Development of a Multi-Disciplinary Optimization Framework for Nonconventional Aircraft Configurations in PACELAB APD
Why this work is in the frame
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Bibliographic record
Abstract
<div class="section abstract"><span class="label">1</span><div class="htmlview paragraph">Most traditional methods and equations for estimating the structural and nonstructural weights and aerodynamics used at the aircraft conceptual design phase are empirical relations developed for conventional tube-and-wing aircraft. In a computation-heavy design process, such as Multidisciplinary Design and Optimization (MDO) simplicity of calculation is paramount, and for conventional configurations the aforementioned approaches work well enough for conceptual design. But, for non-traditional designs such as strut-braced winged aircraft, empirical data is generally not available and the usual methods can no longer apply. One solution to this is a movement toward generalized physics-based methods that can apply equally well to conventional or non-traditional configurations. In this work, physics-based methods for calculating the aerodynamic drag and wing weight of an aircraft were implemented in a commercial aircraft conceptual design and optimization tool, PACELAB Aircraft Preliminary Design (APD), which in its default form utilizes traditional empirical methods for estimating these characteristics. The new methods are based on past MDO work at Virginia Tech and are general enough to appropriately capture the physics of nonconventional models, yet are also simple enough that they can be realistically applied in an MDO environment. Special attention has been paid to capturing the transonic wave drag effects encountered through the cruise regime. Preliminary design optimizations for minimum fuel consumption were performed in the extended version of PACELAB APD for a mid-range regional airliner type mission, and results show significant fuel savings using a strut-braced wing configuration.</div></div>
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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