Dynamic Load Balancing for Vorticity-Based Polynomial Adaptation of Turbulent Flows
Bibliographic record
Abstract
This paper introduces a novel non-dimensional dynamically load-balanced vorticity-based polynomial adaptation technique for turbulent flows using a high-order unstructured spatial discretization. We verify the Dynamic Load Balancing (DLB) implementation by performing simulations of an Euler Vortex (EV) and illustrate the accuracy, efficiency, and speed up factor when applied to large-scale applications, by performing simulations of turbulent flow over a three-dimensional circular cylinder, and turbulent flow over a three-dimensional NACA 0020 airfoil at a fixed angle of attack. Results demonstrated that the DLB algorithm is capable of increasing the efficiency of the parallel adaptive simulations by dynamically distributing computation load among processors when paired with polynomial adaptation. It also increases the granularity of adaptive simulations by increasing the scalability. Results demonstrate the p-adaptative scale resolving simulations are feasible using the proposed DLB approach.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".