A Modified Upwind-Biased Strategy to Calculate Flow on Structured-Unstructured Grid Topologies
Bibliographic record
Abstract
A numerical upwind-biased procedure which respects the essence of upwinding is suitably extended in order to reduce the false diffusion induced by a first-order approximation. In this regard, some arbitrarily first and second order gradient terms are added to the primary upwind approximation. The additional terms are then discretized using secondorder schemes which essentially produce dispersive errors. The suitable choices for the weights of the new added terms result in lowering the dissipative role of the original upwind scheme. Additionally, the implicit appearance of the third-order terms, which are the consequences of second-order discretizations, hrlps to reduce the dissipative impact of the original scheme. The extended formulations are then used to solve the flow on unstructured finite element grids. The performance of the derived formulations is eventually tested through solving benchmark test cases. The current results indicate high capability and accuracy of the formulation even using coarse grid distributions.
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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 | 0.000 | 0.000 |
| 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".