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Record W4415575366 · doi:10.25144/14778

NUMERICAL INVESTIGATION OF THE EFFECTS OF MODEL UNCERTAINTIES ON COMPONENT-BASED TRANSFER PATH ANALYSIS METHOD WITH DYNAMIC

2023· article· W4415575366 on OpenAlexafffund
Simon Prenant, Thomas Padois, Thomas Dupont, Olivier Doutres

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicVehicle Noise and Vibration Control
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of CanadaConsortium de Recherche et d’innovation en Aérospatiale au Québec
KeywordsPath (computing)Control theory (sociology)Stability (learning theory)Numerical analysisTransfer functionWork (physics)

Abstract

fetched live from OpenAlex

The hydraulic pumps are considered to have a major contribution to the structure borne noise generated inside aircrafts.Methods such as Component-Based Transfer Path Analysis (CB-TPA) are promising tools for aircraft manufacturers to build internal processes for the specification, design and validation of the impact of vibrating systems on new aircrafts.However, the experimental applicability of these methods remains limited due to some experimental difficulties.CB-TPA' formulation is based on dynamical quantities, which require the determination of terms related to rotational degrees of freedom.Indirect methods such as Virtual Point (VP) are commonly employed for this purpose, but it results in more cumbersome experimental set-ups and increased measurement uncertainties.The implementation of these methods can also be difficult in the case of a flat and thin receiving structure as commonly encountered in aircraft applications, since in-plane translational excitations have to be applied and the access to the vibrating system/structure interface points is limited.In this work, a decoupling procedure is used to overcome these issues.A numerical model is used to investigate the influence of model uncertainties on the CB-TPA' predictions, when the dynamical quantities are provided by VP method including the decoupling procedure.

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: none
Teacher disagreement score0.574
Threshold uncertainty score0.703

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.010
GPT teacher head0.237
Teacher spread0.226 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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