Generalized framework for PIV-based pressure gradient error field determination and correction
Bibliographic record
Abstract
Abstract A framework leveraging the governing equations of incompressible flow for the reconstruction and correction of pressure gradient estimation errors from experimental data is extended to incorporate non-zero errors on domain boundaries and Lagrangian pseudo-tracking methods for material acceleration estimation. A second-order system derived from the first-order divergence-curl system governing the pressure gradient error field facilitates handling a variety of error boundary conditions, and the solution can be split into independent Poisson equations, making it straightforward to implement. For known boundary errors, a precise determination of the pressure gradient field is possible, up to the limitations of the numerical method. In practice, the error equations cannot be solved exactly; however, a number of terms may be computed exactly and approximations may be applied on the remaining terms. For the selected test case of a cylinder wake flow in turbulent shedding regime, the analysis of simulated three-dimensional, three-component velocity measurements demonstrates that the errors in pressure estimates can be reduced by up to 50% using a basic finite difference implementation.
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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 | 0.000 | 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 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".