Transformational Vehicle Management Systems
Bibliographic record
Abstract
The US helicopter industry has been working on a multi-year joint cooperative research and development program in the Vehicle Management Systems area. This work has been carried out under the auspices of the Vertical Lift Consortium and funded jointly by NRTC and the participating companies. The first phase of this program was executed in 2009 and 2010 and included a total investment in excess of $2.0 million. The focus was on areas and technologies of broad interest to all participants which included Bell Helicopter, The Boeing Company and Sikorsky Aircraft Corporation along with Penn State University. Together, these tasks help to define technology gaps and launching points for the AMRDEC Transformational VMS program. The premise of this VLC TAJI project was that current handling qualities requirement documents (ADS-33- PRF, MIL-F-9490D) are based on present knowledge and vehicles, and are considered necessary but may not be sufficient to ensure excellent handling qualities for new vehicles. Therefore, there is a need to update and refine these documents for the future. The TVMS project contributed to this goal both by gathering additional handling qualities data on a variety of aircraft, and discussing the results across key U.S. rotorcraft industry and government partners. In a second joint project, conducted under the auspices of this program, the participants investigated the use of active rotors for gust alleviation, with an extra emphasis on shipboard operations. A stochastic model of the ship airwake was developed. The gust rejection compensators were evaluated in Simulink development models for the H-60 class helicopter by Penn State and CH-47 airframes, and in a PSU-GenHel non-linear simulation using CETI turbulence models. In all cases, the final controllers provided a significant reduction in the body rate and attitude excursions from trim when compared to cases without the gust rejection compensator.
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.
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 | 0.000 |
| 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 | 0.001 |
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".