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Record W2281262592 · doi:10.1115/1.4032781

A Study of Acoustic Wave Resonance in Water-Filled Tubes With Different Wall Thicknesses and Materials

2016· article· en· W2281262592 on OpenAlexaff
Alireza Mokhtari, Vijay Chatoorgoon

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2016
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsComputational fluid dynamicsTurbulenceMechanicsAmplitudeResonance (particle physics)AcousticsAcoustic resonanceMaterials scienceRange (aeronautics)Tube (container)Flow (mathematics)PhysicsOpticsComposite material

Abstract

fetched live from OpenAlex

Acoustic resonance of a fluid-filled tube with closed and open outlet ends for zero and turbulent mean flows is investigated both experimentally and numerically for different wall materials and thicknesses. The main goal is to create a data bank of acoustic wave resonance in fluid-filled tubes at a frequency range of 20–500 Hz to validate and verify numerical prediction models used by the nuclear industry and to determine if there is a better method with existing technology. The experimental results show that there is a strong effect of turbulent flow, wall material, and wall thickness on resonant amplitudes at frequencies above ∼250 Hz. A numerical investigation is performed solving the linear wave equation with constant and frequency-dependent damping terms and a computational fluid dynamic (CFD) code. Comparing the one-dimensional (1D) and CFD results shows that CFD solution yields better predictions of both resonant frequency and amplitude than the 1D solution without the need for simplified added damping methods, which are required by the 1D methodology. This finding is valid especially for frequencies higher than ∼300 Hz.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.675
Threshold uncertainty score0.134

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 head0.007
GPT teacher head0.185
Teacher spread0.178 · 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 designBench or experimental
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

Citations1
Published2016
Admission routes1
Has abstractyes

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