Paired Explicit Runge-Kutta Schemes for Ansys Fluent's Density-Based Solver
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-2127.vid Recently, a novel time integrator referred to as Paired Explicit Runge-Kutta (P-ERK) schemes, has been proposed for the solution of locally-stiff systems of equations. This approach allows different Runge-Kutta schemes with different numbers of active stages to be assigned based on local stiffness criteria. In this paper, we develop P-ERK schemes for finite volume methods. Then, we verify that P-ERK schemes obtain their designed order of accuracy using an isentropic vortex case. We then evaluate performance of P-ERK schemes in a finite volume solver with benchmark simulations including laminar flow over a circular cylinder, turbulent flow over an SD7003 airfoil, and turbulent flow over a T106A turbine blade cascade. Results demonstrate that P-ERK schemes can significantly accelerate simulations and achieve speed-up factors in excess of four when compared to a standard explicit temporal scheme, while maintaining accuracy with respect to reference data.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.060 | 0.012 |
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".