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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 OpenAlexfundno aff
Dimitri J. Mavriplis, Evan Anderson, Ray S. Fertig, Mark Garnich

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations6
Published2016
Admission routes1
Has abstractyes

Explore more

Same venue57th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials ConferenceSame topicComputational Fluid Dynamics and AerodynamicsFrench-language works237,207