New Approach of Gas–Liquid Computational Fluid Dynamics Simulations for the Study of Minimum Quantity Cooling With Airblast Plain-Jet Injectors
Bibliographic record
Abstract
Due to the complexity of multiphase flows, they are often studied with numerical simulations. These simulations must be validated with experimental results. This paper introduces a new approach to initialize the continuous phase of gas–liquid flows generated by airblast nozzles for microlubrication applications with a recently modified commercial computational fluid dynamics (CFD) code FINE™/Open. Microlubrication is a technology used in metal machining where the coolant flow rate is lower than with conventional flood cooling. In this paper, single-phase gas and two-phase liquid–gas flows are studied. The continuous phase is simulated using Reynolds-averaged Navier–Stokes (RANS) equations coupled with a k–ε turbulence model and the dispersed phase is simulated using a Lagrangian method. To validate these simulations, particle image velocimetry (PIV) and particle dynamics analysis (PDA) measurements have been performed. This study illustrates the possibility of performing complex two-phase simulations with the help of single-phase studies to initialize the continuous phase of the flow (i.e., the gas). The single-phase flow also helps in estimating the magnitudes of the droplet velocities.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".