Enhanced Microconvection Through Distributed Heat Source Modulation
Bibliographic record
Abstract
Much research in small-scale natural convection is targeted towards improved passive cooling of microelectronic devices (with increasing dissipative heat flux, higher operating frequencies, and increased component density). Small-scale convection also plays a central role in some more recent miniaturization efforts such as chemical analysis systems and energy conversion devices. In general, the small length-scales associated with these systems greatly inhibit natural convection heat transfer and species transport. The focus of this study is the enhancement of natural convection based heat transfer through independent modulation of heat fluxes from a planar array of distributed sources. Unsteady heat generation is common in electronic components, and more importantly, small-scale systems can be designed to induce dynamic heat fluxes. In this work, the heat transfer resulting from distributed and modulated heat sources on the order of 100μm–1000μm in 2D enclosures filled with air are investigated numerically. The heat sources are modelled as flush-mounted sources with prescribed heat flux boundary conditions. Air adjacent to the sources is heated, and eventually, the flow structure will transition from the conduction-dominated regime to the buoyancy driven convection-dominated regime. Optimum heat transfer rates and the onset of thermal instability are governed by the size and spacing of the sources, the width-to-height aspect ratio of the enclosure and the phase shift between modulated heat sources. The thermal expansion coefficient is assumed small enough that the Boussinesq approximation is appropriate, and the continuity, momentum and energy equations are solved using computational fluid dynamics (CFD). The effects of the source size and source spacing on heat transfer rates are determined. This work provides insight on the parameters required to provide enhanced heat and mass transfer in small-scale systems exhibiting distributed and modulated heat sources.Copyright © 2005 by ASME
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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 | 0.000 | 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 | 0.000 |
| 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 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".