Thermal Spreading Resistance Inside Anisotropic Plates with Arbitrarily Located Hotspots
Bibliographic record
Abstract
Graphite-based anisotropic materials are becoming the key component of next-generation cooling systems in electronics and telecommunication industries. Proper use of these materials in the form of thermal spreaders, compared to conventional metallic ones, can significantly reduce the thermal stress and thermal resistance in the system. In this study, a new analytical model for temperature distribution inside anisotropic rectangular plates subjected to multiple sources and sinks on the top and bottom surfaces is presented. All lateral faces are assumed insulated. The solution is first justified for the case with single hotspots on each side and then using the superposition principle, it is extended into the general form to cover multihotspot cases. The model is validated by numerical simulation data and a perfect agreement is observed. Thermal spreading resistance is defined for the anisotropic plate and a comprehensive parametric study for optimization purpose is performed. The influence of both anisotropy and geometrical parameters on the resistance is discussed in detail and critical values are evaluated.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".