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Record W4239818217 · doi:10.32920/ryerson.14657070

Development of a Combined-Ritz Method for Modeling 3D Acoustics in Candu Fuel Sub-Channels

2021· preprint· en· W4239818217 on OpenAlexafffund
David Villero

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsToronto Metropolitan UniversityUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAcousticsNode (physics)Finite element methodInletAcoustic impedanceStructural acousticsChannel (broadcasting)Acoustic waveEngineeringMechanicsPhysicsStructural engineeringMechanical engineeringTelecommunications

Abstract

fetched live from OpenAlex

A combined finite element-Ritz method is developed to effectively model the 3D lowfrequency acoustics in CANDU fuel sub-channels. The complex acoustic behavior of CANDU fuel sub-channels in the cross section is captured using the six-node isoparametric triangular elements; and the acoustic wave propagation in the axial direction is modeled using the polynomials of order n. The Lagrange equations are utilized to formulate the system equations of motion. The acoustic system considered in this study consists of pipe-like medium (water) with rigid and smooth walls. At the inlet of the fuel channel acoustic system, an acoustic pressure wave is prescribed to simulate the pulsation induced by the main feeder pumps. At the outlet, the acoustic system is assumed to interact with a reacting and absorbing material with prescribed acoustic impedance. The method was tested for several scenarios of interest. Numerical results obtained are in excellent agreement with the analytical and ANSYS solutions.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.552
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.042
GPT teacher head0.278
Teacher spread0.236 · 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.

Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2021
Admission routes2
Has abstractyes

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