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Record W2312353094 · doi:10.2514/6.2011-183

Constrained and Unconstrained Aerodynamic Quadratic Programming Optimization Using High Order Finite Volume Method and Adjoint Sensitivity Computations

2011· article· en· W2312353094 on OpenAlexafffund
Mohammad Y. Azab, Carl Ollivier‐Gooch

Bibliographic record

Venue49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition · 2011
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaDeutsches Zentrum für Luft- und Raumfahrt
KeywordsAerodynamicsSensitivity (control systems)ComputationFinite volume methodSequential quadratic programmingMathematical optimizationApplied mathematicsQuadratic programmingQuadratic equationMathematicsComputer scienceAlgorithmPhysicsEngineeringAerospace engineeringMechanicsElectronic engineeringGeometry

Abstract

fetched live from OpenAlex

nd and 4 th order schemes reached the target geometry and required a comparable number of iterations. For transonic drag reduction without a lift constraint, the 2 nd and 4 th order schemes reached different optimal airfoil shapes with shock free pressure distributions. The final test case is a transonic drag reduction with a lift constraint. The lift constraint is satisfied using a penalty term in the merit function; the 2 nd and 4 th order accurate computations reached different optimal shapes with non shock free pressure distribution. The difference in the optimal 2 nd and 4 th order profiles is attributed to the difference in the penalty term in both 2 nd and 4 th order merit function; it is also attributed to the noise in the pressure sensitivity field due to the existence of weakened shock waves in the optimal flow solution.

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)0.003
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.020
GPT teacher head0.247
Teacher spread0.227 · 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

Citations2
Published2011
Admission routes2
Has abstractyes

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Same venue49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace ExpositionSame topicComputational Fluid Dynamics and AerodynamicsFrench-language works237,207