Assessment of Different Cavitation Models in Mixture and Eulerian Framework for Two-Phase Flow in Diesel Injectors
Bibliographic record
Abstract
A comparative study using different cavitation models in mixture and Eulerian framework is carried out for the analysis of two-phase flows in diesel injectors. Three different cavitation models are investigated here: recently developed modified single-fluid, Schnerr-Sauer and Zwart-Gerber-Belamri models. The last two models have been implemented in both mixture and Eulerian framework. The numerical predictions are compared both qualitatively and quantitatively with experimental results available in the literature. Qualitative assessments have been carried out with experimental images of two-phase flow in an optically accessible nozzle. Quantitative comparisons have been done with measured mass flow rates and velocity profiles. It appears that at low pressure differentials there can be considerable discrepancy in the predictions of the vapour distribution from the three models considered. The modified single-fluid approach turns out to be comparatively better with respect to the other two models. Implementation in mixture and Eulerian framework yields noticeable differences in the results because of the relative velocity of the two phases. Numerical experiments have been carried out with different two-phase turbulence modelling approaches, pressure-velocity coupling algorithms, gradient calculation methods and under-relaxation factors to assess the robustness of the models. Additionally comparisons have been carried out for conditions under high inlet pressure in an axisymmetric nozzle to understand the performance of the models under realistic operating conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".