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Record W3008970298 · doi:10.1080/10618562.2020.1724973

Combining CFD-EFD-FFD data via Gappy Proper Orthogonal Decomposition

2020· article· en· W3008970298 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational journal of computational fluid dynamics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicModel Reduction and Neural Networks
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputational fluid dynamicsAerodynamicsComputer scienceProper orthogonal decompositionAlgorithmDecompositionSpace (punctuation)Applied mathematicsMathematicsMechanicsPhysicsTurbulenceChemistry

Abstract

fetched live from OpenAlex

This paper demonstrates how, by means of Reduced Order Modelling (ROM), CFD cost can be drastically reduced and, in addition, a more complete investigation of a continuous design space obtained by adding experimental fluid dynamics (EFD) and flight fluid dynamics (FFD) data. The ‘Gappy’ Proper Orthogonal Decomposition (GPOD) method is used to enrich the EFD and FFD data to the size of their companion CFD. All three are then combined in a multidimensional database providing a much finer real-time exploration of a design space. Examples from aerodynamics and in-flight icing are given.

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)0.000
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 score0.831
Threshold uncertainty score0.635

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 science0.0010.000
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 head0.029
GPT teacher head0.309
Teacher spread0.280 · 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