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Record W2079458480 · doi:10.1115/icef2002-532

Effect of EGR on Heavy-Duty Diesel Engine Emissions Characterized With Laser-Induced Incandescence

2002· article· en· W2079458480 on OpenAlexaff
W. Stuart Neill, Gregory J. Smallwood, David R. Snelling, Robert A. Sawchuk, Dan Clavel, Daniel S. Gareau, Wallace L. Chippior

Bibliographic record

VenueDesign, Application, Performance and Emissions of Modern Internal Combustion Engine Systems and Components · 2002
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsIncandescenceSootParticulatesDiesel fuelDiesel exhaustEnvironmental scienceNOxDiesel engineVolume (thermodynamics)Exhaust gas recirculationDiesel particulate filterGravimetric analysisSmokeInstrumentation (computer programming)Nuclear engineeringAutomotive engineeringMaterials scienceAnalytical Chemistry (journal)CombustionWaste managementInternal combustion engineEnvironmental chemistryChemistryComputer sciencePhysics

Abstract

fetched live from OpenAlex

The regulations governing diesel engine particulate matter (PM) and oxides of nitrogen (NOx) emissions are becoming increasingly stringent. New instrumentation is urgently needed to make accurate and precise measurements of PM emissions from low-emitting engines and emission control systems in a reasonable amount of time. Laser-induced incandescence (LII) is a technique for making temporally resolved measurements of soot volume fraction. LII offers real-time particulate concentration measurements over several orders of magnitude, and adds desirable information about particulate size and surface area. In this study, the exhaust gas recirculation (EGR) system of a heavy-duty diesel engine was tuned at eight speed/load conditions using quantitative LII. Soot concentrations measured by LII correlated strongly with measurements taken using the standard gravimetric technique and an AVL smoke meter.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.218
Teacher spread0.201 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations3
Published2002
Admission routes1
Has abstractyes

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