MétaCan
Menu
Back to cohort
Record W4286426690 · doi:10.1515/secm-2022-0015

Vibration and noise reduction of pipelines using shape memory alloy

2022· article· en· W4286426690 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScience and Engineering of Composite Materials · 2022
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsConcordia University
Fundersnot available
KeywordsMaterials scienceShape-memory alloyReduction (mathematics)VibrationSMA*LaminationNoise (video)Pipeline (software)Composite materialStiffnessFiberNoise reductionStructural engineeringAcousticsMechanical engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

Abstract In this article, the pipeline design is introduced. The new pipe structure is made of new fiber metal laminates with the unidirectional composite and sheet metal (Ti–Ni alloy). Many pipe structures are in the heating environment such as in or around the engine, which will also cause the heating-up structure. If the shape memory alloy (SMA) fiber is added to the composite lamination, it can be seen that with the increase of temperature, the stiffness of the structure is increased and so is its frequency. The changed frequency of the structure can avoid the excitation frequency in this way, which effectively inhibits the resonance. In dynamic analysis, it can also show that the pipeline with the SMA fiber has good performance for vibration reduction and noise attenuation. Additionally, the convergence of the meshing model and the effect of the thickness of the SMA material on vibration and noise reduction are also discussed.

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.

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 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 score0.005
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.014
GPT teacher head0.232
Teacher spread0.217 · 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