A resolved CFD–DEM investigation on granular sand sedimentation considering realistic particle shapes
Bibliographic record
Abstract
In this study, a resolved framework coupling computational fluid dynamics (CFD) with the discrete-element method (DEM) is proposed to simulate the sedimentation of granular sand. Realistic sand particles were reconstructed by spherical harmonic representation combined with the multi-sphere clump method, and a fictitious domain method for irregular clumps was further developed to solve the fluid–particle interaction. This resolved CFD–DEM offers a direct and robust approach for computing real fluid forces on irregular-shaped granular sands, without relying on any empirical drag force models. Initially, the effectiveness and accuracy of the proposed CFD–DEM were validated through a series of single-particle free settling simulations for various ideal-shaped particles. Critical fluid–particle interacting behaviours in terms of drag force and wake structure were mainly investigated and corroborated with experimental data. The study subsequently progressed to simulate the sedimentation processes of various granular sand assemblies composed of realistic-shaped sand particles, utilising the proposed CFD–DEM. Detailed numerical analyses concentrated on particle-scale mechanics during sedimentation, including settling trajectories and velocities of particles, as well as the coordination and anisotropy of inter-particle contacts. The results and findings gained from this study provide a novel insight into the micro-mechanisms underlying the sedimentation and accumulation process of granular soils in geological environments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".