Fusion dynamics properties of gas‐non‐Newtonian two‐phase flow in microchannels with different branching angles
Bibliographic record
Abstract
Abstract The fusion dynamic properties of microchannels with varying branching angles is critical for optimizing the design and enhancing the performance of microfluidic devices. This study systematically reveals the mechanism of branching angle and non‐Newtonian fluid rheological properties about the bubble fusion dynamics and pressure drop in microchannels. Rheological properties of carboxymethyl cellulose sodium (CMC) solutions induce pronounced shear‐thinning effects and modify the bubble fusion mechanism compared to water. The bubble exhibits fusion only in 30° (partial flow rate) and 90° (all flow rate) channels in water. The fusion phenomenon occurs in the overall velocity range of 30°–90° channel at 0.1% CMC solution. Increasing the concentration to 0.2%–0.3% CMC expands this behaviour to the 120° branch angle, while the 150° branch angle always exhibits a stable single‐channel flow pattern. Through numerical simulation, it is found that the change in branch angle affects the fluid flow rate in channel 2. The length of the bubble is affected by the branch angle and the gas–liquid flow rate. The increase in the branch angle leads to a forward shift in the bubble generation position. The proposed single‐phase model incorporates branching angle effects and Reynolds number to predict pressure drops in single‐phase flow, while the proposed two‐phase model accounts for gas–liquid velocity variations. The new model can predict the pressure drop in single‐phase flow and two‐phase flow with errors less than 15% and 20%, respectively.
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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 | 0.000 | 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 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".