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Record W1965843345 · doi:10.1115/1.1318205

Effects of NO Models on the Prediction of NO Formation in a Regenerative Furnace

2000· article· en· W1965843345 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Energy Resources Technology · 2000
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsWestern University
Fundersnot available
KeywordsChemistryThermalSensitivity (control systems)Kinetic energyThermodynamicsEngineering

Abstract

fetched live from OpenAlex

A study of the effects of nitric oxide (NO) models on the prediction of NO formation in a gas-fired regenerative furnace with highly preheated air was undertaken. Three chemical kinetic processes for NO formation/depletion, i.e., thermal NO, prompt NO, and NO reburning, are included. In the thermal NO model, the sensitivity encountered when using two different approaches, namely the equilibrium approach and the partial equilibrium approach, for determining the O radical concentration was studied. The effects of the third reaction in the thermal NO mechanism, NO reduction (reburning) mechanism, and different types of probability density functions (PDFs) on the NO predictions have also been tested. The sensitivity of the excess air ratio on the NO generation rate in the furnace has been investigated. Finally, the impact of the temperature on the NO formation rate in the regenerative furnace was discussed. [S0195-0738(00)00304-6]

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.

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 score0.042
Threshold uncertainty score0.239

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 head0.004
GPT teacher head0.166
Teacher spread0.162 · 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