Inverse analysis for the determination of heat transfer coefficient
Bibliographic record
Abstract
The parallel hot wire technique is considered an effective and accurate means of experimental measurement of thermal conductivity. However, the assumptions of infinite medium and ideal infinitely thin and long heat source lead to some restrictions in the applicability of this technique. To make an effective experiment design, a numerical analysis should be carried out a priori, which requires a precise specification of the heating source strength and the heat transfer coefficient on the external surface. In this work, a more accurate physical and mathematical modeling of an experimental setup based on the parallel hot wire method is considered to estimate the two above-mentioned parameters from noisy temperature histories measured inside the material. Based on a sensitivity analysis, the heating source strength is estimated first using early time measurements. With such estimated value, determination of the heat transfer coefficient using temperatures measured at later times is then considered. The Levenberg–Marquardt (LM) method is successfully applied using a single experiment for the inverse solution of the two present parameter estimation problems. Estimates of this gradient-based deterministic method are validated with a stochastic method (Kalman filter). The effects of the measurement location, the heating duration, the measurement time step, and the LM parameter on the estimates and their associated confidence bounds are investigated. Used in the traditional fitting procedure of the parallel hot wire technique, the estimated heating source power provides a reasonable agreement between fitted and exact values of the thermal conductivity and the thermal diffusivity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".