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Record W4296536347 · doi:10.18280/mmep.090402

Simulation of Plasma Assisted Supersonic Combustion over a Flat Wall

2022· article· en· W4296536347 on OpenAlexvenueno aff
Albio D. Gutierrez, Luis F. Alvarez

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2022
Typearticle
Languageen
FieldEngineering
TopicPlasma and Flow Control in Aerodynamics
Canadian institutionsnot available
FundersUniversidad del Valle
KeywordsSupersonic speedCombustionNanosecondPlasmaCombustorIgnition systemMechanicsKinetic energyMaterials scienceComputational fluid dynamicsChemistryThermodynamicsPhysicsOpticsLaserClassical mechanics

Abstract

fetched live from OpenAlex

This work presents a simplified methodology to couple the physics of a nanosecond pulsed discharge to the process of supersonic combustion in a flat wall combustor configuration. Plasma and supersonic combustion are separately simulated and then coupled by seeding plasma-generated radicals on the combustion domain. The plasma model is built assuming spatial uniformity and considering only the kinetic effects of the nanosecond pulsed discharge. Therefore, a zero-dimensional kinetic scheme accounting for the generation of plasma species is utilized. For the combustion model, the complete set of Favre-averaged compressible Navier Stokes equations along with finite rate chemistry is solved through a control-volume based technique via the commercial software Ansys Fluent. The computational results are compared against experimental studies showing that the proposed methodology can capture the main kinetic effects of the nanosecond pulsed discharge on supersonic combustion. OH concentration contours reveal the presence of an enhanced flame when the plasma is applied following the trends from experimental OH PLIF images. In addition, time evolving temperature and OH concentration contours show that the ignition delay time is reduced with the application of the discharge.

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 score0.488
Threshold uncertainty score0.649

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 head0.018
GPT teacher head0.195
Teacher spread0.177 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueMathematical Modelling and Engineering ProblemsSame topicPlasma and Flow Control in AerodynamicsFrench-language works237,207