Unmanned Aerial Vehicle Conceptual Design Using a Genetic Algorithm and Data Mining
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Aircraft design is a complex process involving multiple co-dependent design variables and many design decisions. For commercial aircraft design, this di‐culty is ofiset somewhat by the wealth of knowledge available. Observing existing designs has provided useful empirical relationships and insights for the designer to apply yielding a relatively well deflned problem. The wide variety of conflguration possibilities, mission proflles, and the relative lack of historical data leave the problem of unmanned aerial vehicle (UAV) design less deflned. The purpose of this research was to develop a robust optimization package for UAV design using data mining to aid conflguration decisions and to develop empirical relationships applicable to a wide variety of mission proflles. An optimization software package was developed using a Genetic Algorithm (GA) and Data Mining. The algorithm proved succesful in carrying out the preliminary design phase of a number of test cases similar to existing UAVs. Designs produced by the algorithm promise improved performance and reduced development time. Future work will introduce high fldelity analysis to the framework developed in this research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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