Convergence of Dynamic Mode Decomposition As an Assessment Criteria for Completion of Internal, Unsteady Flow Simulations
Bibliographic record
Abstract
Abstract The length of simulation time for unsteady computational fluid dynamics (CFD) is often based on intuition or best practices, as there is no established convergence criteria to guarantee the computations have generated enough flow to resolve all spatio-temporal content. Thus, these simulations are often run longer than necessary, but this increases the run time and computational resources required. This is often the case for unsteady CFD for turbomachines. This paper introduces an algorithm, based on dynamic mode decomposition (DMD), that can determine when continuing an unsteady CFD computation no longer yields additional spatio-temporal information. The algorithm is shown to correctly determine the modal content for an analytical dataset with a start-up transient with 44% less data than by only using data from after the start-up transient. The algorithm is then applied to 2D CFD of a fully turbulent flow over a cylinder with periodic boundaries, where the amount of flow time required is just sufficient for the size of fluctuations in cylinder lift coefficient to stop growing. The algorithm is also applied to a 2D periodic flow over a rotor airfoil, with an empty stationary duct downstream. The algorithm determines the point at which the statio-temporal content of the flow stops changing meaningfully greatly in advance of using conventional methods.
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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".