Hydrodynamics and Gas Hold-Up of a Gas–Liquid Coaxial Mixing System at Different Scales Containing a Non-Newtonian Fluid
Bibliographic record
Abstract
The gas–liquid mixing phenomenon that occurs in a mixing tank containing a non-Newtonian fluid is an important process in many industrial applications, such as chemical and biochemical processing. The design and optimization of an aerated mixing tank with such characteristics is a challenging task. Most of these challenges are due to the non-Newtonian behavior of the fluid, which can lead to compartmentalization of the mixing tank and the formation of oxygen-segregated zones. These issues become more pronounced at larger scales. Therefore, the primary objective of this study was to identify the mixing dead zones and determine their impact on the overall mixing process in a coaxial mixing system at two different scales. This research focused on the evaluation of the hydrodynamics attained by a coaxial gas–liquid mixing tank through numerical and experimental methods. The study was conducted using computational fluid dynamics (CFD) and the electrical resistance tomography (ERT) method. The effects of the aeration rate, inner impeller speed, and rotating mode on the creation of dead zones were investigated.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".