Aircraft Conceptual Design Optimization with Uncertain Contributing Analyses
Why this work is in the frame
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Bibliographic record
Abstract
This paper outlines the development of a multi-disciplinary design optimization (MDO) architecture for aircraft conceptual design that includes the assessment of uncertainties introduced by approximate equations or computational methods in the contributing disciplinary analyses. Aircraft conceptual design traditionally harnesses prior knowledge in the form of empirical or statistical equations and low fidelity analysis. This approach is computationally inexpensive and allows for rapid design iterations. However, the use of approximate methods introduces uncertainties that can lead to an optimum conceptual design that, when subjected to more detailed analysis later in the design process, is found to fail one or more of the design goals. This may lead to costly revision. By assessing the uncertainty of the contributing analyses using Reliability Based Design Optimization (RBDO) methods, the probability of failure of a given conceptual design can be estimated and minimized.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it