Influence of Bypass on Flow Through Plate Fin Heat Sinks
Bibliographic record
Abstract
Forced air convection cooling of plate fin heat sinks is typically used as an effective means of cooling microelectronic devices because of its inherent simplicity and cost effectiveness. While the increased surface area obtained by placing plate fin heat sinks in close proximity to one another can significantly reduce the boundary resistance because of the added surface area, the added pressure drop associated with a constrained flow can lead to a decrease in inter-fin flow velocity along with a decrease in heat transfer. The ability to accurately predict the distribution of fluid flow between the fins of a heat sink and the fluid flow bypassing the heat sink is critical in the design and effective operation of heat sinks used to cool electronic components. An analytical model for predicting air flow and pressure drop across the heat sink is developed by applying conservation of mass and momentum over the bypass regions and in the flow channels established between the fins of the heat sink. The model is applicable for the entire laminar flow range and any type of bypass (side, top or both) or fully shrouded configurations. During the development of the model, the flow is assumed to be steady, laminar, developing flow. The model is found in good agreement with the experimental data over a wide range of flow conditions, heat sink geometries and bypass configurations, typical of many applications found in microelectronics and related fields. Data published in the open literature are also used to show the flexibility of the models to simulate a variety of applications. The model is also correlated to a simple equation within plusmn12% confidence level for easy calculation of channel velocity through the heat sink when heat sink geometry, duct geometry and flow conditions are known.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".