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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 OpenAlexaff
Jason S. Olfert, M. David Checkel

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2007
Admission routes1
Has abstractyes

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Same venueProceedings of the Institution of Mechanical Engineers Part D Journal of Automobile EngineeringSame topicAdvanced Sensor Technologies ResearchFrench-language works237,207