Calibration of Triple-Wire Probes Using an Artificial Neural Network
Bibliographic record
Abstract
Hot-wire anemometry is an established technique for velocity measurements in turbulent flows. Calibration of hot-wire probes is challenging due to the nonlinear relationship between the probe output voltage and the velocity, and the sensitivity to the temperature difference between the heated wire and the ambient flow. A triple-wire probe contains three mutually orthogonal wires that permit the three components of the local instantaneous velocity vector to be measured simultaneously. Calibration data reduction methods for multi-wire probes, based on variable-angle calibration techniques, may include curve-fits and direct-interpolation schemes. In the present study, a novel calibration data reduction method for a triple-wire probe is reported which uses an artificial neural network. Such a method has been successfully applied by other researchers for the calibration of seven-hole pressure probes. For the triple-wire probe, the neural network is used to produce a calibration relation between the three probe output voltages and the three components of the local velocity vector. Variable-angle calibration data were obtained for a triple-wire probe for velocity magnitudes from 5 to 40 m/s, yaw angles from −35° to +35°, and roll angles from 0° to 345°. A three-layer perceptron feed-forward network, using a Levenberg-Marquardt training algorithm, was applied to the calibration data, to map the mean voltages to the mean velocity components. The network was tested using an independent data set. The present results yielded standard errors of approximately ±0.38 m/s, ±0.25 m/s and ±0.26 m/s in the magnitudes of the streamwise, vertical, and cross-flow velocity components, respectively. The results showed that the present neural network model is not significantly sensitive to the size of the calibration data set, suggesting it may be a more convenient calibration data reduction method compared to other methods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".