IMPROVING DROPLET BREAKUP AND VAPORIZATION MODELS BY INCLUDING HIGH PRESSURE AND TURBULENCE EFFECTS
Bibliographic record
Abstract
This article reviews recant experimental work conducted at the Laboratoire de Combustion et Systemes Reactifs (LCSR), Orleans, France, on single droplet breakup and vaporization. Emphasis is essentially put on high pressure and turbulence effects. The experimental facilities developed and the diagnostics used are first presented. Droplet breakup studies are conducted with cryogenic and noncryogenic droplets subjected to aerodynamic shear forces under high-pressure conditions. The transition criteria between droplet breakup regimes, characteristic breakup times, and secondary droplet distributions are obtained for uniquely low values of the density ratio between liquid and gas phases and systematically varied vales of droplet Weber and Reynolds numbers. Combined effects of high pressure and temperature oil droplet vaporization are also systematically explored. The variation patterns of average vaporization rates with reduced pressure and temperature are conclusively established and compared to estimates from the quasi-steady model. The influence of turbulence on droplet vaporization rates is explored in detail. It is demonstrated that droplet vaporization rates increase significantly with turbulent Reynolds number, even when the droplet size is smaller than the turbulence integral length scalp. Comprehensive correlations are established to take into account these various effects. Suggestions are made for ways of including these correlations as submodels into spray combustion numerical prediction codes and for future work to further improve them.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".