Transient coupled thermo-elasticity analysis of a temperature-dependent thick-walled cylinder under cyclic thermo-mechanical loads
Bibliographic record
Abstract
Abstract Several studies have reported the solution of the classical coupled thermo-elasticity for thick-walled cylinders under different boundary conditions; however very limited studies have been conducted on the analysis of temperature-dependent thick-walled cylinders under cyclic nonlinear boundary conditions. Therefore, this study investigates the transient response of temperature-dependent thick-walled cylinders under cyclic nonlinear thermo-mechanical loads based on classical coupled thermo-elasticity. Also, it studies the impact of considering temperature-dependent material properties (TDMP) and temperature-independent material properties (TIMP) hypotheses on the accuracy of the results. The governing equations of the classical nonlinear coupled thermo-elasticity are numerically solved based on the finite-difference method, namely the Crank-Nicolson method. Then, an experimental setup is designed to further investigate the impact of considering TDMP and TIMP through a temperature measurement of the outer surface of a machine gun barrel under burst shooting. It has been found that considering TIMP overestimates the residual temperature and residual stress values through the cylinder thickness compared with TDMP. Moreover, based on the temperature measurement, TDMP estimates results with higher accuracy of 7% than TIMP.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".