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Record W3183698850 · doi:10.4271/2021-01-1224

Investigating Humidity Effects on Small Offroad Engine SI Performance and Emissions

2021· article· en· W3183698850 on OpenAlexaff
Adrian Olmos, Steven Griffin, G Townley Price, Nathan Beilke, Scott Sajdowitz

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2021
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsIntertek (Canada)
Fundersnot available
KeywordsHumidityEnvironmental scienceComputer scienceAutomotive engineeringMeteorologyEngineeringPhysics

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">Collaborative research outlined in this paper documents recent engine and emission performance of a newer, more robust small SI engine across a sweeping range of relative humidity (RH) having fixed intake air temperature and pressure. The experimental results will show that power correction references to SAE J1349 as well as humidity correction (Kh) reference in EPA 40 CFR §1065 may generically be applied, but do not accurately compensate for the extent of correction required. The test results shared from this particular performance testing of a Kohler KT745 carbureted engine develops the case for a more diverse and less conservative approach to a one-size-fits all strategy related to humidity corrections within the small SI testing community. Moreover, humidity effects for both observed and corrected power, as well as emission corrected constituents (not just NOx) are generally greater than would otherwise be assumed from the literature. From these results, facilities without intake air humidity management may unknowingly bias brake specific emissions of both HC+NOx and CO family emission limits (FELs). Moreover, in defining family emission limits with NOx only correction, general performance repeatability in certifications as well as production line testing (PLT) results throughout the year may contribute to a wider deviation in engine-to-engine emission variability and power than might normally be expected. The following results are specifically for rich engine operation of an air-cooled engine, and subsequent results for stoichiometric air-cooled engine relative humidity effects would need to be discussed separately.</div></div>

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
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 score0.899
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.003
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 head0.014
GPT teacher head0.236
Teacher spread0.222 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

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