Modeling of Dry-Out Incipience for Flow Boiling in a Circular Microchannel at a Uniform Heat Flux
Bibliographic record
Abstract
Dry-out is an essential phenomenon that has been observed experimentally in both slug and annular flow regimes for flow boiling in mini and microchannels. The dry-out leads to a drastic drop in heat transfer coefficient, reversible flow and may cause a serious damage to the microchannel. Consequently, the study and prediction of this phenomenon is an essential objective for flow boiling in microchannels. The aim of this work is to develop an analytical model to predict the critical heat flux (CHF) based on the prediction of liquid film variation in annular flow regime for flow boiling in a horizontal uniformly heated circular microtube. The model is developed by applying one-dimensional (1D) separated flow model for a control volume in annular flow regime for steady, and sable saturated flow boiling. The influence of interfacial shear and inertia force on the liquid film thickness is taken into account. The effects of operating conditions, channel sizes, and working fluids on the CHF have been investigated. The model was compared with 110 CHF data points for flow boiling of various working fluids, (water, LN2, FC-72, and R134a) in single and multiple micro/minichannels with diameter ranges of (0.38≤Dh≤3.04 mm) and heated-length to diameter ratios in the range of (117.7 (117.7≤Lh/D≤470)470). Additionally, three CHF correlations developed for saturated flow boiling in a single microtube have been employed for the model validation. The model showed a good agreement with the experimental CHF data with mean absolute error (MAE) = 19.81%.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".