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Record W2807663262 · doi:10.11159/ffhmt18.128

Numerical Analysis of the Combustion Characteristics for the Power Improvement and Additional SOFA System in a Pulverized-Coal Boiler

2018· article· en· W2807663262 on OpenAlexvenueno aff
Yu Jiang, Seok-Gi Ahn, Dong-Hun Oh, Chung‐Hwan Jeon

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2018
Typearticle
Languageen
FieldEngineering
TopicRadiative Heat Transfer Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPulverized coal-fired boilerBoiler (water heating)CombustionCoalEnvironmental scienceComputer scienceNuclear engineeringProcess engineeringWaste managementEngineeringChemistry

Abstract

fetched live from OpenAlex

Tangentially fired pulverized coal boilers are one of the most widely used boilers in power plants because of their good flame distribution and uniform wall heat flux to the furnace walls and have been used to generate power in Korea The pulverized-coal boiler is simulated within CFD models implemented in the ANSYS FLUENT V17.1 software and the fluid flow and coal particle combustion process are modeled using the Euler-Lagrange approach. The governing equations for the conservations of energy, mass, momentum, and species are solved However, as domestic NOx emission standards become more stringent, additional NOx reduction technologies are needed. Here, the traditional air staging technology has been unable to meet the reduction of pollutants, so we append the SOFA (Separated Over-Fire Air) system, this can be a good way to reduce the NOx generation So we introduce the concept of that SOFA (Separated Over-Fire Air) system with a pulverized-coal boiler. The CFD analysis represents a useful technology to provide the flow and temperature fields. And we expect this emission control technologies to reduce environmental pollution and the impact on human health.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.372

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 head0.015
GPT teacher head0.223
Teacher spread0.208 · 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
Published2018
Admission routes1
Has abstractyes

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