Testing Analysis of Freezing Phenomenon in Conventional Carbon Steel Pipes
Bibliographic record
Abstract
Solidification of water in closed piping systems is a common phenomenon in both the industrial and residential sectors.In some cases, the resulting damage may be both costly and dangerous and thus warrants attention.Therefore, further study of the cooling and freezing behavior of stagnant water within closed pipes seems to be necessary.In this regard, several experiments were performed to investigate the freezing behavior of water in pipes.The inelastic deformation, work hardening, and fracture of the pipes that were caused by volume expansion due to the freezing of the water were also studied.Two conventional steel pipe materials, namely ASTM A333-6 and ASTM A106-B, were used in the freezing tests.The fracture surfaces of the pipes were analyzed by using both macroscale and microscale images of the surfaces.Fractography analysis of the fractured regions was also performed by using a scanning electron microscope.It was found that the damage caused during freezing to the A106-B pipe was considerably more than that in the A333-6 pipe.The results confirm that A333-6 should be used instead of A106-B in pressure equipment, such as pipes, that are exposed to low temperatures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".