Robust Strain/Rotation-Rate Tensor Reconstruction Based on Least Squares RBF-QR for 3D Lagrangian Particle Tracking
Bibliographic record
Abstract
Abstract Based on the least squares and Radial Basis Function (RBF), we propose to use a robust method to reconstruct the velocity gradient and strain/rotation-rate tensor from Lagrangian Particle Tracking (LPT) data. A stable RBF method, RBF-QR, is employed to provide robust approximation in the flat limit of shape functions without suffering ill-conditioning. Least squares method enables the reconstruction of noisy data and further improves the robustness of the calculation on realistic experimental data. The use of Partition-of-Unity Method (PUM) localizes the calculation and allows handling large data set in 2D and 3D and improves computational efficiency. The accuracy and robustness of the method is validated on both 2D and 3D simulated LPT data with artificial noise based on Direct Numerical Simulation (DNS). The technique is further tested on the 3D LPT data obtained from a synthetic jet experiment.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".