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Record W2330099294 · doi:10.2514/6.2016-1175

Development of a High-Fidelity Time-Dependent Aero-Structural Capability for Analysis and Design

2016· article· en· W2330099294 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venue57th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference · 2016
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsnot available
FundersCanadian Centre for Applied Research in Cancer Control
KeywordsComputer scienceFidelityTelecommunications

Abstract

fetched live from OpenAlex

The development of a tightly coupled aeroelastic simulation capability for analysis and design is described in this paper. The method makes use of a well established unstructured mesh computational fluid dynamics solver, combined with a recently developed structural dynamics code. These two disciplinary codes are coupled through a fluid-structure interface and a mesh deformation capability. The discrete adjoint for all disciplinary software components has also been implemented with the goal of enabling time-dependent aeroelastic optimization. The individual disciplinary components are validated both in analysis and adjoint mode. Subsequently, the coupled aeroelastic analysis capability is demonstrated for both static and dynamic problems. Based on the validation and performance of these components, the future development of a time dependent coupled aeroelastic adjoint optimization capability is described.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.012
GPT teacher head0.225
Teacher spread0.213 · 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