A comparison of single-time and spectral linear stochastic field pressureestimation in the flow over a backward-facing step
Bibliographic record
Abstract
The present work aims to study and compare two Linear Stochastic Estimation (LSE) methods, namely conventional single-time LSE and a much less explored technique, Spectral Linear Stochastic Estimation (SLSE).To assess the accuracy of each technique the estimated unsteady pressure fields are compared directly to the actual instantaneous pressure field using the results of a long runtime Large-Eddy Simulation of the flow over a backwardfacing step.Wall pressure fluctuations were obtained through virtual multi-point measurements placed on the wall and were used to estimate the time-resolved field pressure.Conventional single-time LSE significantly underpredicted the field pressure fluctuations.The rms errors in the instantaneous estimate were lowest in the vicinity of the reference probes due to the singlepoint technique not incorporating correlations at non-zero time-lags associated with mean convection.The estimated pressure field was improved significantly using spectral estimation achieving a 400 to 500% improvement in rms error between the estimated and actual pressure in the shear-layer region.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".