Numerical Simulation of Confined Nano-Impinging Jet in Microscale Cooling Application Using DSMC Method
Bibliographic record
Abstract
In this study, we simulate rarefied gas flow through a confined nano-impinging jet using direct simulation Monte Carlo (DSMC) method. The effects of geometrical parameters, pressure ratio, and wall conditions on the heat transfer from a hot surface are examined. Hot surface modeled via diffusive constant wall temperature. Various inlet/confining surface conditions such as specular, adiabatic, and constant temperature are implemented and the effects of them on the wall heat flux rates are studied. The results show that Knudsen number, velocity slip, and temperature jump are main reasons which specify magnitudes of wall heat flux rates. Among all geometrical parameters, H/W ratio has the greatest effect on heat transfer, where H is jet distance from the hot surface and W is the jet width. For different values of pressure ratio, the biggest quantity of wall heat flux rate relates to the lowest velocity slip case. Also for inlet/confining walls with constant temperature condition equal to coolant flow temperature, heat transfer from the hot surface was the maximum.
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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".