Application of parallel multi-grid method in computational fluid dynamics
Bibliographic record
Abstract
For a SIMPLE(semi-implicit method pressure-linked equations) algorithm,solving the pressure correction equation using the conjugate gradient method converges very slowly and consumes a great deal of CPU time.Especially for large length/width ratios,the research shows that a multi-grid algorithm converges faster than the conjugate gradient method by one order of magnitude.Therefore,a parallel multi-grid algorithm based on MPI was developed to decrease the computation time and improve performance.With this method,the grid file was firstly transformed into a graph format file,then the graph-partition tool METIS was utilized for domain segmentation,as it is suitable for any non-structural mixed grid.A 2-D cascade segmentation of the mixed grid was performed.Several 2-D and 3-D classical examples are given to verify the effectiveness of this method,showing a higher parallel efficiency and linear acceleration ratio.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".