Thermal Contact Conductance of Non-Flat, Rough, Metallic Coated Metals
Bibliographic record
Abstract
Abstract Thermal contact conductance is an important consideration in such applications as nuclear reactor cooling, electronics packaging, spacecraft thermal control, and gas turbine and internal combustion engine cooling. In many instances, the highest possible thermal contact conductance is desired. For this reason, soft, high conductivity, metallic coatings are sometimes applied to contacting surfaces (often metallic) to increase thermal contact conductance. O’Callaghan et al. (1981) as well as Antonetti and Yovanovich (1985, 1988) developed theoretical models for thermal contact conductance of metallic coated metals, both of which have proven accurate for flat, rough surfaces. However, these theories often substantially overpredict the conductance of non-flat, rough, metallic coated metals. In the present investigation, a semi-empirical model for flat and non-flat, rough, uncoated metals, previously developed by Lambert and Fletcher (1996), is employed in predicting the conductance of flat and non-flat, rough, metallic coated metals. The models of Antonetti and Yovanovich (1985, 1988) and Lambert and Fletcher (1996) are compared to experimental data from a number of investigations in the literature. This entailed analyzing the results for a number of metallic coating/substrate combinations on surfaces with widely varying flatness and roughness. Both models agree well with experimental results for flat, rough, metallic coated metals. However, the semi-empirical model by Lambert and Fletcher (1996) is more conservative than the theoretical model by Antonetti and Yovanovich (1985, 1988) when compared to the majority of experimental results for non-flat, rough, metallic coated metals.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".