Comparison of momentum and impulse formulations for PIV-based force estimation
Bibliographic record
Abstract
Abstract The estimation of fluid-induced loads using particle image velocimetry (PIV) data is investigated using momentum- and impulse-based control volume methods, which require additional calculations of pressure and vorticity surrounding the immersed body, respectively. A new, comprehensive comparison of the two methods is presented based on two-dimensional velocity data. The effects of random error, finite spatio-temporal resolution, and spatial filtering of the velocity fields are considered using numerical (CFD) data of flow around a stationary circular cylinder in a steady freestream at a Reynolds number of . In general, the momentum method is found to be more robust, exhibiting lower random-error sensitivity and lower errors due to discretization, except at coarse spatial resolutions, for which a significant underestimation of drag arises using the momentum method. The impulse method is best suited to cases where vorticity does not leave the control volume, or in cases where a deforming control volume can be defined to minimize the presence vorticity on the outer control surface. For example, the impulse method performed as well as the momentum method when applied to particle image velocimetry (PIV) data obtained around a cylinder accelerating from rest in quiescent fluid (with a peak Reynolds number of 5100). For the broad class of flows involving a steady freestream and an established wake, the momentum method can be applied with greater confidence than the impulse method.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".