CFD-Based Flutter Boundaries of the Common Research ModelIvan Figueroa – CSU Long Beach / Mentor: Bret Stanford – LaRC AB

AbstractThe focus of NASA’s AATT program is to design technologies that will result infuel-efficient, quiet, and low-emissions aircraft. To aid in this effort, NASA hasutilized the Common Research Model, a computational model of a generictransport aircraft, in order to test various aeroelastic models. Important incharacterizing this model is determining the onset of wing flutter, a dynamicinstability that can quickly destroy a wing structure. To accomplish this,dynamic aeroelastic simulations were run at Mach numbers ranging from 0.5 to0.9, and various dynamic pressures along the flight envelope using NASA’s CFDtool, FUN3D. The resulting flutter boundaries found in this effort should providea link to bridge the knowledge gap between the higher and existing lowerfidelity aeroelastic models built upon industry-standard linear panel methods.

References• AATT program: http://www.aeronautics.nasa.gov/aavp/aatt/index.html• Vassberg, John; et al. , “Development of a Common Research Model for Applied CFD Validation Studies”,

AIAA, 26th AIAA Applied Aerodynamics Conference, 2008.• Bob Biedron [pdf], FUN3D v12.4 Training Session 15: Aeroelastic Simulations , retrieved from

http://fun3d.larc.nasa.gov/session15_2014.pdf

Goals in conducting NASA effort• Bridge the knowledge gap between high fidelity models used in academia

and lower fidelity models used in industry.• Such a model could be used in the design of optimized high aspect ratio

transport aircraft.

Aeroelastic analysis approach• Use Common Research Model (CRM): a generic transport aircraft

computational model• Consists of computational mesh and structural mode shapes, see fig 3

and 4. • Conduct an CFD aeroelastic analysis of the CRM using the FUN3D CFD

solver, using structural modal data computed in NASTRAN.• Run simulations at various Mach numbers from 0 to 1, and at

differing dynamic pressures to map flutter boundaries.• Compare the flutter mapping with other models and a typical flight

envelope.• Look into how angle of attack affects flutter onset.

Results of analysis

• In fig 8 we see that the FUN3D simulations roughly match the trend expected to be encountered, particularly the evidence of a transonic “dip”, where flutter boundaries drop below those values predicted by lower fidelity models in the transonic regime.

• The FUN3D results also generally follow the trend set by the linear aeroelastic model in NASTRAN, for subsonic Mach numbers.

• The results obtained are also outside the boundaries of the flight envelope, even taking into account a 15% flutter margin, meaning the aircraft should be flutter-free.

• From fig 7 we can see that damping decreases as points get nearer to flutter, the curve has a parabola-like trend.

• Since it is uncoupled, mode 2 nominally vibrates at a higher frequency than mode 1.

• During flutter, however, all modes vibrate at the flutter frequency, which is around 3.55 Hz, and this was seen across the Mach number range tested, see fig 9.

• When increasing the angle of attack, the flutter point for a given Mach number shifts upwards, as shown by fig 8.

• This shift is more prominent in the transonic region, where the supersonic flow bubble and normal shock over the wing are more pronounced by the change in angle, which could be an explanation for this trend, see fig 10 and 11.

Future Work• Account for non-aerodynamic loads during the static and dynamic simulations, such as

self-weight, fuel weight, engine thrust, etc.• Utilize a trimming module in FUN3D, to automatically find the trimmed AOA during

steady simulations.

AcknowledgementsI would like to thank my NASA mentor, Bret Stanford for guidance in this project. I would also like to thank Steve Massey and Pawel Chwalowski for their troubleshooting expertise in FUN3D. In addition, I would like to thank the NASA AATT program for providing the

funding to conduct this project.

Optimized wings and flutter• NASA’s AATT program is seeking new technologies to improve

performance metrics such as noise, emissions, and fuel burn.• Computational design and optimization of high aspect ratio wings

can help obtain these goals.• These wings can improve aerodynamic performance and reduce

overall structural mass.• Optimized high aspect ratio wings have to overcome challenges like

maintaining structural integrity and being more susceptible to aeroelastic effects like wing flutter.

• Wing flutter is an unstable oscillation that can lead to wing failure. • Flutter can be determined through wind tunnel testing or aeroelastic

modeling.• Models range from relatively low fidelity models like double lattice

panel methods to high fidelity Navier-Stokes solvers. • Models like the double lattice method cannot take into account

important transonic effects due to shockwaves, see fig 1. • It is important to properly predict flutter in the transonic regime since

transport aircraft typically operate within this range of Mach numbers.

Fig 1. The transonic dip [1]

CFD Procedure

• Find the flutter point by making an initial guess for the dynamic pressure, then from these results calculate the logarithmic decrement (damping) of the modal response.

• At the onset of flutter the modal response oscillations will diverge instead of damp out.

• Decrement should thus decrease as one gets closer to the flutter point, see fig 5.

• The search will be an iterative process to bound the location of flutter and narrow that boundary.

Rigid Steady-State

• Obtain converged flow to initialize flexible simulations

Static Aeroelastic

• Obtain a steady state response for the wing’s structural modes in response to steady air loads

Dynamic Aeroelastic

• Apply a perturbation to determine modal response characteristics

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Fig 2. Lift decrease due to aeroelastic deformation

Fig 5. Progression of response to flutter conditions

Fig 3. View of mesh and 1st structural mode

Fig 10. Growth of the supersonic flow bubble with increase in alpha.

Fig 6. View of surface pressure distributions and wing deformation

Fig 4. View of mesh and 8th structural mode

[1] http://elib.dlr.de/70263/1/R._Vo%C3%9F_%2B_L._Tichy_%2B_R._Thormann.pdf

Fig 11. Strengthening of the upper wing normal shock

Fig 7. Response damping

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Fig 8. Flutter point mapping within flight envelope

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