Simulations of dilute sedimenting suspensions at finite-particle Reynolds numbers
Bibliographic record
Abstract
An alternative numerical method for suspension flows with application to sedimenting suspensions at finite-particle Reynolds numbers Rep is presented. The method consists of an extended lattice-Boltzmann scheme for discretizing the locally averaged conservation equations and a Lagrangian particle tracking model for tracking the trajectories of individual particles. The method is able to capture the main features of the sedimenting suspensions with reasonable computational expenses. Experimental observations from the literature have been correctly reproduced. It is numerically demonstrated that, at finite Rep, there exists a range of domain sizes in which particle velocity fluctuation amplitudes ⟨ΔV∥, ⊥⟩ have a strong domain size dependence, and above which the fluctuation amplitudes become weakly dependent. The size range strongly relates with Rep and the particle volume fraction ϕp. Furthermore, a transition in the fluctuation amplitudes is found at Rep around 0.08. The magnitude and length scale dependence of the fluctuation amplitudes at finite Rep are well represented by introducing new fluctuation amplitude scaling functions C1, (∥, ⊥)(Rep, ϕp) and characteristic length scaling function C2(Rep, ϕp) in the correlation derived by Segre et al. from their experiments at low Rep [“Long-range correlations in sedimentation,” Phys. Rev. Lett. 79, 2574–2577 (1997)10.1103/PhysRevLett.79.2574] in the form \documentclass[12pt]{minimal}\begin{document}$\langle \Delta V_{\parallel , \perp } \rangle = \langle V_{\parallel } \rangle C_{1, ( \parallel , \perp )} ( Re_{p},\phi _{p} ) \phi _{p}^{1/3} \lbrace 1 - \text{exp} [ -L / ( C_{2} ( Re_{p}, \phi _{p} ) r_{p} \phi _{p}^{-1/3} )] \rbrace$\end{document}⟨ΔV∥,⊥⟩=⟨V∥⟩C1,(∥,⊥)(Rep,ϕp)ϕp1/3{1−exp[−L/(C2(Rep,ϕp)rpϕp−1/3)]}.
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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 | 0.001 | 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".