Parametric Analysis of the Effect of the Fluid Properties and the Mesh Setup by Using the Schnerr-Sauer Cavitation Model
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The primary target of the internal combustion engines design is to lower the fuel consumption and to enhance the combustion process quality, in order to reduce the raw emission levels without performances penalty.</div><div class="htmlview paragraph">In this scenario the direct injection system plays a key role for both diesel and gasoline engines. The spray dynamic behaviour is crucial in defining the global and the local air index of the mixture, which in turns affects the combustion process development. At the same time it is widely recognized that the spray formation is influenced by numerous parameters, among which also the cavitation process inside every single hole of the injector nozzle. The proper prediction of the cavitation development inside the injector nozzle holes is crucial in predicting the liquid jet emerging from them. In this mechanism the CFD simulation is of great importance because of the too small dimension of the nozzle holes, which are mostly non suitable for an accurate experimental investigation and, when they are, these analyses need to be limited to a few cases for cost reasons. Nowadays the most used cavitation model is the two-phase homogeneous mixture model, especially with the Raleigh-Plesset closure model. In the literature it is possible to find out multiple examples of validation attempts of such a model versus experimental data: generally they fail because of the systematic overestimation of the mass flow with respect to the experimental data. The main parameters identified as responsible are the fluid properties, mostly in terms of the liquid density value, and the mesh setup.</div><div class="htmlview paragraph">The focus of the present paper is to investigate the effective weight of each single fluid parameter, both for liquid and vapor phase, plus the mesh setup on the final pressure difference-mass flow rate curve, in terms of curve shape, curve values and evaluation of the cavitation onset/development.</div></div>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".