An Experimental Study of Spark Anemometry for In-Cylinder Velocity Measurements
Bibliographic record
Abstract
It has been demonstrated by previous researchers that an approximate value of the bulk flow velocity through the spark plug gap of a running spark ignition engine may be deduced from the voltage and current waveforms of the spark. The technique has become known as spark anemometry and offers a robust means of velocity sensing for engine combustion chambers and other high temperature environments. This paper describes an experimental study aimed at improving performance of spark anemometry as an engine research tool. Bench tests were conducted using flow provided by a calibrated nozzle apparatus discharging to atmospheric pressure. Whereas earlier studies had relied upon assumptions about the shape of the stretching spark channel to relate the spark voltage to the flow velocity, the actual spark channel shape was documented using high speed video in the present study. A programmable ignition system was used to generate well-controlled constant current discharges. The spark anemometry apparatus was then tested in a light duty automotive engine. Results from the image analysis of the spark channel shape undertaken in the present study have shown that the spark kernel moves at a velocity of less than that of the free stream gas velocity. A lower velocity threshold exists below which there is no response from the spark. It is possible to obtain a consistent, nearly linear relationship between the first derivative of the sustaining voltage of a constant current spark and the free stream velocity if the velocity falls within certain limits. The engine tests revealed a great deal of cycle-to-cycle variation in the in-cylinder velocity measurements. Instances where the spark restrikes occur during the cycle must also be recognized in order to avoid false velocity indications.
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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 | 0.000 |
| 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) | 0.001 | 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".