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Record W4308494299 · doi:10.1002/cjce.24753

Simulation analysis of instantaneous impact on the radiant syngas cooler by rapping vibrators

2022· article· en· W4308494299 on OpenAlexvenueno aff
Hongfei Cui, Weijuan Yang, Lintao Shao, Zhijun Zhou, Junhu Zhou

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsnot available
Fundersnot available
KeywordsVibrationDisplacement (psychology)Natural frequencyPosition (finance)EngineeringFinite element methodStructural engineeringAcousticsPhysics

Abstract

fetched live from OpenAlex

Abstract Slag or ash removal from water wall tubes in a radiant syngas cooler (RSC) is a routine task in operation, which can effectively increase the overall thermal efficiency. Rapping ash removal is one of the valid methods for shedding off the ash deposits in the high‐pressure surroundings of the RSC. In this study, the model of a real RSC was established by a numerical simulation based on the finite element software ABAQUS, and modal analysis and instantaneous response analysis (IRA) were researched to reveal the natural vibration characteristics of the RSC and aid the design of rapping ash removal. On the basis of the results of natural vibration characteristics and a subsequent harmony response, 16 Hz was set as the impact frequency, balancing off the vibrator number and the water‐tube displacement effect. It was indicated that ash deposit in the area around 1/2 longitudinal position was easy to remove due to great response to rapping action. Moreover, rapping at ½ position generally obtained greater displacement response in the whole RSC. A rapping design was proposed with six vibrators at the ½ longitudinal position and 16 Hz frequency, and then two rapping intervals were investigated comparatively. The case with a half‐cycle interval had a slightly larger response than the one‐cycle case, with a larger displacement in more circumferential tubes as well as with a greater sound pressure response in the RSC.

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 score0.012
Threshold uncertainty score0.334

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 head0.188
Teacher spread0.181 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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