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Record W2056026427 · doi:10.2514/6.2005-127

Evaluation of Nearby Flows by a Shape Sensitivity Equation Method

2005· article· en· W2056026427 on OpenAlexaff
Régis Duvigneau, Dominique Pelletier

Bibliographic record

Venue43rd AIAA Aerospace Sciences Meeting and Exhibit · 2005
Typearticle
Languageen
FieldEngineering
TopicAdvanced Numerical Methods in Computational Mathematics
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsSensitivity (control systems)Computer scienceEngineeringElectronic engineering

Abstract

fetched live from OpenAlex

This paper studies the use of the Continuous Sensitivity Equation Method (CSEM) for the Navier-Stokes equations to compute nearby solutions, in the particular case of shape parameters. The o w and sensitivity elds are solved using an adaptive nite-elemen t method. A new approach is presented to extract accurate o w derivatives at the boundary, as they appear in the sensitivity boundary conditions for shape parameters. High order Taylor series expansions are used on layered patches in conjunction with a constrained least- squares procedure to evaluate accurate rst and second derivatives of the o w variables at the boundary. The proposed methodology is rst veried on a problem with a closed form solution obtained by the Method of Manufactured Solutions. The methodology is then applied to airfoil o ws, the CSEM yielding fast o w evaluation for such shape parameters as airfoil thickness, angle of attack and camber. tiate approach (often called Discrete Sensitivity Equation Method), the discrete form of the o w equations are dieren tiated and the total derivative of the o w discretization with respect to the design parameters is calculated. In the dieren tiate-then-approximate approach (known as the Continuous Sensitivity Equation Method CSEM), partial dieren tial equations for the o w sensitivities are obtained by implicit dieren tiation of the equations governing the o w. They are then approximated numerically. The CSEM is preferred for the present study, because it oers several advantages over the discrete sensitivity approach. In particular, since dieren tiation occurs before any discretization, the delicate com- putation of mesh sensitivities and all of the overhead associated with them are avoided. Consequently, the CSEM requires less memory and is computationaly less expensive than automatic dieren tiation, as shown by Borggaard and Verma. 3 Moreover, the CSEM is a natural approach when using adaptive methods: since the topology of the mesh changes with adaptation, mesh derivatives do not exist, making the discrete sen- sitivity method ill-suited. Another advantage is that there is no requirement to use the same algorithm to approximate the CSE and the original PDE model. Thus, special algorithms can be constructed to take advantage of the linear structure of the CSE. However, the main dicult y with the CSEM arises when one deals with shape parameters. In this particular case, o w gradients of the PDE solution are required as source terms in the CSE and they also appear as coecien ts in the boundary conditions for the CSE. Flow gradients in the interior of the computational domain can be computed relatively easily and accurately by a local projection technique (which is already used for error estimation). However, the accuracy of such reconstructed derivatives degrades near the boundary. 4 This induces errors in the boundary conditions that results in poor solutions for the sensitivity elds. The current study presents a new approach to obtain accurate boundary conditions for the CSE. It uses high order Taylor series expansions in conjunction with a constrained least-squares procedure. The

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: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.054
GPT teacher head0.344
Teacher spread0.290 · 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
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

Citations11
Published2005
Admission routes1
Has abstractyes

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