Droplet Impact: A GPU Based Smoothed Particle Hydrodynamics (SPH) Approach
Bibliographic record
Abstract
A parallel GPU compatible Lagrangian mesh free particle solver for multiphase fluid flow based on Smoothed Particle Hydrodynamics (SPH) scheme is developed and used to capture the interface evolution during droplet impact. To solve Navier-Stokes equations, the computational domain is discretized using fluid particles. Surface tension is modeled employing the multiphase scheme of Hu et al. In order to precisely simulate the wetting phenomena, a method based on the work of Šikalo et al. is used and compared to ensure accurate dynamic contact angle calculation. Using this method, accurate perditions were obtained for droplet contact angle during equilibrium and spreading. A simple analytical model is developed to predict maximum droplet spread diameter after impact. Solver predictions agreed well to analytical results. To improve stability and performance of the solver, a customized reduction algorithm is used on the shared memory of GPU. Speedup using a variety of different memory management algorithms on GPU-CPU is studied. The proposed algorithm is validated using the Rayleigh-Taylor instability test. Droplet impact simulations are compared side by side against a Volume of Fluid (VOF) solver to ensure accuracy and robustness. GPU speed ups of up to 120 times faster than a single processor CPU were obtained. Variations of droplet spread factor and recoil height during the impact are shown to be in good agreement with experimental 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".