Dynamic Heat Transfer Inside Multilayered Packages with Arbitrary Heat Generations
Bibliographic record
Abstract
In most engineering applications, e.g., hybrid electric vehicles, the multilayered electronic packages generate arbitrary heat over a transient thermal duty cycle. In addition, the outer surface of such media endures time-dependent temperature as a result of variable coolant temperature during driving/duty cycles. As such, a new analytical model is developed to predict transient heat conduction inside multilayered composite media with arbitrary heat generation inside the layers. It is assumed that the temperature of the outer surface varies periodically over time. New compact closed-form relationships are developed for calculating 1) the temperature distribution inside multilayered media, 2) the average temperature of each layer, and 3) the interfacial heat flux. As an example, the methodology is applied to a two-concentric-cylinder composite. A detailed parametric study is conducted, and the critical values for the dimensionless parameters are evaluated; beyond these values, the temperature field inside the media is not affected considerably for any combination of other variables. It is shown that there is an optimum angular frequency that maximizes the amplitude of the interfacial heat flux. An independent numerical simulation is also performed using commercially available software ANSYS; the maximum relative difference between the obtained numerical data and the analytical model is less than 2%.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".