Bibliographic record
Abstract
The main goal of this study is to perform simulations of droplet dynamics using Truchas, a LANL-developed computational fluid dynamics (CFD) software, and compare them to a computational study of Hysing et al.[IJNMF, 2009, 60:1259]. Understanding droplet dynamics is of fundamental importance in liquid-liquid extraction, a process used in the nuclear fuel cycle to separate various components. Simulations of a single droplet rising by buoyancy are conducted in two-dimensions. Multiple parametric studies are carried out to ensure the problem set-up is optimized. An Interface Smoothing Length (ISL) study and mesh resolution study are performed to verify convergence of the calculations. ISL is a parameter for the interface curvature calculation. Further, wall effects are investigated and checked against existing correlations. The ISL study found that the optimal ISL value is 2.5{Delta}x, with {Delta}x being the mesh cell spacing. The mesh resolution study found that the optimal mesh resolution is d/h=40, for d=drop diameter and h={Delta}x. In order for wall effects on terminal velocity to be insignificant, a conservative wall width of 9d or a nonconservative wall width of 7d can be used. The percentage difference between Hysing et al.[IJNMF, 2009, 60:1259] and Truchas for the velocity profiles vary from 7.9% to 9.9%. The computed droplet velocity and interface profiles are found in agreement with the study. The CFD calculations are performed on multiple cores, using LANL's Institutional High Performance Computing.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".