Future management of helicopter fleets will be more heavily based on individual component damage tracking and less on legacy usage monitoring (flight parameter-based) methods. This enhances health assessment capabilities by taking into account the actual loads on a component-by-component basis. However, accurate loads prediction in rotating frame components remains a challenge. Even with advanced computational fluid dynamics (CFD) techniques, prediction of the unsteady aerodynamic loads acting on the rotor blades is computationally intensive and problematic in terms of accurate loads prediction across the entire flight regime of the helicopter. High-speed flight can potentially introduce both shock and near-stall effects within a given rotor rotation. Low-speed flight can include blade-vortex interaction effects, wherein flow from a given blade (vorticity loading from tip vortices) impinges upon the preceding blade, causing unsteady aerodynamic loading that is difficult to quantity and predict numerically. Vehicle maneuvering can produce significantly higher blade pitching moments than steady flight. All of these regimes combine to represent the loading history of the rotor system. Therefore, accurate loads prediction methods, in terms of matching peak-to-peak, magnitude, phase, as well as vibratory/harmonic content, are required that capture all flight regimes for all critical structural components.
This research focuses on the development of a loads prediction method, known as the Load Confluence Algorithm (LCA), and its application to the analysis of a large set of flight test data from the NASA/US Army UH-60A Airloads Program. The LCA combines measured response at a prescribed set of locations with a numerical model of the rotor system. For a given flight condition (steady flight, maneuvers, etc.) the numerical simulation's predicted loads distribution is iteratively incremented (by harmonic) until convergence with measured loads is reached at the prescribed locations (control points). Predicted loads response at non-instrumented locations is shown to be improved as well, thus enhancing fatigue lifing methods for these components.
The procedure specifically investigates the harmonic content of the applied loads and the improved prediction of the harmonic components. The impact of the enhanced accuracy on loads predictions on component structural fatigue is illustrated by way of an example.
Results show that, for a limited sensor set (two 3-axis sensors per blade), blade loads are accurately predicted across a full range of flight regimes. Hub loads are best modeled using the pushrod as the control point. Results also show that load magnitude has a tremendous influence on damage, with a 25% over-estimation of vibratory load resulting in a damage factor of nearly 3. This research highlights the importance of accurate loads prediction for a rotorcraft life tracking program. Small inaccuracies in loads lead to dramatic errors in damage assessment.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/45837 |
| Date | 18 September 2012 |
| Creators | McColl, Chance C. |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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