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Record W2320754164 · doi:10.2514/6.2002-5172

Towards an Integrated Design Environment for Hypersonic Vehicle Design and Synthesis

2002· article· en· W2320754164 on OpenAlexaff
Jeffrey Zweber, Hanee Kabis, William Follett, Narayan Ramabadran

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsComputer scienceSystems engineeringAerospace engineeringHuman–computer interactionAutomotive engineeringEngineering

Abstract

fetched live from OpenAlex

The US Air Force Research Laboratory, along with its contractor partners, is developing an integrated modeling environment for the conceptual and preliminary-level design and synthesis of airbreathing, hypersonic vehicles. This effort is built on the team’s successful prototype of a similar environment for rocket-powered space access vehicles. The modeling environment under development will begin by developing a 3-4 level deep hierarchy of objects that represent a hypersonic vehicle. Initially, these objects will contain only conceptual-level representations of the geometry and mass properties of the vehicle and its components. This initial information will be used with a vehicle synthesis routine to develop an initial conceptual design. This is typically called the “as drawn” design. The second step in the design process is an initial analysis of the aerodynamic and propulsive characteristics of the vehicle. These analyses will be conducted in the environment and the geometric model that was developed in the initial hierarchy of objects will be of sufficient fidelity to support these analyses. Next, the mass properties, aerodynamic and propulsion analysis results will be used by a trajectory simulation code, also integrated into the environment, to determine if the initial vehicle design will meet the mission performance requirements. Finally, the results of the trajectory simulation will be used to iteratively resize the vehicle until the mission requirements are satisfied. The above process depicts what is known as the closure process, that is, matching the required vehicle propellant fraction for a given mission to the available vehicle propellant fraction. The purpose of the integrated modeling environment is to streamline this closure process. Additionally, this paper will describe the modeling environment used for this effort, lessons learned from the development of the environment for rocket-powered vehicles, and the next steps planned to

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)0.006
metaresearch head score (Gemma)0.006
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 score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0040.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.004

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 head0.081
GPT teacher head0.248
Teacher spread0.167 · 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

Citations13
Published2002
Admission routes1
Has abstractyes

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