Particle dynamics in horizontal buoyant jets within linearly stratified environments
Bibliographic record
Abstract
This study presents a comprehensive investigation into the dynamic behavior of particles with varying diameters transported by horizontal buoyant jets in linearly stratified fluids. An advanced modeling approach is developed that utilizes the realizable k-ε model based on Reynolds-averaged navier–stokes equations for buoyant jets mixing with stratified fluids, coupled with the discrete element method for particle tracking. The research employs a component transport model to simulate the intricate stratified environment, characterized by high-concentration saline and freshwater layers, with its accuracy validated using previous experimental data. A detailed numerical analysis of hydrodynamics and particle transport under different outflow and particle conditions reveals insights into the fluid-particle coupling mechanism and particle transport behaviors in horizontal buoyant jets. The results identified three primary modes of particle transport: settling during the upward stage, suspension diffusion within the neutral buoyancy layer, and settling during the horizontal stage. The findings reveal that particle size, density, and volume fraction profoundly influence these transport processes, and demonstrate that particles can substantially alter the jet’s flow structure, affecting the centerline trajectory, velocity decay, and the upper and lower half-widths. Furthermore, the study offers a novel perspective on the influence of particles on jet self-similarity and the statistical characteristics of particle sedimentation, advancing the understanding of the complex interaction between stratified fluid dynamics and particle-laden buoyant jets.
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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 | 0.000 |
| 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 | 0.001 | 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 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".