Establishment and simulation study of equivalent model for thermal contact resistance in electronic devices
Bibliographic record
Abstract
Abstract To accurately determine thermal contact resistance (TCR) for the thermal design of electronic devices, a simplified simulation method is first proposed for calculating thermal contact resistance. First, an equivalent geometric model is established based on actual rough surfaces Then, finite element methods are employed to calculate the thermal contact resistance. The results demonstrate that the proposed equivalent geometric model, containing contact point information, can accurately predict the thermal contact resistance, which has a deviation of 10% with the experimental data. Furthermore, the results revealed that both the contact pressure and the surface roughness of the materials significantly influenced TCR, through adjusting the gap thickness. As the contact pressure increased from 200 to 1400 kPa, the TCR decreased from to . The decrease became more gradual at high contact pressure, indicating a nonlinear relationship between pressure and TCR. On the other hand, the surface roughness affected TCR primarily through the equivalent contact point height, that is, the gap thickness between two samples. Moreover, temperature affected TCR mainly through radiative heat transfer, which was dependent on material intrinsic properties. The developed simplified calculation method can efficiently simulate TCR for electronic devices, which not only enhanced the understanding of TCR behaviour but also provided a practical tool for optimizing thermal designs in electronic devices.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".