Novel analysis for large strains based on particle image velocimetry
Bibliographic record
Abstract
Over the last few decades, the particle image velocimetry (PIV) technique has become an interesting tool used to measure displacements in the field of experimental mechanics. This paper presents a procedure to interpret PIV displacements, measured following an Eulerian scheme, with the purpose of providing accumulated displacements, velocities, accelerations, and strains on points representing physical particles. Strains are computed as the gradient of displacements. When compared with other standard procedures already published, the presented methodology is especially well suited to interpret large strains. The basis of the procedure is to map displacement increments measured through PIV analysis on the subset (or patch) centres into numerical particles that are defined as portions of the moving masses whose deformation is analyzed. The implementation of the method is explained in detail, highlighting its simplicity. The procedure can be used as a post-processor of currently available PIV software packages. The methodology is first applied to synthetic cases of rectangular samples in which known displacements are imposed and also to a sandy slope failure experiment involving large displacements. The method reproduces satisfactorily the recorded images.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".