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Record W3195882090 · doi:10.1002/cjce.24294

Experimental and numerical modal analysis of wall tubes in the coal‐fired boiler or radiant syngas cooler

2021· article· en· W3195882090 on OpenAlexvenueno aff
Lintao Shao, Wei Guo, Weijuan Yang, Zhiwen Xia, Zhijun Zhou

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsBoiler (water heating)Natural frequencyFinite element methodCoalMaterials scienceVibrationStructural engineeringModal analysisEngineeringWaste managementAcoustics

Abstract

fetched live from OpenAlex

Abstract The slag deposited on the wall tubes in the coal‐fired boiler or radiant syngas cooler (RSC) of the entrained flow gasifier could reduce the heat transfer efficiency and degrade the tubes by corrosion. During the operation of the boiler or RSC, the rapping‐off‐ash method is an effective method to remove slag deposits, especially in a high‐pressure environment. The operating parameters of the rapping‐off‐ash method are related to the modal parameters of wall tubes, including the natural frequency, damping ratio, and mode shapes. In this study, modal parameters of wall tubes composed of three single tubes and fins were experimentally determined through the modal test analysis under both empty and water‐filled conditions. The dynamic characteristics of wall tubes were extracted by using the roving hammer‐impact test and the frequency response function (FRF). A numerical simulation based on the finite element method in ABAQUS was conducted to compare with the experimental modal analysis. The first five natural modes ranged from 14 to 154 Hz, and the maximum vibration or torsion locations of wall tubes under the corresponding mode frequencies were investigated. The water inside the wall tubes could reduce the stiffness of the structure and decrease the values of natural frequency.

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 score0.033
Threshold uncertainty score0.297

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)0.0000.000

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 head0.007
GPT teacher head0.196
Teacher spread0.188 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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