Advanced business jet conceptual design and cost optimization using a genetic algorithm approach
Bibliographic record
Abstract
The present challenge of the business and regional aircraft markets is to obtain a highperformance aircraft with a premium on passenger comfort at a very low price. With the maturity of the high subsonic aircraft markets, a significant increase in performance efficiency had been obtained, but the main challenge still lies in the tradeoffs between the possible obtained performance and aircrafts cost. This research discusses the application of a Genetic Algorithm (GA) in conceptual design and optimization to obtain the optimum external configuration for a long range, eight passenger business aircraft to meet the above objectives. Operating Cost of the aircraft is considered as the objective function to be minimized, and constraints are imposed in performance and geometric parameters based on the given aircraft requirements. Continuous and discrete aircraft variables are defined within the GA optimization process to provide a more accurate aircraft characterization. Improvement approaches are discussed as well as comparison with other global optimization methods is performed. The results obtained in this case study show the ability of GA's to explore the design domain, effectively finding optimum aircraft designs characteristics, and meeting the specified performance goals at reduced operating costs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".