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Record W4389244215 · doi:10.3397/in_2023_0206

Wideband fast multipole boundary element method for flow-induced noise analysis based on Lighthill's equation

2023· article· en· W4389244215 on OpenAlexaff
Takayuki Masumoto, Masaaki Mori, Yosuke Yasuda, Naohisa Inoue, Tetsuya Sakuma

Bibliographic record

VenueNOISE-CON proceedings · 2023
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsCybernet Systems Corporation (Canada)
Fundersnot available
KeywordsBoundary element methodNoise (video)Multipole expansionFlow (mathematics)Computer scienceFast multipole methodBoundary (topology)WidebandFinite element methodAlgorithmAcousticsMathematical analysisMathematicsGeometryPhysicsElectronic engineeringEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

In order to reduce the computational load for the analysis of three-dimensional flow-induced noise based on the Lighthill equation using the boundary element method (BEM), an efficient method is developed on the basis of the fast multipole BEM (FMBEM). The common hierarchical cell structure for grouping boundary element nodes, sound-receiving points and flow-induced noise sources is used; this enables coefficients of the FMBEM to be reused during the calculation of sound pressure at boundary element nodes and receiving points, thereby further accelerating the analysis. In addition, to deal with the increasing hierarchical level for encompassing flow-induced noise sources, which are often widely distributed around the object to be analyzed, a wideband version of the FMBEM is applied. Following the computational procedure, two application examples are presented for discussion. First, noise from a cylinder located in a flow is analyzed to discuss the accuracy of the method. Then, a problem with large number of degrees-of-freedom and flow-induced noise sources is analyzed to demonstrate its applicability to large-scale problems.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.760
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.033
GPT teacher head0.298
Teacher spread0.265 · 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
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 routes1
Has abstractyes

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