Performance Enhancement of Tilt-Rotor Unmanned Aerial Vehicle Using Nacelle-Fixed Auxiliary Wing
Bibliographic record
Abstract
No AccessEngineering NotePerformance Enhancement of Tilt-Rotor Unmanned Aerial Vehicle Using Nacelle-Fixed Auxiliary WingMyeong Kyu Lee and In LeeMyeong Kyu LeeSchool of Mechanical, Aerospace and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon 305-701, Republic of Korea and In LeeSchool of Mechanical, Aerospace and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon 305-701, Republic of KoreaPublished Online:29 Jan 2013https://doi.org/10.2514/1.C031896SectionsRead Now ToolsAdd to favoritesDownload citationTrack citations About References [1] Nixon M. W., "Improvements to Tilt Rotor Performance Through Passive Blade Twist Control," NASA TM-100583, 1988. Google Scholar[2] Lake R. C., Nixon M. W., Wilbur M. L., Singleton J. D. and Mirick P. H., "Demonstration of an Elastically Coupled Twist Control Concept for Tilt Rotor Blade Application," AIAA Journal, Vol. 32, No. 7, 1994, pp. 1549–1551.doi:https://doi.org/10.2514/3.12232 AIAJAH 0001-1452 LinkGoogle Scholar[3] Ozbay S., "Extension-Twist Coupling Optimization in Composite Rotor Blades," Ph.D. Dissertation, School of Aerospace Engineering, Georgia Inst. of Technology, Atlanta, 2006. Google Scholar[4] Prahlad H. and Chopra I., "Design of a Variable Twist Tiltrotor Blade Using Shape Memory Alloy (SMA) Actuators," Proceedings of SPIE, Vol. 4327, Aug. 2001, pp. 46–59. doi:https://doi.org/10.1117/12.436559 PSISDG 0277-786X CrossrefGoogle Scholar[5] Park J. S., Kim S. H., Jung S. N. and Lee M. K., "Design and Analysis of Variable-Twist Tiltrotor Blades Using Shape Memory Alloy Hybrid Composites," Smart Materials and Structures, Vol. 20, No. 1, 2011, pp. 1–10. doi:https://doi.org/10.1088/0964-1726/20/1/015001 SMSTER 0964-1726 CrossrefGoogle Scholar[6] Wang J. M., Jones C. T. and Nixon M. W., "A Variable Diameter Short Haul Civil Tiltrotor," Proceedings of the American Helicopter Society 55th Annual Forum, Vol. 2, American Helicopter Society International, Alexandria, VA, 1999, pp. 2170–2177. Google Scholar[7] KARI, "Tiltrotor Aircraft," Republic of Korea Patent No. 10-0822366, 8 April 2008. Google Scholar[8] Yeo H., Sinsay J. D. and Acree C. W., "Selection of Rotor Solidity for Heavy Lift Tiltrotor Design," Journal of the American Helicopter Society, Vol. 55, No. 1, 2010, pp. 0120101(1)–0120101(12). doi:https://doi.org/10.4050/JAHS.55.012010 JHESAK 0002-8711 CrossrefGoogle Scholar[9] Lee J. J., Kim J. M., Lim C. H. and Han J. W., "Development of Airframe for Smart UAV," Proceedings of the Korean Society for Aeronautical & Space Sciences 2008 Fall Conference, Vol. 1, The Korean Society for Aeronautical & Space Sciences, Seoul, Korea, 2008, pp. 410–413. Google Scholar[10] Pamadi B. N., Performance, Stability, Dynamics, and Control of Airplanes, AIAA, Reston, VA, 2004, pp. 56–57. LinkGoogle Scholar[11] ANSYS Fluent, Software Package, Release 12, ANSYS Inc., Canonsburg, PA, 2009. Google Scholar[12] Yeo H. and Johnson W., "Performance and Design Investigation of Heavy Lift Tilt-Rotor with Aerodynamic Interference Effects," Journal of Aircraft, Vol. 46, No. 4, 2009, pp. 1231–1239.doi:https://doi.org/10.2514/1.40102 JAIRAM 0021-8669 LinkGoogle Scholar[13] Lan C. E. and Roskam J., Airplane Aerodynamics and Performance, Roskam Aviation and Engineering Corp., Ottawa, KS, 1981, pp. 455–456. Google Scholar Previous article Next article
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".