Experimental investigation on two‐phase flow maldistribution in parallel minichannels with U‐type configuration
Bibliographic record
Abstract
Abstract Two‐phase flow in parallel minichannels finds a number of applications. Maldistribution between parallel channels reduces both the thermal and fluid‐dynamic performances. To reduce the maldistribution effect, it is important to have information about the phase split in individual channels. The present study brings out the effects of various parameters like the channel diameter, number of channels, two‐phase flow regimes, and void fraction on the flow split in two‐phase flow inside a system of parallel channels for a U‐type configuration. Experiments are carried out with the plug and slug regimes typical to minichannel flows. High speed photography is used for flow visualization and the pressure drop values in individual channels are measured with a differential pressure transmitter to quantify maldistribution. The time averaged void fraction is found using an image processing technique. A counterintuitive non‐monotonous distribution of the void fraction in the channels brings out the fact that in two‐phase flow splitting, the relative distribution of the two phases does not depend on pressure drop alone. Flow configuration and the two‐phase flow regime in the header play a key role. An analysis with the existing separated flow model modified for minichannels reveals that although it is possible to estimate the orders of magnitude with respect to splitting, better splitting models still need to be developed. An empirical correlation for variation of the normalized pressure drop in the parallel minichannels, as a function of dimensionless distance along the header, is developed for each of the investigated flow regimes.
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 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 | 0.001 |
| 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) | 0.001 | 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".