Evaluation of a Spark Discharge Particulate Matter Sensor in a Turbocharged Diesel Engine
Bibliographic record
Abstract
This paper describes an experimental study of a Particulate Matter (PM) sensor that is intended for on-board control and diagnostic applications in diesel engines. The sensor measures the exhaust PM concentration based upon changes in the voltage waveform of a repetitive, low energy spark discharge. The sensor is electrically heated to prevent carbon fouling from diesel soot and to control its operating temperature. Earlier versions of the sensor were installed directly in the engine exhaust pipe like an Exhaust Gas Oxygen sensor. It was determined that the output of the PM sensor was sensitive to temperature as well as PM concentration, and variations in exhaust temperature made it difficult to maintain the sensor at a constant temperature. In the present study, the sensor was mounted in an electrically heated chamber and a portion of the engine exhaust was bypassed through the chamber. This made it possible to improve the stability of the sensor temperature, thereby reducing the sensitivity of the PM indication to changes in exhaust temperature as the engine load was varied. The PM sensor has been evaluated using a Caterpillar Model 3126 turbocharged 6-cylinder medium duty diesel engine. Small changes in load were used to create minor variations in exhaust PM levels. The PM levels were measured using an AVL 415S smoke meter. Experimental results are presented showing the correlation between the PM sensor signal and the reference PM measurements and the impact of speed and load variations on the correlation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".