Patuxent River, MD 20670. ABSTRACT ... 2012 by the American Helicopter Society International, ... This ARLP system exploits the University of Maryland. Advanced .... 500. 1000. 1500. 2000. Azimuth ft-lbs . Flight. Case 1. 0. 90. 180. 270. 360.
Rotor Load Prediction using Coupled Rotor/Fuselage Model and Sensor Data Abhishek Abhishek1, Inderjit Chopra2 1 Assistant Research Scientist, 2 Professor – Aerospace Engineering University of Maryland College Park, Maryland
Ashish Purekar Program Manager Techno-Sciences, Inc. Beltsville, MD 20705
Gang Wang Assistant Professor University of Alabama in Huntsville Huntsville, AL 35899
Nam Phan3, Roberto Semidey4, Daniel Liebshutz4 3 Branch Head, 4Engineer Structures Division - Air 4.3.3.2 Naval Air Systems Command Patuxent River, MD 20670 ABSTRACT
A combined analytical and experimental methodology for loads estimation of rotor and dynamic components using sensor inputs is described based on a set of core components. A comprehensive physics-based rotor analysis tool is used to determine blade loads with the capability of using selected sensor measurements from flight test data. Improved airload and structural load predictions were achieved and validated by the UH-60A steady state and maneuver flight test data. Then, the development of a coupled rotor/fuselage analysis scheme is presented and compared with acceleration measurements from flight. Finally, a flexible fuselage model is developed and validated by measurements from ground shake test data. The integration of these components provides the basis for loads estimation on rotating components. Further simulations and verifications are expected to build a comprehensive load prediction database. The ultimate goal is to develop a load derivation model to estimate loads in rotor and dynamic components based on the fixed-frame measurements such as acceleration data. These accurate load predictions in rotor and dynamic components will significantly benefit the structural health monitoring and remaining life prediction of these components.
INTRODUCTION Rotors and their associated dynamic components operate in high-cycle and environmentally challenging conditions. Driving factors that result in rotor system maintenance actions are: fatigue in the rotor hub dynamic components (spherical bearings, bushings, pushrods, root end couplings, etc); operational impact damage (ballistic and FOD) in the rotor blade; and out-of-track and/or out-ofbalance rotors. Applied research is needed to develop and refine technologies that manage and mitigate these maintenance drivers through prognostics. Maley et. al provided an overview of the Navy’s structural health and usage monitoring (SHUM) practices for rotary wing aircraft and their dynamic components [1]. Currently, the Navy is working aggressively w ith the V-22 and CH-53E aircraft and the MH-60R/S and H-1Y/Z to implement SHUM features. In order to achieve the goal of SHUM for rotorcraft, the accurate prediction of loads/stresses in rotor and dy namic components is a key to such an endeavor.
Presented at the American Helicopter Society 68th Annual Forum, Ft. Worth, Texas, May 1-3, 2012. Copyright © 2012 by the American Helicopter Society International, Inc. All rights reserved.
The objective of this research is to develop an innovative use of combined analytical modeling and actual aircraft measurements based on selective onboard sensors to dramatically improve the accuracy of predictions for rotor loads and stresses in dynamic components on in-service rotorcraft. Such accurate load/stress predictions in rotor and dynamic components will benefit the Navy SHUM program [1]. An Advanced Rotorcraft Load Prediction (ARLP) system was developed in our previous work for rotor and dynamic components that features the combined analytical and experimental approaches [2], in which the fuselage was assumed rigid and not coupled with rotor system. The airloads are derived based on a few blade strain sensor measurements in conjunction with the refined lifting-line aerodynamic model. The modal components of the deformation geometry are estimated using blade strain measurements in the rotating frame. Based on the updated blade deflection inputs, the airloads are calculated, which are then used for the prediction of rotor load/stress. Systematic loads prediction validations were conducted using the newly developed airloads derivation approach for the Black Hawk (UH-60A) in steady level and unsteady maneuver flight conditions [2, 3]. A physics based methodology for loads prediction based on sensor measurements can be described using the graphic in Figure 1. A comprehensive rotor analysis and suitable
fuselage model are coupled together to provide an accurate description of the dynamics of the vehicle. Selective measurements on the rotor and fuselage serve as inputs to the model with the results being loads on rotating components and the fuselage response. Rotor Measurements
Comprehensive Rotor Analysis
Rotating Component Loads Estimation
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Section loads were determined based on deformations determined from the sensor data of the three cases. The normal force sections loads from 67.5%R to 99%R are shown in for Case 1 in Figure 3a and the corresponding pitching moment loads are shown in Figure 3b. The results for Case 2 and Case 3 are shown in Figure 4 and Figure 5. The normal force components show generally good agreement with flight test data though the pitching moments are less satisfactory.
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This paper provides a description of the components of the loads prediction methodology which includes the development of the rotor system comprehensive analysis using selected measurements, the development of a flexible fuselage model, and coupling between rotor and fuselage. ROTOR MODEL This ARLP system exploits the University of Maryland Advanced Rotorcraft Code (UMARC) [4] with updated features of the refined lifting-line aerodynamics, the CFD/CSD coupled model, multibody dynamic models and swashplate dynamics. Figure 2 shows a multi-body dynamic model of the UH-60A rotor system, in which the rotor blade is idealized as a beam undergoing flag, lag, and torsion motions.
