Error propagation dynamics of velocimetry-based pressure field calculations (2): on the error profile
Bibliographic record
Abstract
This work investigates the propagation of error in a Velocimetry-based Pressure field reconstruction (VPressure) problem to determine and explain the effects of error profile of the data on the error propagation. The results discussed are an extension to those found in Pan et al. (2016). We first show how to determine the upper bound of the error in the pressure field, and that this worst scenario for error in the data field is unique and depends on the characteristics of the domain. We then show that the error propagation for a V-Pressure problem is analogous to elastic deformation in, for example, a Euler-Bernoulli beam or Kirchhoff-Love plate for one- and two-dimensional problems, respectively. Finally, we discuss the difference in error propagation near Dirichlet and Neumann boundary conditions, and explain the behavior using Green’s function and the solid mechanics analogy. The methods discussed in this paper will benefit the community in two ways: i) to give experimentalists intuitive and quantitative insights to design tests that minimize error propagation for a V-pressure problem, and ii) to create tests with significant error propagation for the benchmarking of V-Pressure solvers or algorithms. This paper is intended as a summary of recent research conducted by the authors, whereas the full work has been recently published (Faiella et al., 2021).
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".