Validation of Rotorcraft Comprehensive Analysis Performance Predictions for Coaxial Rotors in Hover Jimmy C. Ho Hyeonsoo Yeo Mahendra Bhagwat Research Scientist Research Scientist Research Scientist Science and Technology Corporation U. S. Army Aviation Development Directorate – AFDD Ames Research Center Aviation & Missile Research, Development & Engineering Center Moffett Field, California Research, Development and Engineering Command (RDECOM) Ames Research Center, Moffett Field, California ABSTRACT Comparisons of rotor aerodynamic performance parameters in hover, between rotorcraft comprehensive analysis pre- dictions using a free vortex wake model and measured data, for two model–scale and two full–scale coaxial rotors are provided. Predictions from a computational fluid dynamics analysis are also included for one of the full–scale rotors to provide additional insights. Test points evaluated form sweeps in both the coaxial rotor system thrust and the axial separation distance between the two rotors. The comprehensive analysis predictions are mostly in good agreement with measured data, which includes reflecting the same trends from all sweeps. While the comprehensive analysis predictions are mostly in good agreement with measured data and computational fluid dynamics predictions for individual rotor thrust, good agreement for individual rotor torque is more elusive. The comprehensive analysis predictions also show that even though the coaxial rotor figure of merit is not strongly dependent on the thrust sharing ratio between the two rotors, the figure of merit of the two individual rotors are highly sensitive to the thrust sharing ratio. NOTATION INTRODUCTION cd = airfoil drag coefficient In the United States, there is renewed interest in coaxial rotor cℓ = airfoil lift coefficient helicopters especially for high–speed applications. Sikorsky cm = airfoil pitching moment coefficient completed flight testing its X2 Demonstrator in July 2011 and CP,ideal = ideal rotor power coefficient is expected to begin flight testing its S–97 Raider this year. CT = rotor thrust coefficient Sikorsky and Boeing are partners on the U. S. Army’s Joint CT,L = rotor thrust coefficient of lower rotor Multi–Role Technology Demonstrator program in which they CT,U = rotor thrust coefficient of upper rotor are expected to fly their SB>1 Defiant design by 2017. In CQ = rotor torque coefficient Russia, Kamov continues to develop a navalised derivative CQ,L = rotor torque coefficient of lower rotor of its Ka–52 while Rumas completed first flight of its Ru- CQ,U = rotor torque coefficient of upper rotor mas 10 in September 2014. These recent activities involving D = rotordiameter the development of coaxial rotor helicopters indicate a strong FM = rotorfigureofmerit need for computational tools to accurately predict rotor aero- h = axial separation distance between rotors dynamic performance for coaxial rotors. The accuracy of a KD = Reynolds number correction factor for drag computationaltool needs to be validated by comparing its pre- M = Machnumber dictions against experimentally measured data. n = exponent in Reynolds number corrrection Coleman wrote a survey (Ref. 1) of experimental and an- Re = Reynoldsnumber alytical research, up to 1997, on the aerodynamics of coaxial Re = Reynoldsnumberof the C81 tables C81 rotors. The survey is excellent in describing the influence of V = rotortipspeed tip the primary factors, such as axial separation between the two α = angle–of–attack rotors, that determine coaxial rotor performance. σ = rotor solidity Comparisons of coaxial rotor performance, between pre- Presented at the AHS 71st Annual Forum, Virginia Beach, dictions and measured data, do exist for some modern com- Virginia, May 5–7, 2015. This is a work of the U. S. Gov- putational tools. Wachspress and Quackenbush (Ref. 2) pro- ernment and is not subject to copyright protection in the U. S. vided validations of the aerodynamics tool CHARM for sev- DISTRIBUTION STATEMENT A. Approved for public re- eral coaxial rotors. They included comparisons, between lease; distribution is unlimited. PR 1635 March 23, 2015 CHARM predictions and measured data, of rotor performance 1 and/or tip vortex position for the isolated coaxial rotors tested The next section is a description of the analytical mod- by Nagashima et al. (Refs. 3–5) and Andrew (Ref. 6), as eling used in the RCAS analysis. This is followed by vali- well as for the Kamov Ka–32 (Ref. 7) in flight. Lim et dations of RCAS performance predictions for coaxial rotors al. (Ref. 8) provided validations of the comprehensive analy- in hover. Most of the validations are comparisons between sis tool CAMRAD II for several coaxial rotors in hover. They RCAS predictions and measured data of model–scale rotors included comparisons, between CAMRAD II predictions and from Ramasamy (Ref. 18). Comparisons between RCAS pre- measured data, of rotor performance for the isolated coax- dictions and measured data of full–scale rotors from Harring- ial rotors tested by Harrington (Ref. 9) and McAlister and ton (Ref. 9) are also shown. Where