Improving an Algorithm for Assessing the Completion of Internal, Unsteady Flow Simulations using Spatial Downsampling
Bibliographic record
Abstract
In practical computational fluid dynamics (CFD), a reduction in flow time required to be solved is extremely beneficial in reducing the amount of time and computational resources required for the computation. The authors have previously introduced a convergence algorithm based on dynamic mode decomposition (DMD) that can determine the completion of an unsteady CFD flow, reducing the overall amount of flow time required to be solved. However, this algorithm itself can be expensive, and was only tested on cases with less than half a million cells. The original algorithm become too expensive on larger datasets. This paper introduces an updated algorithm along with a method for spatially downsampling the CFD data to make the execution of the convergence algorithm feasible on practical CFD, which could have up to hundreds of millions of cells. The updated algorithm and spatial downsampling method is applied to two CFD cases - a turbulent flow over a cylinder and a rotor in duct case - where the full solution is known. It is able to correctly determine the amount of flow time required for the spatio-temporal content stops changing meaningfully, requiring much fewer computational resources than the original version of the algorithm.
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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".