Particle resolved simulations of liquid/solid and gas/solid fluidized beds
Bibliographic record
Abstract
The present work studies particle resolved simulations of liquid/solid and gas/solid fluidization in a cuboid domain with periodic lateral boundary conditions. The focus is on investigating particles’ dynamics, while a particular care is devoted to the spatial grid resolution and statistical time convergence of the results. A statistical analysis of particles’ motion and fluid fluctuations asserts the intrinsic differences in the flow characteristics and mixing properties of these two configurations. Results reveal anisotropic mechanisms driving particles’ motion and highlight the dominance of diffusive and convective mechanisms in liquid/solid and gas/solid regimes, respectively. Following a framework similar to that of Nicolai et al. [“Particle velocity fluctuations and hydrodynamic self-diffusion of sedimenting non-Brownian spheres,” Phys. Fluids 7(1), 12–23 (1995)], we estimate the correlation time and the fluctuation length of particles’ motion. A force budget analysis is discussed to gain more insight into the role of collision in isotropization of the system. Owing to the wide range of employed grid resolutions and accurate error analysis, the present dataset is also deemed to be useful in calibrating the grid resolution for a desired accuracy of the solution in a fluidization configuration.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".