Bioinspired Aerodynamic Load Estimation for UCAVs in Turbulent Environments
Bibliographic record
Abstract
Small aircraft carriers play a crucial role in military navigation and field intelligence. Unmanned Combat Aerial Vehicles (UCAVs) have emerged as indispensable tools for various military operations. However, the efficacy of UCAV operations is often hampered by the turbulent conditions generated by oceanic gusts. To enhance the reliability and autonomy of these systems, this research focuses on advancing aerodynamic load estimation techniques.
 Many biological systems employ sensory systems to collect environmental feedback to improve predation and avoid attack. Examples include the proprioception exhibited in bird feathers and the lateral line function in fish. Drawing inspiration from these biological models, a system of sensors is employed to achieve more precise estimations of the UCAV aerodynamic state and therefore train an existing Multi-Layer Perception Artificial Neural Network (MLP ANN) to respond to the environment.
 A series of experiments were conducted on a non-slender delta-wing model of NACA 0012 mounted on a force/moment sensor and exposed to varying flow conditions in a controlled setup called the WindShaper. To simulate gusty environments, the WindShaper produced unsteady random axial gusts ranging from 5m/s to 15m/s. A total of 100 sets of data, consisting of 60000 data points were collected and used to train the MLP ANN.
 Statistical methods were employed to distinguish valid and edge cases within the datasets. From analyzing the outputs generated by the MLP ANN, it was determined that there exists an importance in collecting data of high quality. These analyses identify factors affecting model performance and highlight specific challenges that hinder accurate gust prediction. The utilization of advanced neural network methodologies as a technique for load estimation has significant implications for naval aviation, aerodynamics, and their related autonomous systems. This research contributes to the development of more robust UCAV operations in challenging environments.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".