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Record W7133019245

The Impact of Ammonia Combustion on Soot Emissions Formation in Compression Ignition Engines

2023· dissertation· W7133019245 on OpenAlexfundno aff
Mohammed H. Zaher

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSootCombustionIgnition systemDiesel fuelCarbon fibersDiesel engineExhaust gas recirculationDiesel exhaust
DOInot available

Abstract

fetched live from OpenAlex

The impact of ammonia (NH3) co-firing with diesel on the greenhouse gases (GHG) and soot emissions of compression ignition (CI) engines is a current concern in large-scale agriculture, marine transportation, and shipping applications for which NH3 is proposed as a near-term decarbonization solution. In this thesis, the addition of NH3 to hydrocarbons combustion is studied in laminar diffusion co-flow C2H4 flames and through port injection in a dual fuel diesel engine. The effect of NH3 combustion on soot formation is investigated numerically and experimentally using measurements of: (1) the soot volume fraction and mass yield, (2) the average primary particles diameter and number concentration, (3) the graphitization of the soot nanostructure, and (4) the soot surface nitrogen content. Engine GHG, NOx, and unburnt NH3 emissions are also measured. The results show a suppressive chemical effect for NH3 cofiring with hydrocarbons on soot inception and growth in flame and engine combustion. The chemical bonding of nitrogen to the soot surface is found to increase soot graphitization leading to less carbon bonding. In addition, the nitrogen content of the soot surface is found to increase indicating an increased potential for nitrogenated PAHs formation with NH3 addition.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.027
GPT teacher head0.358
Teacher spread0.330 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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