Implementation of an efficient Selective Frequency Damping method in a RANS solver
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-0359.vid Steady-state Reynolds-Averaged Navier-Stokes (RANS) flow solvers can encounter convergence problems when trying to solve flow conditions which involves unsteady phenomena, such as buffet or vortex-shedding. However, a fixed point steady-state solution can exist in these conditions, which is useful in many engineering applications such as design or stability analysis. The selective frequency damping method aims to stabilize such unstable flows through the addition of source terms to the RANS equations, proportional to the difference between the flow and a low-pass time-filtered version of the same flow. The method adds two parameters, chi the influence factor of the source term, and delta the cutoff wavelength of the low-pass filter. Both these parameters need to be selected with care to allow the convergence of the solver. This work aims to extend the use of the SFD algorithm to turbulent flows of industrial relevance, using RANS modeling with a pseudo-time stepping scheme. A novel modification to the SFD method is proposed to improve the convergence rate of the solver. The modification to the algorithm consists in the addition of a periodic reset of the low-pass time-filtered flow to the value of the base solver flow. This adds an additional parameter to the method, which is defined as r, the number of iterations between each reset. The goal is to remove the influence of previous poorly converged solver iterations. The novel modification is tested for the test cases of vortex shedding over a cylinder and transonic buffet over a supercritical airfoil. The results show an improved convergence rate with a successful stabilization of the flow solution. They also highlight the importance of choosing a suitable reset period and the fact that the periodic reset modifies the optimal values of the chi and delta parameters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.017 | 0.006 |
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".