A method to anchor displacement vectors to reduce uncertainty and improve particle image velocimetry results
Bibliographic record
Abstract
When large fields of view are used with particle image velocimetry (PIV) in the study of complex fluid flows, extraneous effects linked to velocity gradients and non-uniformities in both image illumination and particle number density become more prevalent. These factors, coupled with the limiting requirement that large areas of interest (AOIs) must be employed to measure the full range of velocity, cause degradation of correlation results (i.e. broadening and/or splintering of the cross-correlation peaks). Advanced iterative and hierarchical PIV strategies provide improved results but these can break down in complex flows where velocity gradients are significant and particle dispersion does not remain uniformly random. One reason for this breakdown is that local displacement vectors obtained using the cross correlation method are not necessarily representative of the fluid motion where these vectors are typically anchored (namely, the geometric centre of the AOI). To address this issue a simple but effective technique is presented that enables individual displacement vectors to be anchored within an AOI at a location(s) where the actual fluid motion is more consistent with the measured displacement. The method involves a straightforward approach to extract the intensity features from within each AOI that most influence the calculation of the cross-correlation plane. To demonstrate the utility of the methodology, bounds of uncertainty are approximated, and results obtained from the analysis of high gradient synthetic flow fields are compared against results obtained using the conventional PIV approach.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".