Assessing The Potential Of PIV Data To Resolve Hidden Frequency Scales
Bibliographic record
Abstract
The present work investigates the performance of an advection-based flow reconstruction model to increase the temporal resolution of PIV measurements recorded in a turbulent, planar jet. Using a semi-Lagrangian technique in combination with Rapid Distortion Theory, a modified trajectory tracking procedure is implemented. The method introduces a specification for a two-dimensional convective velocity based on the least squares minimization of the linearized advection equation, in contrast to the previously introduced one-dimensional mean velocity profile outlined in Vocke et al. (2023). The new implementation is based on local flow measurements, making it well-suited towards streamwise heterogeneity and spatially developing flows. With this, the flow at some unknown time can be estimated from the know flow measurements at the forward and backward time. Spectral analysis illustrates the model's proficiency in recovering spatiotemporal information far exceeding the Nyquist frequency, with spectral reconstruction errors of less than 5% for the most extreme case. It is demonstrated that the spectral content can be estimated at least two orders of magnitude beyond the sampling frequency of the original recording. Improved performance compared to alternative methods is demonstrated with only minor impact on computational time. This indicates the approach may be used as a tool for experimental researchers a) having no access to a high-speed PIV or b) with high-speed PIV to increase the spectral resolution even further.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".