THERMAL MANAGEMENT OF HIGH-HEAT-FLUX DEVICES EDIFICE: EMBEDDED DROPLET IMPINGEMENT FOR INTEGRATED COOLING OF ELECTRONICS
Bibliographic record
Abstract
This presentation will first explore recent research developments for thermal management of high-heat-flux devices. These include detachable heat storage units, jet impingement, droplets and sprays, and phase-change cooling, heat pipes, capillary- and gravity-pumped loops. Micro-manufacturing and MEMS (Micro Electro-Mechanical Systems) will be discussed as enabling technologies for some innovative cooling schemes recently proposed. In the second part of the presentation, the development of EDIFICE: Embedded Droplet Impingement For Integrated Cooling of Electronics will be discussed. The EDIFICE project seeks to develop an integrated droplet impingement cooling device for removing chip heat fluxes in the range 70-100 W/cm2, employing latent heat of vaporization of dielectric fluids (50-100 micron droplets) to achieve these high heat removal rates. A novel feature to enable adaptive on-demand cooling is MEMS sensing (on-chip temperature, remote IR temperature and ultrasonic dielectric film thickness) and MEMS actuation. EDIFICE will be integrated within the electronics package and fabricated using advanced micro-manufacturing technologies (e.g., deep RIE and CMOS CMU-MEMS). The development of EDIFICE involves modeling, CFD simulations, and physical experimentation on test beds. This presentation will then examine jet impingement cooling of EDIFICE with a dielectric coolant and the influence of several parameters such as impinging jet diameter, jet velocity, and latent heat effects.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".