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Record W2002309604 · doi:10.1115/gt2007-28350

Experimental Investigation and Evaluation of a Low NOx Natural Gas-Fired Mesh Duct Burner

2007· article· en· W2002309604 on OpenAlexaff
Omar Ramadan, Jérôme Gauthier, Patrick M. Hughes, Robert Brandon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsNatural Resources CanadaCarleton University
Fundersnot available
KeywordsCombustorNOxCombustionDuct (anatomy)Gas burnerNatural gasCombustion chamberMaterials scienceWaste managementEnvironmental scienceNuclear engineeringChemistryEngineering

Abstract

fetched live from OpenAlex

To increase the thermal output of a micro-turbine powered CHP system, a low NOx natural gas-fired, mesh duct burner was designed built and tested. The gas-fired burner was installed in the exhaust duct of a recuperated micro-turbine of a micro-cogeneration unit. The full-scale experimental burner was integrated with an Ingersol-Rand 70 kW micro-turbine system for the evaluation. Three wire-mesh burners with different pressure drops were used. Each burner has a conical shape made from FeCrAL alloy mat (NIT100S by ACOTECH) and their design based on a heat release per unit area of 2500 kW/m2 and a total heat release of 240 kW at 100% excess air. The local momentum of the gaseous mixture introduced through the wire-mesh was adjusted so that the flame stabilized outside the burner mesh (surface combustion). Performance of the duct burner was tested and the effect of excess air and firing rate on the stable burning zones, and emissions (NOx, CO) were measured. The range of thermal inputs at which surface combustion was maintained for the duct burner was defined by direct observation of the burner surface and monitoring of the temperature in the combustion zone. Stable combustion with low emission of pollutants was achieved at atmospheric pressure for a firing rate range of 175 to 310 kW. The total system (micro-turbine and duct burner) was shown to produce less than 5 ppm NOx for the conditions tested.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.829
Threshold uncertainty score0.243

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 head0.014
GPT teacher head0.253
Teacher spread0.238 · 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 designBench or experimental
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

Citations3
Published2007
Admission routes1
Has abstractyes

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