Bubble Dynamics and Flow Boiling Characteristics in a Chemically Patterned Microchannel
Bibliographic record
Abstract
Flow boiling heat transfer in microchannels is of significant interest for thermal management applications, where the latent heat of phase change offers an efficient method to dissipate large heat fluxes in a compact device, such as a mciro heat spreader or a heat pipe. However, a significant challenge for the implementation of microscale phase change heat spreaders is associated with micro/nano flow instabilities due to insufficient micro/nano bubble removal, leading to local liquid dry-out that severely limits the heat removal efficiency. Furthermore, for the various heat transfer mechanisms involved, it is difficult to predict the location of nucleation sites at which the onset of nucleate boiling (ONB) occurs. In this paper, a chemcially patterned surface has been developed to manipulate nucleation boiling in a micro fluid flow channel. Bubble dynamics and heat transfer with shear force conditions in a heterogeneous wettability microchannel were studied experimentally and compared with a homogeneous hydrophilic microchannel. The bubble nucleation site and coalescence are studied experimentally. The effects of chemically patterned surfaces on bubble nucleation and bubble pinning and depinning in a microchannel will be investigated utilizing high speed visualization techniques and analytical modelling. Effects of mass flux on flow boiling in a wettability patterned microchannel are studied. Chemically patterned surfaces are manufactured on glass/silicon wafers. It is found that the heat transfer coefficient can be significantly enhanced by chemical patterns in comparison to a hydrophilic surface. The surface temperature with chemically patterned surfaces is lower than hydrophilic surfaces.
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 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".