Description:
TECHNOLOGY AREA(S): Air Platform, Sensors
OBJECTIVE: Develop an innovative, physics based system which incorporates measurements taken by small, unobtrusive sensors located within the fuselage to accurately predict rotor head loads generated during all phases of flight, including turbulent flow, buffeting, and the influence of tail rotor interactions.
DESCRIPTION: As the service lives of aircraft are continually extended, the ability to reliably and accurately predict the initiation and propagation of fatigue cracks will become increasingly critical to maintaining aircraft availability. For rotorcraft, this requires that the loads imparted to the rotor head be accurately and correctly predicted over a variety of flight conditions. There is currently a technological gap between the predictive capabilities of modern computational fluid dynamics (CFD) coupled with computational structural dynamics (CSD) software and the ability to determine rotor head loads during all phases of flight, capturing turbulent flow, buffeting, and the influence of tail rotor interactions. This gap includes deficiencies in comprehensive loads prediction across all flight regimes (hover to high maneuvers to high speeds) as well as lack of main rotor, fuselage, and tail rotor integration. A physics-based approach is sought which combines with selective sensors located within the fuselage, e.g. accelerometers, [Ref. 1] that can accurately predict rotor head modal content and loads generated by various flight maneuvers such as hover, high speed flight, and maneuvers contributing large pitching and rolling moments, each at a range of gross weights and centers of gravity (CG). It is also important that the system be adaptable over a fleet of aircraft each of which may have unique differences in build, load-out, repair configuration, which would affect the stiffness and dynamic responses of the airframe and rotor. The innovative methodology the system uses will be able to incorporate the full maneuver spectrum of both the main and tail rotor [Ref. 12].
PHASE I: Determine feasibility for the development of the methodology needed to accurately predict the first eight excitation modes of the main rotor and tail rotor. Demonstrate the feasibility of this methodology by correlating the predicted loads of a rotor blade in above ground hover condition for amplitude, phase, and frequency content when compared to publicly available data, such as for the UH-60A [Ref. 12]. Identify a minimum number of key sensors, types and mounting locations needed from a loads standpoint for the model prediction efforts including sample rates to capture the dominant modal content.
PHASE II: Develop and expand the methodology developed during Phase I to include all dominant excitation modes. Demonstrate the ability to accurately predict rotor head loads using the methodology for other flight maneuvers at various regimes such as steady and transient conditions. Validate the loads prediction methodology using publicly-available loads data for a conventional (single main rotor, single tail rotor) rotorcraft [Ref. 12]. Validate the sensors identified in Phase I will provide all necessary data for the methodology.
PHASE III: Implement the methodology into a system that utilizes the sensors identified in Phase I. Enhance the utility of the loads prediction methodology by incorporating the capability to predict fatigue damage initiation and propagation of critical fuselage and dynamic components. Integrate the system into a flight test vehicle. Validate fatigue life predictions based on flight loads using real-world aircraft data. Validate the performance of the loads prediction methodology using in-flight data at a variety of gross weight and CG combinations [Ref. 12]. Private Sector Commercial Potential: The technology developed herein has immediate potential application for rotating machinery and energy generation equipment such as windmills, compressors, turbines, and water pumps, where direct measurement of loads on the rotating components are essential yet impractical and costly. In addition, the coupled fuselage/rotor load prediction methodology could be tailored and validated against any rotorcraft used in the private sector.
REFERENCES:
1. Haas, D. (1991). Determination of Helicopter Flight Loads from Fixed System Measurements. AIAA 32nd Structures, Structural Dynamics, and Materials Conference
2. Bakker, R.J.J., Bos, M.J., Münninghof, N., van Tongeren, J.H., van der Ven, H. (2009). A Modelling Framework for the Calculation of Structural Loads for Fatigue Life Prediction in Helicopter Airframe Components. National Aerospace Laboratory paper NRL ERF2009-101154
3. Datta, A., Sitaraman, J., Chopra, I., Baeder, J.D. (2006). CFD/CSD Prediction of Rotor Vibratory Loads in High-Speed Flight. Journal of Aircraft, Vol. 43, No. 6, pp. 1698-1709, doi: 10.2514/1.18915
4. Giaansante, N., Jones, R., Calapodas, N.J. (1981). Determination of In-Flight Helicopter Loads, 37th Annual Forum of the American Helicopter Society, New Orleans, LA
5. Guo, X.L., Li, D.S. (2004). Experiment Study of Structural Random Loading Identification by the Inverse Pseudo Excitation Method. Journal of Structural Engineering and Mechanics, Vol. 18, No. 6, pp. 791-806
6. Lang, P., Centolanza, L. (2006). Improved High Frequency Dynamic Airframe Loads and Stress Prediction. 62nd Annual Forum of the American Helicopter Society, Phoenix, AZ
7. Möller, P.W. (1999). Load Identification through Structural Modification. Transactions of the ASME, Vol. 66, pp. 236-241
8. Rhoads, D. (2006). Rotorcraft Airframe Load Spectrum Development. 9th Joint FAA/DOD/NASA Aging Aircraft Conference, Atlanta, GA
9. Strawn, R.C., Nygaard, T., Bhagwat, M.J., Dimanlig, A., Saberi, H., Ormiston, R.A., Potsdam, M. (2007). Integrated Computational Fluid and Structural Dynamics Analyses for Comprehensive Rotorcraft Analysis. AIAA Atmospheric Flight Mechanics Conference and Exhibit, Hilton Head, SC, Paper #2007-6575
10. Thite, A.N., Thompson, D.J. (2006). Selection of Response Measurement Locations to Improve Inverse Force Determination. Journal of Applied Acoustics, Vol. 67, pp. 797-818, doi: 10.1016/j.apacoust.2006.01.001
11. Vishwakarma, R., Turner, D., Lewis, A., Chen, Y., Xu, Y. (2010). The Use of Pseudo-Inverse Methods in Reconstructing Loads on a Missile Structure. The 2010 International Conference on Modelling, Identification, and Control, Okayama, Japan
12. Bousman, W. G. and Kufeld, R. M. (2005). UH-60A Airloads Catalog. NASA TM 2005-212827/AFDD TR-05-003 -
KEYWORDS: Fatigue; Rotorcraft Structures; Physics-based Modeling; Sensors; Maintainability; Service Life Extension
