Additive Manufacturing Methods to Make High-Heat-Flux Heat Sinks
Bibliographic record
Abstract
Cold plates with internal flow passages were made by placing 3D printed polyvinyl alcohol (PVA) parts in channels machined in the plates, spraying metal over the polymer, and then dissolving the polymer. Aluminum and copper plates with coolant channels were made and found to have significantly lower thermal resistances than commercial cold plates. Properties for a sprayed metal layer to adhere to a polymer substrate were identified, including substrate roughness, substrate temperature during spraying and the thermal coefficients of expansion of the metal and polymer. Surface features such as pin fins and channels were deposited directly on heat-generating surfaces using thermal spray deposition. Tests were done by cooling a 1 cm2 copper surface with a surface heat flux of 50-450 W/cm2. The thermal resistance of the heater surfaces decreased resulting in heat transfer coefficients up to 50 kW/m2K. These features increased the heat transfer area by 45% and distributed cooling liquid uniformly without introducing any thermal contact resistance, unlike conventional heat sinks. Nozzles with an array of water jets were 3D printed using polymer to impinge water jets over a high heat-flux surface. Experiments were done to measure convective heat transfer from a copper surface with a surface area of 0.142 cm2 emitting heat fluxes of 50 to 900 W/cm2 while varying the water flow rate from 0.1 to 1.0 L/min. Heat transfer coefficients as high as 150,000 W/m2K were measured during impingement cooling.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".