The 3D Numerical Study of Flow Regimes within Bubble Column Reactor
Bibliographic record
Abstract
The majority of industries engaged in two-phase flow (air-water) or multiphase flow (air-water-solid) materials are trying to meet maximum possible achievements such as high quality of mixing and maximum rate of heat transfer, with spending the less amount of investments. Bubble column reactor (BCR), probably, is a suitable solution to the industrial aims owing to providing lots of benefits such as: effective rate of mixing, high rate of interfacial area, high heat and mass transfer rate, low rate of maintenance requirement, ease of operation and low rate of operation costs (due to the lack of moving parts).These days, there are lots of interest to use BCR in various kinds of industries such as: chemical gas cleaning, various bio technological application, and chlorination, alkylation and polymerization industries. However, the lack of understanding of flow physics such as hydrodynamic behaviour (owing to existence of coalesce and break up phenomena within reactor in high value of superficial gas velocities) as well as the complexity to evaluate the mass and heat transfer within the reactor, not only the prediction of flow behaviour but also the scale up of the BCR become very hard to achieve
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".