On the Influence of Turbulence on the Coagulation of Droplets in the Process of Bulk Condensation in Vapor–Gas Flow
Bibliographic record
Abstract
Abstract Bulk condensation is one of the frequently encountered and exploited processes in the technologies of gas purification from impurities. The phase transition process can be conditionally divided into stages of the droplet formation and growth due to two simultaneously acting mechanisms, namely, continuing vapor condensation on the surface of formed droplets and droplet coagulation due to their collisions. Early computational estimates in regard to coagulation showed a good qualitative agreement between the calculated and experimental data, but there was a significant quantitative difference. Within the framework of the present study, a hypothesis about a possible reason of these differences is put forward: turbulent disturbances are not considered in the one-dimensional formulation. The main aim is to test the hypothesis on the need to take turbulence into account within the framework of the calculation model for expanding flow in which bulk condensation takes place. The proposed modification of the approach makes it possible to take into account the effect of turbulent disturbances on coagulation of condensation aerosol particles. This can be essential, for example, in vapor–liquid turboexpanders. The study considers the bulk condensation of heavy water vapor mixed with nitrogen, acting as a non-condensable carrier gas, in the flow part of a Laval slot nozzle with regard to coagulation and turbulence. The hypothesis on the effect of turbulence in the system of gas dynamics equations on the process of droplet (particle) coagulation of a condensing impurity in the flow is confirmed. It is found that taking turbulence into account significantly improves the numerical convergence of calculations and experiment; however, it does not provide exact agreement, which, in turn, may be caused by the adopted assumption of the Brownian coagulation approximation, as well as the use of the k–ω turbulence model. It is shown that taking turbulence into account affects the magnitude of the coagulation kernel, the maximum difference for calculations with and without turbulence being approximately 10%. Taking into account turbulence during coagulation, droplets (particles) grow to larger sizes, which in the long term makes it possible to control this process.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".