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Figure 1: Loads prediction
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Case 1: Root flap, pitch, and lag angles; bending moments at 30%, 50%, 70%, and 90%R, torsion moment at 30% and 70%R, and lag moment at 30%R Case 2: Same as Case 1 with the root flap angle determined by the pitch link load. Case 3: Same as Case 1 with the root flap angle determined by the swashplate servo load.
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(b) Pitching Moment Combined analytical modeling with aircraft measurements were conducted with sensor data from the C8534 flight test case (Cw/σ = 0.0783, µ=0.368) from UH60 Airloads program. Three sets of sensor inputs were used for loads derivation:
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The internal moments in the blade (flap bending, edgewise bending, and torsion) were determined for the three cases as well. For Case 1, the bending moment at 50%R is shown in Figure 6a and the torsion moment at 30%R is shown in Figure 6b. Similar results from Cases 2 and 3 are shown in Figure 7 and Figure 8 respectively. There is generally good agreement for both the flap bending and torsion moments.
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In the conventional method of vibration and loads analysis, rotor hub loads are calculated in a hub fixed condition as shown in Figure 9a. Airframe vibration is then calculated in the second step using these vibratory hub loads as a forcing function. Unlike actual flight conditions where the rotor and fuselage are structural coupled, this method eliminates any interaction between airframe vibratory motion and the rotor vibratory loads. A more realistic description of the dynamics would include a coupled system where the airframe motion can have a significant influence on vibratory hub loads due to fuselage feedback effects as shown in Figure 9b.
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airloads for the first steady revolution of the C11029 flight in the UH-60A Airloads program is considered. This flight condition is a 2.1g UTTAS pull-up maneuver. For the results presented, a lifting-line coupled analysis is used. A comparison of the flight test data and predicted translational hub-accelerations using the coupled rotor-fuselage model are shown in Figure 10. The predictions show a phase error of approximately 20o, which may be the direct outcome of identical phase discrepancy in the predicted normal force. This phase discrepancy in negative lift is typical of all lifting-line based analysis and originates from incorrect elastic twist coming from less accurate pitching-moment prediction. It is expected that the use of flight test airload data would correct the phase discrepancy. 0.6 0.4
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A coupled rotor-fuselage model for loads prediction can be developed using either of the following two approaches: (1) blade – fuselage equations formulated together in a coupled aero-elastic manner, (2) blades and fuselage modeled separately with the coupling terms moved to the right hand side of the governing equation.
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The first approach is the appropriate way of coupled rotor-fuselage modeling for steady flight, as the periodicity of the solution can be exploited to solve the governing equations using Finite Element in Time (FET), which is an order of magnitude faster than time marching based solution procedures. This approach is more complicated and does not provide any advantage for transient analysis. Since the analysis of both steady state and transient (e.g. maneuver) flight conditions are of interest, the second approach is explored further in this section.
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In the analysis presented in this section, only the rigid body fuselage is considered in the modeling. The fuselage is effectively modeled using translational and rotational inertias. The next section describes an effort in developing a flexible fuselage model which will be used in future efforts for coupled rotor/fuselage analysis. To understand the effect of fuselage modeling on the blade loads prediction, the
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In the second method, the coupling terms are used forcing for rotor and fuselage subsystems. The blade equations are solved at each time step, the estimated hubloads and the fuselage-blade coupling loads at every time step can be applied on the fuselage model to solve for fuselage motions where the fuselage motions are assumed to be zero to begin with. The calculated fuselage motions are then fed back to blade equations at the next time step via the rotor-fuselage coupling terms which are moved to the right hand side of the equation. The resulting coupled bladefuselage equations are solved in a serial staggered manner.
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Figure 11 show the magnitude and phase of the first 10 harmonics of the translational fuselage accelerations in the three directions, predictions show similar trends as the flight test data for both magnitude and phase. As expected the predictions only have Nb/rev harmonics (4 and 8 for four bladed UH-60A rotor), while the flight test data shows significant non-Nb/rev harmonics. This might be due to dissimilarity of blade as well as fuselage flexibility. The correlation for 4/rev harmonics is expected to improve with the inclusion of flexible modes of fuselage. 150
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prevent a singularity in the system of equations. The displacement at any generic point on the fuselage can be represented by a linear combination of the mode shapes and the normal mode coordinates. Modal superposition can be employed to determine the response of a fuselage with rigidbody modes. qf = qfe + qfr = Φfepfe +Φfrpfr where Φfe represents fuselage elastic mode shapes and Φfr represents fuselage rigid mode shapes. The net fuselage motion is determined by adding together the contributions of flexible and rigid components.
