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Record W7132929730

Application of Tabulated Chemistry to Laminar Co-flow Diffusion Flames at Atmospheric and Elevated Pressures

2022· dissertation· W7132929730 on OpenAlexfundno aff
Eligh Nicholas Corchis-Scott

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsnot available
FundersUniversity of TorontoCompute Canada
KeywordsLaminar flowCombustionDiffusion flameMethaneDiffusionFlame structurePremixed flameJet (fluid)Combustor
DOInot available

Abstract

fetched live from OpenAlex

Numerical simulations of combustion can be very computationally expensive, particularly when combustion of the complex fuels frequently found in aircraft gas turbine engines is considered. The large chemical mechanisms required to simulate flames involving these fuels become computationally prohibitive when realistic combustion conditions are used. In order to render these simulations tractable, this thesis investigates chemistry tabulation techniques, which have the potential to significantly reduce the computational costs of simulating reacting flows. In this thesis, four different tabulation techniques will be assessed by applying the methods to the prediction of steady, laminar, co-flowflames. The tabulation techniques considered are the flame prolongation of intrinsic low dimensional manifold (FPI) method, the steady laminar flamelet method (SLFM), the flamelet/progress variable (FPV) method, and the radiative flame prolongation of intrinsic low dimensional manifold (RFPI) method. These techniques are all applied to ethylene flames at atmospheric pressure, methane flames at 5 atm and 10 atm, and Jet A surrogate flames at atmospheric pressure. The results are compared to those obtained for the same case obtained using detailed chemistry, both with and without low-Mach preconditioning. This comparison facilitates the discussion of the relative merits of the tabulation techniques in relation to each other and to detailed chemistry.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.718
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.004
GPT teacher head0.256
Teacher spread0.252 · 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
Published2022
Admission routes1
Has abstractyes

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