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Record W4404028720 · doi:10.4271/2024-01-4312

Emissions Analysis for a Hydrogen-Fueled Low-Pressure-Ratio Split-Cycle Engine

2024· article· en· W4404028720 on OpenAlexaff
Mohamed Hossam Eldakamawy, Mathieu Picard

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2024
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsEnvironmental scienceHydrogenComputer scienceAutomotive engineeringChemistryEngineering

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">Recuperated low-pressure-ratio split-cycle engines represent a promising engine configuration for applications like transportation and stand-alone power generation by offering a potential efficiency as high as 60%. However, it can be challenging to achieve the stringent NOx emission standard, such as Euro 6 limit of 0.4 gNOx/kWh, due to the exhaust cylinder high intake temperature. This paper presents experimental investigation of hydrogen-air combustion NOx emissions for such engines for the first time. Experiments are carried out using a simplified constant-volume combustion chamber with glow-plug ignition. Two fuel injection techniques are performed: direct injection and injection via a novel convergent-divergent injector. For the direct injection scenario, NOx levels are unsatisfactory with respect to the Euro 6 standards over a range of operating temperatures from 200 °C to 550 °C. Recorded NOx levels can reach twice the permissible limit which necessitates the implementation of after-treatment techniques. Interestingly, NOx emissions are found to be less by around 50% in the case of the novel injector. The enhanced fuel-air mixing mechanism using the convergent-divergent injector increases the ignition delay and shifts the combustion flame towards the premixed conditions and results in lower NOx levels.</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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.866
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.257
Teacher spread0.247 · 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
Published2024
Admission routes1
Has abstractyes

Explore more

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