On the bias error due to obscured vectors in particle image velocimetry for concentrated solid–liquid flows
Bibliographic record
Abstract
Particle image velocimety (PIV) is a valuable tool for single-phase and multiphase flow velocity measurements. Many previous works have used PIV in solid–liquid (and other multiphase) flow to obtain velocity fields for the fluid phase. Early works noted the potential for bias error in multiphase PIV measurements, noting the need to constrain experiments to low solid concentrations. Refractive index matching (RIM) techniques have enabled acceptable PIV images to be obtained at much higher solid concentrations, and the technique is now used extensively. In the single-phase flow literature, the errors induced by measurements close to solid boundaries are well known, and similar errors occur in multiphase measurements except that they affect all velocity locations rather than just near-wall locations. By extending the near-wall analysis that is well understood for single-phase flow to a multiphase flow scenario, this paper demonstrates that even when RIM is perfect, multiphase PIV still suffers from a considerable statistical bias error when obtaining mean velocity measurements. Considering this error is important for planning and conducting accurate PIV measurements.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".