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Record W2750424956 · doi:10.1115/1.4040007

Large Eddy Simulation of a Pressurized, Partially Premixed Swirling Flame With Finite-Rate Chemistry

2018· article· en· W2750424956 on OpenAlexaff
Sandeep Jella, Pierre Gauthier, Gilles Bourque, Jeffrey M. Bergthorson, Ghenadie Bulat, Jim Rogerson, Suresh Sadasivuni

Bibliographic record

VenueJournal of Engineering for Gas Turbines and Power · 2018
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsMcGill UniversitySiemens (Canada)
FundersDeutsches Zentrum für Luft- und Raumfahrt
KeywordsCombustorTurbulenceContext (archaeology)MechanicsReynolds-averaged Navier–Stokes equationsLarge eddy simulationCombustionRushton turbineChemistryParticle image velocimetryTurbineThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Finite-rate chemical effects at gas turbine conditions lead to incomplete combustion and well-known emissions issues. Although a thin flame front is preserved on an average, the instantaneous flame location can vary in thickness and location due to heat losses or imperfect mixing. Postflame phenomena (slow CO oxidation or thermal NO production) can be expected to be significantly influenced by turbulent eddy structures. Since typical gas turbine combustor calculations require insight into flame stabilization as well as pollutant formation, combustion models are required to be sensitive to the instantaneous and local flow conditions. Unfortunately, few models that adequately describe turbulence–chemistry interactions are tractable in the industrial context. A widely used model capable of employing finite-rate chemistry is the eddy dissipation concept (EDC) model of Magnussen. Its application in large eddy simulations (LES) is problematic mainly due to a strong sensitivity to the model constants, which were based on an isotropic cascade analysis in the Reynolds-averaged Navier–Stokes (RANS) context. The objectives of this paper are: (i) to formulate the EDC cascade idea in the context of LES; and (ii) to validate the model using experimental data consisting of velocity (particle image velocimetry (PIV) measurements) and major species (1D Raman measurements), at four axial locations in the near-burner region of a Siemens SGT-100 industrial gas turbine combustor.

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 score0.285
Threshold uncertainty score0.433

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)0.0000.000

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 head0.005
GPT teacher head0.205
Teacher spread0.200 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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