Bibliographic record
Abstract
The behaviour of the solution to two nonlinear heating problems in a thin cylinder of revolution of variable cross‐sectional area is analysed using asymptotic and numerical methods. The first problem is to calculate the fold point, corresponding to the onset of thermal runaway, for a steady‐state nonlinear elliptic equation that arises in combustion theory. In the limit of thin cylindrical domains, it is shown that the onset of thermal runaway can be delayed when a circular cylindrical domain is perturbed into a dumbell shape. Numerical values for the fold point for different domain shapes are obtained asymptotically and numerically. The second problem that is analysed is a nonlinear parabolic equation modelling the microwave heating of a ceramic cylinder by a known electric field. The basic model in a thin circular cylindrical domain was analysed in Booty & Kriegsmann (Meth. Appl. Anal.4 (1994) p. 403). Their analysis is extended to treat thin cylindrical domains of variable cross‐section. It is shown that the steady‐state and dynamic behaviours of localized regions of high temperature, called hot‐spots, depend on a competition between the maxima of the electric field and the maximum deformation of the circular cylinder. For a dumbell‐shaped region it is shown that two disconnected hot‐spot regions can occur. Depending on the parameters in the model, these regions, ultimately, either merge as time increases or else remain as disconnected regions for all time.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".