Comparison of momentum and impulse formulations for PIV-based force estimation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it