A Runge-Kutta-Newton-Krylov Algorithm for Fourth-Order Implicit Time Marching Applied to Unsteady Flows
Bibliographic record
Abstract
Two implicit time-marching methods are investigated for accuracy and efficiency in solving the unsteady Navier-Stokes equations. The methods considered are the second-order backwards differencing formula and the fourth-order explicit-first-stage, single-diagonal-coefficient, diagonally-implicit Runge-Kutta method. First, the efficiency of two strategies for solving the nonlinear problem arising at each time step, an approximate factorization algorithm and a Newton-Krylov algorithm, is investigated. The Newton-Krylov strategy is seen to be more efficient, especially on fine meshes. Next, the relative efficiency of the two time-marching methods is compared for two-dimensional unsteady laminar flows over a cylinder and an airfoil. The backwards differencing method with approximate factorization dual time stepping is very efficient on a coarse mesh, whereas the implicit Runge-Kutta scheme combined with the Newton-Krylov algorithm is more efficient on finer meshes and when lower errors are required. The combination of the implicit Runge-Kutta method with the Newton-Krylov algorithm is shown to be very efficient for high-fidelity time-accurate simulations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".