CFD-DEM Simulation Of Multi-Particle Arching At Sand Filter Opening
Bibliographic record
Abstract
The primary motivation of this research is to offer an insight into the conditions and parameters that influence on the formation, stabilization, destruction and reformation of the multi-particle sand arching (bridging) as an efficient particle retention mechanism occurred at filter opening.The arching phenomenon is numerically explored by coupling two tools: CFD to model the fluid flow, and DEM to model the particle flow.The coupling is done in STAR-CCM+ (SIEMENS PLM).In this research, the arching at the filter opening at the micro-scale with heavy oil as the carrier phase of particles (in oil sand reservoirs) is investigated.The research outcome of this study is a computational fluid dynamic (CFD) -discrete element method (DEM) model cable of predicting multi-particle arch formation, stabilization, breakage and reformation.In particular, some of the parameters and conditions that could affect multi-particle arch performance are also studied such as size and shape of the particles and particle size distribution.Applying and advancing the knowledge gained in this research will help the research industry partner make better decisions about filter selection and filter opening design.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".