Effects of particle concentration and size on the dissipation rate and turbulent kinetic energy of oil
Bibliographic record
Abstract
Abstract The presence of particles in oil can change the quasi‐sequence structure of the turbulent flow of the oil, and it is extremely important to explore the turbulent energy dissipation rate of the particulate‐containing oil and ensure the safe and stable operation of the oil‐using equipment. Thus, the flow field of the oil containing particles in the pipeline was experimentally studied by a particle image velocimeter (PIV); the turbulent kinetic energy of the oil was calculated using the transient velocity vector field measured by the PIV; and the dissipation rate distribution was calculated using the large eddy PIV method. The influence of different particle sizes and particle concentrations on the normal distribution of the turbulent kinetic energy and dissipation rate was analyzed. The results show that the distribution of the turbulent kinetic energy in the normal direction is non‐unidirectional and in a parabolic shape. The 25 μm particle size has a great influence on the turbulence kinetic energy and the dissipation rate of the oil; with increasing particle concentration, the flow field distribution of the turbulent kinetic energy increases in the region near the wall, and gradually decreases in the central region. The turbulent kinetic energy distribution of the oil is in the shape of a quasi‐cosine; the flow field of the dissipation rate is larger in the near‐wall region and the central region and shows an inverted ‘W’ shape. This provides a theoretical basis for improving the efficiency of oil transportation, discussing the monitoring of particulate matter in oil, and reducing oil pollution.
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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 | 0.000 | 0.000 |
| 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 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".