Modelling of Cavitation of Wash-Out Water, Ammonia Water, Ammonia Water with Increased Content Ammonia and Hydrogen Sulphide, Tar Condensate
Bibliographic record
Abstract
The aim is to design and implement a procedure of numerical modelling of cavitation of working mixtures: wash-out water, ammonia water, ammonia water with an increased content of hydrogen sulphide and ammonia, tar condensate.The numeric modelling is designed in the program Ansys Fluent using Schnerr-Sauer cavitation model.The issue of these liquids modelling can be solved by the cavitation simulation of water admixtures.Working fluids contain the following main ingredients: water, ammonia, carbon dioxide and hydrogen sulphide.Subsequently, a comparison of the amount of water vapor (reference liquid) and given fluid vapor is executed.The Schnerr-Sauer model is chosen because of good results in previous simulations for water cavitation.As a geometry is selected Laval nozzle.Modelled liquid mixtures are used in the petrochemical industry, as a filling for fluid circuits where cavitation may occur and therefore the research is needed. AbstraktCílem je navrhnout postup a provést numerické modelování kavitace pracovních směsí: vypíracího roztoku, čpavkové vody, čpavkové vody se zvýšeným obsahem čpavku a hydrogenu sulfidu, dehtového kondenzátu v programu Ansys Fluent s využitím Schnerr-Sauer kavitačního modelu.Problematiku modelování těchto kapalin je možno řešit simulací kavitace jednotlivých příměsí daného roztoku.Pracovní kapaliny obsahují tyto hlavní příměsi: vodu, amoniak, oxid uhličitý a hydrogen sulfid.Následně je provedeno porovnání množství páry vody (referenční tekutina) a páry zadaných tekutin.Schnerr-Sauer model je vybrán z důvodu dosažení dobrých výsledků při dřívějších simulacích pro vodní kavitaci.Jako geometrie je vybrána Lavalova dýza.Modelované směsi kapalin se užívají v petrochemickém průmyslu, jako náplň do tekutinových obvodů, kde se může vyskytnout kavitace a proto je tento výzkum potřebný.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".