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Record W3119310154 · doi:10.1115/gt2020-15697

Analysis of Autoignition Chemistry in Aeroderivative Premixers at Engine Conditions

2020· article· en· W3119310154 on OpenAlexaff
Sandeep Jella, Gilles Bourque, Pierre Gauthier, Philippe Versailles, Jeffrey M. Bergthorson, Ji-Woong Park, Tianfeng Lu, Snehasish Panigrahy, Henry J. Curran

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsMcGill UniversitySiemens (Canada)
Fundersnot available
KeywordsAutoignition temperatureIgnition systemChemistryResidence time (fluid dynamics)ThermodynamicsNuclear engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract The minimization of autoignition risk is critical to the design of premixers of high power aeroderivative gas turbines as an increased use of highly reactive future fuels (for example, hydrogen or higher hydrocarbons) is anticipated. Safety factors based on ignition delays of homogeneous mixtures, are generally used to guide the choice of a residence time for a given premixer. However, autoignition chemistry at aeroderivative conditions is fast (0.5–2 milliseconds) and can be initiated within typical premixer residence times. The analysis of what takes place in this short period necessarily involves the study of low-temperature autoignition precursor chemistry, but precursors can change with fuel and local reactivity. Chemical Explosive Modes are a natural alternative to study this as they can provide a measure of autoignition risk by considering the whole thermochemical state in the framework of an eigenvalue problem. When transport effects are included by coupling the evolution of the Chemical Explosive Modes to turbulence, it is possible to obtain a measure of spatial autoignition risk where both chemical (e.g. ignition delay) and aerodynamic (e.g. local residence time) influences are unified. In this article, we describe a method that couples Large Eddy Simulation to newly developed, reduced autoignition chemical kinetics to study autoignition precursors in an example pre-mixer representative of real life geometric complexity. A blend of pure methane and dimethyl ether (DME), a common fuel used for experimental autoignition studies, was transported using the reduced mechanism (38 species / 238 reactions) at engine conditions at increasing levels of DME concentration until exothermic autoignition kernels were formed. The resolution of species profiles was ensured by using a thickened flame model where dynamic thickening was carried out with a flame sensor modified to work with multi-stage heat release. The paper is outlined as follows: First, a reduced mechanism is constructed and validated for modeling methane as well as di-methyl ether (DME) autoignition. Second, sensitivity analysis is used to show the need for Chemical Explosive Modes. Third, the thickened flame model modifications are described and then applied to an example premixer at 25 bar / 890K preheat. The Chemical Explosive Mode analysis closely follows the large thermochemical changes in the premixer as a function of DME concentration and identifies where the premixer is sensitive and flame anchoring is likely to occur.

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 categoriesInsufficient payload (model declined to judge)
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 score0.067
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.198
Teacher spread0.190 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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