Modified Single-Fluid Cavitation Model for Pure Diesel and Biodiesel Fuels in Direct Injection Fuel Injectors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it