Multivariate Knock Detection for Development and Production Applications
Bibliographic record
Abstract
Combustion knock caused by end gas autoignition continues to be a limiting factor in the performance of automotive internal combustion engines. As such, the availability of efficient knock detection methods is a prime requirement for the optimization of engine mapping and control. Current production knock control systems are based on the measurement of mechanical vibration induced by the acoustic resonance excited in the combustion chamber during autoignition. These vibrations are measured using accelerometers on the engine block. Conversely, knock detection in the laboratory environment during engine development or calibration generally involves either acoustic methods or acquisition of in-cylinder pressure. The purpose of this study is to develop an improved multi-transducer vibration-based knock detection method with applications in engine development and production. The possibility of replacing the pressure-based detection methods in the laboratory environment presents many advantages relating to cost and efficiency. Moreover, the economy of a vibration-based system coupled with improved correlation to laboratory methods represents great potential for performance improvements if applied to production applications.
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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 | 0.000 | 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) | 0.000 | 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".