Aerospace digital twins: examining applications of digital twin technology to unmanned aerial vehicles and satellites
Bibliographic record
Abstract
Unmanned aerial vehicles (UAV) and satellites are becoming increasingly popular in business, government, and military applications. Both have unique use cases and value, but they have several overlapping use cases and features. Most notably they are both used for observation, such as the case of climate monitoring or surveying and mapping. Satellites also have uses in communication and navigation by broadcasting signals and enabling technology such as global positioning systems (GPS). UAVs have also been deployed by the militaries across the world for both reconnaissance and offensive capabilities. Each are electro-mechanical systems with a several important components that need to be reliable and high performance. Maximizing the return in value for these assets might mean improving their performance, reliability, or longevity. One emerging technology that has the promise to do this is the digital twin (DT). DTs utilize a combination of multi-domain modeling and extensive data collection for real-time model updates. This real time updating can be utilized for advanced simulation, improved control, and advanced condition monitoring. DTs are an ideal platform for applying to UAVs and satellites to maximize their capabilities and values. As will be demonstrated in this work, DTs have been demonstrated to provide value in improving control performance, orientation and position tracking, condition monitoring, and fault detection in UAVs and satellites. A case study and preliminary work on a CubeSat attitude adjustment device DT has been presented and examined to display benefits of the concept.
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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 | 0.000 | 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 | 0.001 |
| 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 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".