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Record W2985131733 · doi:10.1115/gt2019-91742

Evaluation of Spray Performance of Pyrolysis Oil

2019· article· en· W2985131733 on OpenAlexaff
Sean Yun, Minji Choi, Ashwani Kumar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsPyrolysisPyrolysis oilCombustionFossil fuelNozzleProcess engineeringEnvironmental scienceWaste managementWork (physics)Materials sciencePetroleum engineeringMechanical engineeringEngineeringChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Pyrolysis oil has become an important subject of research as it is considered to be a potential environmentally friendly and cheap alternative to conventional fossil fuels. Unfortunately, due to the significant differences of the chemical and physical properties of pyrolysis oil than that of fossil fuels, the deployment of pyrolysis oil in existing power systems such as gas turbines and internal combustion engines has been highly restricted. Thus, major research on pyrolysis oil has been conducted to overcome these challenges related to the unfavorable physical and chemical properties of pyrolysis oil. This paper reports experimental work on the effects of physical properties of the pyrolysis oil on spray performance of nozzles. Effort to evaluate the spray performance by using different types of atomizers has been made as well. Laser based diagnostics was applied to obtain qualitative comparisons spray characteristics of various pyrolysis oils. Experimental data such as the distribution of fuel droplet sizes and overall spray shapes was analyzed, which could provide valuable guidelines to design fuel nozzles. Lastly, the paper will also present NRC’s plans to accelerate the deployment of such pyrolysis oils in industrial gas turbines.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.837

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)0.0010.000

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 head0.211
Teacher spread0.202 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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