Browse Topic: Center of gravity (CG)
This paper presents results of flight tests conducted on a coaxial ultralight helicopter. An automated flight test evaluation method is presented and exemplified through its application to steady horizontal flight. The results shown include pilot controls, helicopter attitude angles, power, thrust and torque distribution between the rotors, rotor harmonic thrust components, and teeter angles, along with their rotor harmonic components across varying flight speeds. This study focuses on the dependencies of these parameters on center of gravity position and sideslip angle.
A 4.75-ft diameter hingeless hub proprotor model was wind tunnel tested up to the very high speeds of 205 knots, loosely corresponding to 480 knots full-scale, with parametric variations in blades, wing spar, and pylon center of gravity. Testing revealed that a gimballed-hub configuration that reached whirl flutter at 160 knots was completely stabilized when converted to a hingeless hub – using identical blades, span, and pylon. While the gimballed-hub model encountered whirl flutter at 160 knots, the hingeless-hub configuration remained stable throughout the entire test envelope up to 205 knots. The key conclusions are that a hingeless hub can eliminate whirl flutter, and that the most stable configuration is a swept-tip blade hingeless-hub rotor with the pylon center of gravity aft of the wing spar.
This study investigates the use of machine learning (ML) models to estimate the gross weight (GW), the longitudinal position of the center of gravity (CGx), and 1/rev cyclic flapping angles (Δ1c and Δ1s) of a compound helicopter with three redundant controls - main rotor RPM, collective propeller thrust, and stabilator angle. Neural Network (NN), Gaussian Process for Regression (GPR), and Support Vector Machine (SVM) algorithms are employed to develop estimation models using supervised training. The airspeed, redundant controls, main rotor controls, aircraft attitudes, and main rotor torque are selected as input variables (predictors) to the models due to their accessibility through the aircraft Health and Usage Monitoring System (HUMS). The dataset is split into low-speed and high-speed regimes to compare the prediction accuracy and training cost of separate regime models against a combined full-regime model. Separate airspeed regime GPR models showed superior performance in GW
Weight and balance activities are widely recognized and understood as important steps in the operation and maintenance of an aircraft to ensure safe and efficient flight. From the pilot's perspective, the operational limits and maneuverability of the aircraft are directly linked to the weight and balance of the aircraft. From a structural perspective, fatigue damage can vary significantly with center of gravity position and gross weight. In-flight center of gravity and gross weight estimation has been pursued for many years with varying success. One of the major challenges is the lack of data to verify an estimation model, since these parameters cannot be easily measured using sensors in flight. This paper reviews in detail the requirement and challenges of accurately monitoring center of gravity and gross weight. In addition, a survey of published work on the estimation of these values is provided. These efforts are divided into four categories: helicopter dynamic models, performance
This SAE Aerospace Standard (AS) defines the minimum performance requirements and test parameters for air cargo unit load devices requiring approval of airworthiness for installation in an approved aircraft cargo compartment and restraint system that complies with the cargo restraint requirements of Title 14 CFR Part 25, except for the 9.0-g forward ultimate inertia force of § 25.561 (b)(3)(ii).
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