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Record W2158934352

3D parallel spectral computations of fan noise

2006· article· en· W2158934352 on OpenAlexaff
Farzad Taghaddosi, Wagdi G. Habashi

Bibliographic record

VenueResearch Repository (Delft University of Technology) · 2006
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsMcGill University
Fundersnot available
KeywordsDiscretizationEuler equationsPreconditionerDomain decomposition methodsComputer scienceComputational aeroacousticsFinite element methodAcoustic wave equationNoise (video)Applied mathematicsComputationIterative methodAlgorithmComputational scienceAcousticsMathematicsAeroacousticsMathematical analysisAcoustic wavePhysicsSound pressure
DOInot available

Abstract

fetched live from OpenAlex

A three-dimensional computational aeroacoustics code has been developed for the simulation of tone noise generated by turbofan engine inlets. The code is based on the linearized Euler equations, rewritten in terms of acoustic potential and solved in the frequency domain. Spatial discretization is performed using a spectral element method. Solution of the linear system of equations is based on the Schur complement method, which is solved using a matrix-free iterative method on multi-processors. A new preconditioner, which acts locally on individual subdomains, has been introduced to accelerate the convergence. Moreover, mathematical formulations are presented for implementation of geometric symmetry conditions for general, nonsymmetric wave propagation to further reduce computational cost associated with these types of problems. Numerical results include acoustic propagation from a uniform cylinder as a validation test case and a generic scarfed inlet with close to 2 million grid points, both solved using from 8-48 processors. The code is demonstrated to be robust and efficient in simulating ducted acoustic propagation.

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: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.432

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.224
Teacher spread0.214 · 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
Published2006
Admission routes1
Has abstractyes

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