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Record W2127739583 · doi:10.1243/09544070jauto194

An ultrasonic sound speed sensor for measuring exhaust gas recirculation levels

2007· article· en· W2127739583 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor Technologies Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAcousticsUltrasonic sensorMeasure (data warehouse)Speed of soundExhaust gas recirculationTransducerCombustionExhaust manifoldTransient (computer programming)Exhaust gasAcoustic sensorAcoustic theoryMaterials scienceInternal combustion engineAutomotive engineeringComputer sciencePhysicsEngineeringChemistry

Abstract

fetched live from OpenAlex

Exhaust gas recirculation (EGR) has been used for years to improve the performance of internal combustion engines. This paper shows that acoustic methods can be used to measure EGR. Theory is presented which shows that measurements of the speed of sound can be used to measure the amount of EGR in the intake manifold. In particular, a new method called the discrete acoustic wave and phase detection (DAWPD) method can be used to measure EGR levels with a fast-response time. Experimental results show that a DAWPD sensor can be used to measure EGR levels with adequate accuracy (± 1.3 per cent EGR) at steady state. Transient measurements were not possible owing to engine limitations. The sensor's performance was limited by the ultrasonic transducers used. It is postulated that sensor performance could be improved with smaller and temperature-independent non-resonant transducers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.271
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it