Aircraft conceptual design using genetic algorithms
Bibliographic record
Abstract
Nomenclature Aircraft design is a complex multidisciplinary process to determine aircraft configuration variables that satisfy a set of mission requirements. It is very hard for aircraft designers to foresee the consequences of changing certain variables. Furthermore, conventional optimization processes are limited by the type and number of parameters used, resulting in sub-optimal designs. The objective of this research is to test the functionality and implementation of a multidisciplinary aircraft conceptual design optimization method using an adaptive genetic algorithm (GA), as a feasible alternative to the existing sizing and optimization methods. To illustrate the approach the algorithm is used to optimize a medium range commercial aircraft, with takeoff weight as an optimization goal, subjected to constraints in performance and geometric parameters. Adaptive and traditional formulations for the handling of constraints by the GA are tested and compared. Results show the ability of the adaptive GA to unbiased search through the design space of aircraft conceptual designs, leading to more viable aircraft configurations than the traditional GA approach at reduced timeframes, with a lower cost than current aircraft design optimization procedures.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".