Design Of Aerodynamic Devices Using Genetic Optimization
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
The aerodynamics of ground vehicles is important for speed, stability, and fuel efficiency. Research has been conducted on various geometric shape optimization; however, there is limited research related to the design optimization of aerodynamic devices using genetic optimization algorithms. This paper performed aerodynamic optimization using genetic optimization algorithms, particularly the Non-Dominated Sorting Genetic Algorithm (NSGA -II). The method employed here involves the application of NSGA-II, OpenFOAM, and Bspline functions on a generic road vehicle geometry such as the Ahmed body. Due to computing resource constraints, the optimization was stopped after five generations. However, the resultant candidates through each generation trended towards a reduction of drag (cd) and lift (cl), or increase in downforce, thus, demonstrating the effectiveness of the program and proof of concept of the method. In the future, further improvement to the program can reduce the computational requirements of the optimization.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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