Selection and verification of the mathematical model and mesh of The GPSD under moving water condition
Bibliographic record
Abstract
In order to ensure the accuracy of numerical simulation results for the Gill-piece separation device (GPSD), an exploration of the optimal mathematical model and mesh parameters was conducted. The Mixture model in CFX was coupled with RNG k-ɛ, SST, BSL, and SSG turbulence models to simulate the water-sand two-phase flow field in the GPSD under dynamic water conditions. By comparing the numerical simulation results with physical experimental phenomena, it is found that the velocity vector diagram calculated by the Mixture-RNG k-ɛ coupling model conforms more closely to the physical experimental phenomena (Double-layered Counterflow), and the relative error of the water-sand separation efficiency calculated by the Mixture-RNG k-ɛ coupling model is very small, only 1.77%. Thus, it can be regarded as the optimal mathematical model for numerical simulation of the GPSD under dynamic water conditions. Considering factors such as computational time, the number of meshin numerical simulation should be set to around 300,000 for the best performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".