Towards an Integrated Design Environment for Hypersonic Vehicle Design and Synthesis
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".