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Record W2055525813 · doi:10.2514/6.2014-0675

The NASA High Speed ASE Project: Computational Analyses of a Low-Boom Supersonic Configuration

2014· article· en· W2055525813 on OpenAlexaff
Walter A. Silva, Antonio De La Garza, Paul Scott Zink, Elias Bounajem, Christopher L. Johnson, Michael Buonanno, Mark D. Sanetrik, Seung Yoo, George Kopasakis, David M. Christhilf

Bibliographic record

Venue55th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference · 2014
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsSupersonic speedSonic boomBoomAerospace engineeringComputer scienceEnvironmental scienceAeronauticsMarine engineeringEngineering

Abstract

fetched live from OpenAlex

A summary of NASA's High Speed Aeroservoelasticity (ASE) project is provided with a focus on a low-boom supersonic configuration developed by Lockheed-Martin and referred to as the N+2 configuration. The summary includes details of the computational models developed to date including a linear finite element model (FEM), linear unsteady aerodynamic models, structured and unstructured CFD grids, and discussion of the FEM development including sizing and structural constraints applied to the N+2 configuration. Linear results obtained to date include linear mode shapes and linear flutter boundaries. In addition to the tasks associated with the N+2 configuration, a summary of the work involving the development of AeroPropulsoServoElasticity (APSE) models is also discussed.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.251
Teacher spread0.238 · 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

Citations6
Published2014
Admission routes1
Has abstractyes

Explore more

Same venue55th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials ConferenceSame topicComputational Fluid Dynamics and AerodynamicsFrench-language works237,207