Modified Single-Fluid Cavitation Model for Pure Diesel and Biodiesel Fuels in Direct Injection Fuel Injectors
Bibliographic record
Abstract
A cavitation model has been developed for the internal two-phase flow of diesel and biodiesel fuels in fuel injectors under high injection pressure conditions. The model is based on the single-fluid mixture approach with newly derived expressions for the phase change rate and local mean effective pressure—the two key components of the model. The effects of the turbulence, compressibility, and wall roughness are accounted for in the present model and model validation is carried out by comparing the model predictions of probable cavitation regions, velocity distribution, and fuel mass flow rate with the experimental measurement available in literature. It is found that cavitation inception for biodiesel occurs at a higher injection pressure, compared to diesel, due to its higher viscosity. However, supercavitation occurs for both diesel and biodiesel at high injection pressures. The renormalization group (RNG) k-ɛ model for turbulence modeling is reasonable by comparing its performance with the realizable k-ɛ and the shear stress transport (SST) k-ω models. The effect of liquid phase compressibility becomes considerable for high injection pressures. Wall roughness is not an important factor for cavitation in fuel injectors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".