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A flexible fuselage model was developed to generate a computationally efficient representation of the airframe dynamics. A fuselage model was developed by simplifying the structure based on beam elements. A schematic of the UH-60 is shown in Figure 12a with a representative stick model of the airframe structure shown in Figure 12b where the airframe was separated into subsections. The flexible fuselage stick model was based on a high order beam element. The beam element The governing mode shapes used to generate the mass and stiffness matrices of the beam element are based on combined elastic bending and torsion of beams [5]. The beam elements were similar to the beam elements used in the rotor analysis algorithm. The deformation of the beam is split into components for out-ofplane deformation, v and w, in-plane deformation, u, and twist, φ, as shown in Figure 13.
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Figure 11: Hub acceleration harmonics tail cone
FLEXIBLE FUSELAGE MODEL The development of a flexible fuselage model provides a more accurate representation of the coupled rotor/fuselage interactions. The rigid body fuselage used in the previous section provides a first step and the development of a coupled system. An additional benefit of the flexible fuselage development is the ability to track motion in the fixed frame (such as cabin and cockpit accelerations) to use in further validations and loads estimation development. For integration in the coupled model, the fuselage motions are split in to rigid and flexible components to
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(b) Stick model Figure 12: UH-60A Stick Model
Properties of the UH-60 airframe were taken from the NASA Design Analysis Method for Vibrations (DAMVIBS) program. In the DAMVIBS program, shake test data of a stripped down UH-60 airframe [6] was compared to a high fidelity NASTRAN FE model [7]. While there was generally good agreement between test data and the modeling for modal frequencies up to 20 Hz, the results from this comparison indicated the significant challenge in accurately modeling an airframe. A stabilator yaw mode was observed in the shake test and not accurately prediction using the NASTRAN model. Similarly, a fuselage vertical bending mode and fuselage lateral bending mode were predicted from the NASTRAN model and not observed in the shake test data.
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Figure 13: Beam element for stick model
provided in Table 1 for the 9 modes which are below 20 Hz. A comparison between the experimentally determine natural frequencies and the modal frequencies from the NASTRAN and stick modes are shown in Figure 14 for modes up to 20 Hz. Points on the diagonal line indicate good agreement between the experiments and the two models. For the stick model, the two modes show difficulty are the 2nd lateral bending mode and stabilator rigid body mode. A comparison of the first vertical bending modeshapes from the shake test data, NASTRAN predictions, and stick model is shown in Figure 15.
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The goal of the stick model development during the current effort was to accurately model modal frequencies and modeshapes up to 20 Hz. The inertial components of stick model was estimated by first estimating the mass distribution along the fuselage and including concentrated masses for such items as the transmission, engines, and gearboxes. The stiffness distribution was estimated and The modal modified to match the various modes. frequencies to the NASTRAN FE model and stick model are
Figure 14: Comparison of shake test data, NASTRAN FE, and stick model natural frequencies.
Table 1: Natural frequencies of first 9 modes Number
Mode Description
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bending, lateral, 1 bending, vertical, 1 stabilator, RB 1, roll & yaw transmission, pitch transmission, roll bending, vertical, 2 cockpit/cabin, roll bending, lateral, 2 bending, vertical, 3
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Stick Model (Hz) 5.15 7.08
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Figure 15: Modeshape for 1st vertical bending mode
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Figure 16: Modeshape for 1st lateral bending mode
CONCLUSION The components for the load estimation tool have been developed and compared with test data. A comprehensive rotor analysis which includes selected sensor measurements show good agreement with flight test data. A coupled rotor/fuselage model was developed and demonstrated the ability to capture the essential physics and compared favorably with measured accelerations at the fixed frame. A flexible fuselage stick model based was developed based on shake test data for the UH-60A. Modal frequencies and shapes match up well with shake test data and in comparison with a higher fidelity NASTRAN developed models. Further simulations and verifications are expected.
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ACKNOWLEDGEMENTS Research support under the NAVAIR Phase II SBIR contract No. N68335-09-C-0186 (H-53 Heavy Lift Program Office: Dr. Michael Yu) is gratefully acknowledged. REFERENCES 1.
2.
Maley, S., J. Plets, and N. Phan. US Navy Roadmap to Structural Health and Usage Monitoring. 2007. Virginia Beach, VA: American Helicopter Society. Wang, G., et al. Rotor Loads Predictions Using Estimated Modal Participation from Sensors. in
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Proceedings of 29th European Rotorcraft Forum. 2010. Paris, France. UH-60A Airloads Project. [cited; Available from: http://rotorcraft.arc.nasa.gov/research/uh-60.html. Bir, G., I. Chopra, and K. Nguyen. Development of UMARC (University of Maryland Advanced Rotorcraft Code). 1990. Washington, DC, USA: Publ by American Helicopter Soc. Hodges, D.H. and E.H. Dowell, Nonlinear Equations of motion for the Elastic Bending and Torsion of Twisted nonuniform Rotor Blades. 1974, NASA. Howland, G.R., J.A. Durno, and W.J. Twomey, Ground Shake Test of the UH-60A Helicopter Airframe and Comparison with NASTRAN Finite Element Model Predictions. 1990, NASA. Dinyovszky, P. and W.J. Twomey, Plan, Formulate, and Discuss a NASTRAN Finite Element Model of the UH-60A Helicopter Airframe. 1990, NASA.