available, OVERTURNS Tung (Ref. 10), as well as for the Sikorsky XH–59A (Ref. 11) predictions from Ref. 16 are also included to provide addi- in flight. Johnson (Ref. 12) provided validationsof CAMRAD tional insights. Following the validations is a section that II for coaxial rotors in forward flight. He included compar- compares rotor performance between single and coaxial ro- isons, between CAMRAD II predictions and measured data, tors. of rotor performance for the isolated coaxial rotor tested by Dingeldein (Ref. 13) as well as for the XH–59A (Ref. 14) in ANALYTICAL MODELING flight. Ruzicka and Strawn (Ref. 15) provided validations of the Reynolds–average Navier–Stokes (RANS) solver OVER- RCAS allows for numerous options in modeling the vortex FLOW 2 for the isolated coaxial rotor tested by McAlister and wake system of a rotor, the aerodynamic interference between Tung (Ref. 10) by showing comparisons, between predictions rotors, the airloads acting at a blade section, the dynamics of and measured data, of rotor performance. Lakshminarayan a multibody system, and the trim conditions of rotors. This and Baeder (Ref. 16) provided validations of the RANS solver section is restricted to only describing the models used in this OVERTURNS for “Rotor 2” of the isolated coaxial rotors paper. For interested readers, Ref. 22 describes the various tested by Harrington (Ref. 9) by showing comparisons, be- modeling options available in RCAS. tween predictions and measured data, of rotor performance. Rajmohan et al. (Ref. 17) provided validations of the vortex Vortex Wake Model particle method (VPM) as well as of a hybrid VPM solution, in which VPM is coupled with the computationalfluid dynam- The rotor blades are modeled as lifting lines, i. e., as bound ics (CFD) code OpenFOAM, for the coaxial rotor that was vortices located along the blade quarter chord lines. Each lift- tested by McAlister and Tung (Ref. 10). They compared VPM ing line is discretized into a series of spanwise aerodynamic predictions against measured data for both rotor performance segments, or “aerosegments.” The wake behind the blade is and velocity in the flow field. comprised of vortices trailing from the edge of each of the In addition to the experiments already mentioned, a recent aerosegments. Shed vortices can also be optionally included experiment by Ramasamy (Ref. 18) is especially useful for to model the effects of azimuthally varying blade circula- validating the aerodynamic performance predictions of coax- tion distribution. A small azimuthal region behind the blade ial rotors. This experiment included measurements of rotor called the “near–wake” includes all of these individual vor- performance for each individual rotor. It also included varia- tices over the entire blade span. For numerical efficiency, this tions in the axial separation h/D between the two rotors to an near–wake extends only a small number of azimuthal steps extent that is greater than previousexperiments. In this experi- (wake age) behind the blade, after which a simpler “far–wake” ment, Ramasamy tested model–scale rotors in both single and model is used. The far–wake is comprised of a discrete tip coaxial rotor configurations in hover. Two sets of rotor blades vortex, a root vortex, and a large–core vortex representing the were tested. One set used untwisted blades and the other set inboard wake sheet trailing from the entire blade span. The used highly twisted blades. Note that the highly twisted blades tip vortex strength and the relative strengths, between the root used are the same blades used in the experiment by McAlis- vortex and the sheet vortices, are all determined from the ac- ter and Tung (Ref. 10), but the rotor hubs from McAlister and tual loading distribution on the blade. An implementation of Tung were not used by Ramasamy. The experiment by McAl- the University of Maryland free vortex wake (Ref. 23) model ister and Tung featured significant blade coning, which were is used to allow the tip vortex to freely deform. The extent negligible in the experiment by Ramasamy due to the hub re- of the near–wake and far–wake are set to a wake age of 15◦ placements. and 20 revolutions, respectively. This same discretized wake The goal of this paper is to present validations of the per- model was consistently used for RCAS hover calculations of formance predictions of the Rotorcraft Comprehensive Anal- isolated rotors presented earlier by Jain et al. (Ref. 21). ysis System (RCAS, Refs. 19, 20) for coaxial rotors in hover. This is a naturalfollow–up to the work by Jain et al. (Ref. 21), Aerodynamic Interference Model who provided validations of RCAS performance predictions using vortex wake models for several isolated single rotors in In RCAS, the aerodynamic interference between two rotors hover and in forward flight. A free vortex wake model is used is modularly included with several modeling options ranging to allow the wake geometry to deform accordingly. Usage of from a simple cylindrical vortex sheet to a free vortex wake a vortex wake model inherently allows it to calculate the in- model.