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Record W2889940056 · doi:10.1115/ipc2018-78409

In-Situ Modal Response Characterization of Pipe Structures Through Reynolds Number Variation

2018· article· en· W2889940056 on OpenAlexaff
Haobin Chen, Ron Hugo, Simon Park

Bibliographic record

VenueVolume 3: Operations, Monitoring, and Maintenance; Materials and Joining · 2018
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsReynolds numberModalExcitationVibrationSpectral lineModal analysisTurbulencePipe flowMechanicsAcousticsMaterials sciencePhysics

Abstract

fetched live from OpenAlex

The modal response characterization of structures is a proven and reliable technique used to monitor system behavior and change, providing information for condition assessment and damage identification. In traditional modal response characterization procedures, an external mass excitation source is used to excite the system, and this is modeled as an impact function. This provides system forcing across a broad range of frequencies. In this investigation, an in-situ method of system excitation is explored. The modal characteristics of externally-supported pipe structures are investigated by varying the flow Reynolds number (Red). Given the increase in flow turbulence with Reynolds number, hydrodynamic pressure fluctuations on the pipe wall provide a varying excitation source. This removes the requirement for an external excitation source. A comparative analysis of data sets collected for both Acrylic and ABS pipe material show similar pressure spectra, while vibration spectra change significantly. Pressure spectra reveal a character whereby the spectral energy increases with increasing Reynolds number. A comparison of in-situ results to those obtained using traditional impact response tests show that vibration spectra collected through Reynolds number variation successfully capture the modal characteristics of the pipe-structure.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.775

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 communication0.0000.001
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 head0.013
GPT teacher head0.271
Teacher spread0.258 · 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
Published2018
Admission routes1
Has abstractyes

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