Implementing a Preconditioning Technique in A RANS Compressible Code to Accelerate the Convergence Rate for Low-Speed Flows
Bibliographic record
Abstract
In this paper are presented a preconditioning technique to be implemented in a three-dimensional explicit compressible code to solve a turbulence flow to steady state regime. A local preconditioning technique with accurate predictions of mixed speed regimes is implemented in the original code, however, for low flow Mach numbers in the boundary layer region the numerical accuracy is lost to the preconditioning code. To improve the numerical solution are suggested a new limit to the preconditioning sensor based on a pressure sensor and is established an explicit flux function to evaluate the preconditioning sensor in the cell fluxes. The preconditioning code is validated for a supersonic case in nozzle and then to a subsonic case is studied the convergence rate for a low Mach number flow. Numerical solutions demonstrated that the changes applied in the original code improves the accuracy and robustness of the code for low speed flows.
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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".