Bibliographic record
Abstract
The CH-53K® King Stallion™ is the most advanced heavy lift helicopter developed by Sikorsky, a Lockheed Martin Company, to address the requirements of the United States Marine Corps. The aircraft was designed to support missions with a maximum design gross weight of 88,000 lbs and can carry external loads up to 36,000 lb. Performance flight tests for the CH-53K® have been completed as part of its System Design and Development (SDD) phase. Tethered hover and level forward flight performance measurements have been acquired that are used as a basis for Naval Air Training and Operating Procedures Standardization (NATOPS) flight manual performance charts. They were also used in the Key Performance Parameter (KPP) verification analysis, demonstrating that the CH-53K® exceeds its KPP for mission effectiveness. In addition to overview descriptions of the performance flight test program, the test results are herein compared with predictions from aircraft performance modeling tools that were largely based on earlier comprehensive model scale hover stand and wind tunnel tests. In some cases, the flight test results dictated that adjustments be made to the aircraft performance prediction tools so they consistently represent the overall vehicle flight performance characteristics. Recent computational fluid dynamics (CFD) simulations were employed to assist with the overall understanding of the complex aerodynamic flow field in both hover and forward flight. This includes modeling simulations using CREATE-AV™ Helios full-aircraft tool for rotor performance and interactional effects and the Simcenter STAR-CCM+ tool for isolated fuselage drag and fuselage variations. The insights gained from the CFD analysis, such as parasitic drag contributions, main rotor and tail rotor interactions, and other aerodynamics interactions, were used to make appropriate input adjustments to the aircraft performance modeling tools for enhanced correlation to the flight-test results.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".