OPTIMAL GEOMETRICAL DESIGN OF AIRCRAFT USING GENETIC ALGORITHMS
Bibliographic record
Abstract
With the advent of computers and search and optimization tools such as the genetic algorithm, the ability to manipulate numerous aircraft design parameters in a reasonable amount of time is feasible. From this angle, the lengthy time and effort spent creating and integrating aerodynamics codes, sizing routines, and performance modules, can be mitigated by the use of a genetic algorithm. Consequently, a genetic algorithm has been created and employed as a cost effective tool to explore possible aircraft geometries in the conceptual design process of the aircraft. A program has been developed to address most aspects of aircraft design such as aircraft sizing and configuration, performance, and propulsion, to name a few. These codes have been integrated into a genetic algorithm, which performs the task of searching and optimizing. The adaptive penalty method has been employed to handle all constraints imposed on the design. In addition, adjustments for varying degrees of selection and crossover intensities and types have been studied. A design study has also been carried out to compare an existing aircraft shape with the genetic algorithm optimized aircraft shape and configuration. Results indicate that the genetic algorithm is a powerful multi-disciplinary optimization and search tool, capable of simultaneously managing and varying numerous aircraft design parameters. Moreover, the genetic algorithm is capable of finding aircraft geometries and configurations that are both efficient and cost effective.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".