Coefficient in the Design Equations for Elevated Temperature Tanks
Bibliographic record
Abstract
Storage tanks operating at elevated temperatures (200 °F to 500 °F) need to consider stresses due to thermal expansions and restraints, due to the tank shell and bottom plate interactions and operating conditions in addition to the design requirements for ambient temperature tanks. Appendix M of API Standard 650 provides additional requirements and guidelines for the design of tanks operating at elevated temperatures. These are based on Karcher's method which gives a simplified procedure for determining the stresses (strain range) in the tank wall and bottom plate. A factor named “C” is used for defining the ratio of actual expansion against free expansion of the tank. Such partial expansion causes significant thermal stresses. API uses these stresses to estimate the low cycle fatigue life of the tanks. At present, a range of C values (0.25–1.0) is allowed by API without clear guidelines for selecting a suitable value. In the absence of such guidelines, a set value (like 0.85) is being used irrespective of the tank dimensions and temperature change. The restraint against free expansion is mainly a result of the friction between bottom plate, the foundation medium and the ring wall (if present). We can estimate the C factor by relating it to the friction coefficient. This is explored in the present study. This paper evaluates the current procedure and suggests an alternate method by incorporating the friction coefficient directly in the stress equations, instead of the C-factor. Use of friction coefficient provides an improved basis for selecting C and avoids some of the difficulties.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".