Aerostructural design optimization of a 100-passenger regional jet with surrogate-based mission analysis
Bibliographic record
Abstract
In this paper we present a coupled aerostructural optimization procedure for the design of a fuelefficient regional aircraft configuration. A detailed mission analysis is performed on an optimized flight mission profile to accurately compute the mission range, fuel burn, and flight time. The mission analysis procedure is designed to allow flexible mission profiles including those with a variety of cruise, climb and descent segments in the profile. The direct operating cost (DOC) is computed based on the mission characteristics (fuel weight, range, and time), and is then used as the objective function in the optimization problem. We use a coupled aerostructural solver comprised of a high-fidelity structural solver and medium-fidelity aerodynamic solver to solve for the static aeroelastic shape of the lifting surfaces. Due to the large computational cost associated with these solvers, "kriging with a trend" surrogate models are employed to approximate the aerodynamic force and moment coefficients required in the mission analysis. This approach is demonstrated in two DOC minimization cases: a mission profile optimization with a fixed geometry, and an aerostructural optimization with fixed, previously optimized mission profiles for a 100-passenger regional jet aircraft.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".