Flow Boiling Characteristics in a Microchannel with Various WettabilityPatterned Surfaces
Bibliographic record
Abstract
Flow boiling in a microchannel is an effective and attractive solution for thermal management. In this work, flow boiling was performed in microchannels with various wettability-patterned dotted surfaces. Pitch distances of dots were changed to study the wettability pattern effects on microchannel flow boiling. Glass-silicon-glass microchannels integrated with internal platinum heaters and various wettability patterns were fabricated and characterized using MEMS techniques. The transparent top of the channel allowed visualization of bubble dynamics and flow patterns. The hydraulic diameter of the microchannel is 338 m, the side length of hydrophobic dots is 72 m and the pitch distances of hydrophobic dots change from 122 m to 172 m. Bubble dynamics and flow patterns were visualized using a high-speed camera and a microscope. Heat transfer performance, including boiling curves and heat transfer coefficients (HTC), were explored experimentally under different mass flux. It is found that bubbles coalesce more frequently and flow pattern transitions are more intense with the decrease of pitch distance. In the low mass flux region, better heat transfer capability can be observed on the surface with larger pattern pitch distance, while with the increase of mass flux, a microchannel with smaller dot pitch distance presents better heat dissipation ability. Moreover, the critical heat flux (CHF) decreases with the decrease of dot pitch distance. The reasons behind these phenomena are related to the bubble coalescence, bubble density, and flow movement.
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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".