An Approach for Estimating LDA Spatial Filtering Effects on Velocity Measurements
Bibliographic record
Abstract
averaging over the length of the hot wire resulted in an under estimation of the measured one-dimensional spectra at large wave numbers. A means of correcting the one dimensional spectra was also proposed. In a recent analysis, the effects of the spatial resolution of PIV were studied for decaying grid turbulence 2 using similar approach to that in reference 1. The results show that spatially filtering the velocity field results in the underestimation of the mean turbulent kinetic energy and the energy dissipation rate. It is important to note that spectral analysis of measured or simulated data has been the main tool used to investigate spatial filtering effects. For LDA, direct application of the approaches used previously is not possible. In addition to the complications introduced by random particle arrival time, the signals produced by the particles passing through the measurements volume, the processing of the signal, and the size of the probe volume are all integral to the velocity measured by the system. As an example of the complications added by particles used in LDA, calculating the spectra of LDA velocities dependent on particle arrival times is not trivial. Various methods have been studied for determining the spectra of non-equidistant sampled data obtained using LDA. Reference 3 studied effects of velocity bias (transit time) and uncorrelated noise on estimation of spectra. Simulated data with a known spectral density function were generated, and particle paths were calculated using assumptions about the shape of measuring probe volume and transit time for each particle. An estimate of the spectrum was then studied using the simulated data based on modifications of the slotting technique. 4 In another study, 5 a model based spectra was introduced. The auto-correlation function was then computed using inverse fast Fourier transform (FFT) of the spectra. Simulated data was then generated using autocorrelation to obtain random arrival time data. The spectra of this simulated data set was compared with spectra of LDA and hot wire data from a free jet. Although these studies provided insight into the calculation of spectra from LDA data, they have not considered the other effects of the LDA
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".