Extrapolation Boundary Conditions for 2‐D Smoothed Particle Hydrodynamics
Bibliographic record
Abstract
ABSTRACT This paper presents a new robust treatment for smoothed particle hydrodynamics (SPH) open (inflow/outflow) and solid boundary conditions, avoiding the unphysical fluctuations and numerical noise present in existing techniques. By novel use of concepts from finite volume methods, the fluid properties from sequential dynamic particles with different normal distances to the boundaries are extrapolated to ghost particles. No so‐called mirror points are required, making the method computationally efficient and easy to implement. The new methodology is validated through a series of progressively challenging test cases. The effectiveness of the wall and inflow‐outflow boundaries is evaluated for 2‐D Poiseuille laminar flow. The performance of the wall boundary for complex geometries is demonstrated using a hydrostatic tank with a triangular wedge, followed by a conventional 2‐D dam‐break problem to capture impact pressures. A range of challenging vertical inflows rarely explored using SPH, with varying efflux velocities, demonstrate highly accurate performance of the boundary treatment, with results compared to STAR‐CCM+. Finally, the robust performance is demonstrated for flow past circular and square cylinders over a range of Reynolds numbers, showing excellent results compared to reference results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".