Review of Thermal Joint Resistance Models for Non-Conforming Rough Surfaces in a Vacuum
Bibliographic record
Abstract
The thermal contact resistance (TCR) problem is categorized into three different problems: geometrical, mechanical, and thermal. Each problem includes a macro and micro scale sub-problem; existing theories and models for each part are reviewed. Empirical correlations for microhardness, and the equivalent (sum) rough surface approximation are discussed. Suggested correlations for estimating the mean absolute surface slope are summarized and compared with experimental data. The classical conforming rough contact models, i.e elastic and plastic, as well as elastoplastic models are reviewed. A set of scale (dimensionless) relationships are derived for the contact parameters, i.e. the mean microcontact size, number of micro-contacts, density of microcontacts, and the external load as functions of dimensionless separation, for the above models. These scale relationships are plotted; it is graphically shown that the behavior of these models, in terms of the contact parameters, are similar. The most common assumptions of existing thermal analysis are summarized. As basic elements of thermal analysis, spreading resistance of a circular heat source on a half-space and flux tube are reviewed, also existing flux tube correlations are compared. More than 400 TCR data points collected by different re-searchers during last forty years are grouped into two limiting cases: conforming rough, and elasto-constriction. Existing TCR models are reviewed and compared with the experimental data at these two limits. It is shown that the existing theoretical models do not cover both of the above-mentioned limiting cases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".