A strategy to eliminate all nonlinear effects in constant-voltage hot-wire anemometry
Bibliographic record
Abstract
A constant-voltage anemometer is subject to nonlinear effects when the operating hot wire is exposed to large velocity fluctuations in the incident flow. This results in the generation of undesirable higher harmonics, just as in the two classic systems, constant-current and constant-temperature anemometers, for which no attempts are normally made to correct the nonlinearities. The present investigation shows that these undesirable higher harmonics can be suppressed in the case of a constant-voltage anemometer. A new approach to process experimental data is proposed. It is based on three explicit equations established and solved with all terms included, i.e., without linearization. These are (1) the first-order differential equation that describes the electronic circuit of a constant-voltage anemometer-this equation permits to deduce the instantaneous resistance of the hot wire from the output voltage of the anemometer; (2) the first-order differential equation that expresses the thermal lag behavior of the hot wire when used in a constant-voltage mode-this equation permits to restore the instantaneous resistance that an ideal wire would have without thermal inertia in the same flow conditions; and (3) the algebraic relation that expresses the heat-transfer law of an ideal wire, according to King's law, a look-up table, or a polynomial fit-this relation permits to deduce the instantaneous flow velocity from the instantaneous resistance of the ideal wire. The proposed method is easily implemented on a personal computer and permits odd turbulence moments, such as skewness factors, to be obtained satisfactorily.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".