A parallel multigrid method for predicting compressible flow in turbomachinery
Bibliographic record
Abstract
A parallel multigrid method is developed for solving two-dimensional inviscid compressible flows on body-fitted multi-block mesh. The method utilizes a Godunov-type up-winding scheme, exact and approximate Riemann solvers and limited reconstruction to achieve higher-order spatial accuracy. The computation is carried out on a quadrilateral grid that is divided into blocks to facilitate scalable performance on parallel computers. A Full Approximation Storage multigrid scheme is employed to accelerate the convergence of an explicit multistage time integration scheme. Several example cases, including the flow about NASA rotor 67, are used to validate the parallel algorithm and to investigate its convergence and parallel performance. The effectiveness of the multigrid method is demonstrated and good convergence acceleration is obtained, particularly for problems of increasing complexity. The parallel efficiency of the method is found to be dependant on the problem size. The potential of the algorithm for solving large scale and three-dimensional problems is indicated.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".