A chimera approach for MP-PIC simulations of dense particulate flows using large parcel size relative to the computational cell size
Bibliographic record
Abstract
The Multiphase Particle in Cell (MP-PIC) is an Eulerian-Lagrangian numerical method that resolves the particle-particle interactions using the averages mapped from the Lagrangian parcels onto the Eulerian mesh. The MPPIC's accuracy depends on mesh quality and resolution, but the mesh resolution requirements for the Computational Fluid Dynamics (CFD) fields and MP-PIC models are not in accordance. This paper proposes a chimera approach, which implements two overlapping meshes in the Lagrangian-Eulerian framework with disparate length scales - a fine mesh for the CFD fields and a coarser mesh for the MP-PIC fields. The CFD fields are mapped to the MP-PIC mesh, while the coarse mesh fields, such as solids volume fraction and momentum source of parcels, are mapped to the finer CFD mesh. The National Energy Technology Laboratory's (NETL) Small-Scale Challenge Problems-I (SSCP-I) fluidized bed case is selected for simulations and model validation. A parametric study is conducted, which considers different drag and inter-particle stress models and different solids volume fraction limits. We show that the chimera approach results in a realistic turbulent flow field for accurate drag force calculations on parcels while preserving adequate conditions for the submodels’ validity under MP-PIC. The results are in good agreement with the experimental findings, specifically the pressure drop, Eulerian average particle velocity, and granular temperature. The chimera method is developed to overcome the averaging limitations when the particle size is comparable to the cell size or when particle collisions may not be captured accurately, and a finer mesh is required for the fluid flow.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".