computational aero elas ti city -...

The recent push towa rds more f uel-ef f ic ient a nd environmentally friendly aircraft is causing a dra-

m a t i c s h i f t i n t h e d e s i g n o f t h e next-generation of aircraft. With surg-ing fuel prices and more and more people taking to the skies, aircraft companies h ave be e n forc e d to re t h i n k t he i r approach to future designs, keeping in mind the goal of reducing fuel consump-tion by up to 50% by 2020 [1]. To achieve this, increasingly lighter and more flex-ible composite structures are being introduced, and innovative, unconven-t ion a l de s i g n s re s u lt i n g i n h i g he r lift-to-drag ratios are on the table. With such radical changes to the structure, propulsion and the aerodynamic shape of aircraft, the use of high-fidelity com-putational aeroelasticity (CAe) early in the design process will be critical to meet tomorrow’s design challenges.

THE CRITICAL ROLE OF HIGH-FIDELITY COMPUTATIONAL AEROELASTICITYAvoiding negative aeroelastic impacts while at the same time exploiting the benefits of aeroelasticity will be critical

d Meeting air-worthiness requirements: The aerospace industry has entered uncharted territory with the introduction of l ight, f lexible composites and with the development of revolutionary concepts such as morph i ng veh icles, joi ned-w i ng a i rcra ft, blended wing-body configurations and innovative unmanned aerial vehicles (e.g. HALE UAV). As a result, companies are no longer able to rely on their historical knowledge accumulated from traditional designs and instead need to gain a solid understanding of a new design’s real-world behavior early on through simulations. One major challenge for the industry is to accurately predict aeroelastic phenomena critical to flight safety, especially in the transonic flight regime (e.g. flutter, buffet and buzz). To take full advantage of lighter designs, it will be of utmost importance to avoid surprises late in the development cycle, as they almost always result in aeroelastic weight penalties and huge safety margins to meet aeroelastic stability air-worthiness requirements.

SABINE A. GOODWIN, CD-adapco

Above: Computational aeroelastic analyses are critical to modern aircraft design due to the increased interdependency between structures and aerodynamics caused by more flexible composite materials.

THE TREND OVER THE NEXT 20 YEARS IS NOT MORE OF THE SAME. IT IS LARGER AIRCRAFT, CLEANER AIRCRAFT, MORE FUEL EFFICIENT AIRCRAFT.

AERO E L A S T I C I T Y :A KEY ENABLING TECHNOLOGY

FOR THE DESIGN OF NEXT-GENERATION AIRCRAFT

- John Leahy, COO Airbus, discussing Airbus Global Market Forecast 2012-2031

COMPUTATIONAL

for improved performance and reduced cost of the future fleet. As the industry takes advantage of increasingly more powerful and lower cost computers, integrating validated high-fidelity CAe methods early in the design process will soon become a necessity for companies to remain competitive.

Above: Flexible wing deflection

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..::FEATURE ARTICLE Aerospace

d Developing and deploying game-changing technologies: As airframes become more and more flexible, they also become increasingly sensitive to dynamic atmospheric disturbances such as tu rbu lence. H igh-fidel ity CAe w i l l become a key enabler in the development of novel active control technologies to l imit a flexible vehicle’s dynamic response to these disturbances and to minimize critical design loads.

Game-changing structures technologies are also opening the door for exploiting the potential benefits of aeroelasticity. Flexible composite materials now allow the designer to introduce directional stiffness to the wing, a nd sma rt struc tu res i n conju nc tion w ith active control can be applied to aeroelastically shape the wing for drag reduction, improved stability, and load alleviation. High-fidelity CAe enables for these innovative technologies to be evaluated up front, and the knowledge acquired through the simulations can then be transferred into the design resulting in a further weight reduction.

CHALLENGES OF HIGH FIDELITY COMPUTATIONAL AEROELASTICITYT he coupl i ng of two d i sti nc t eng i neer i ng disciplines like fluid and structural dynamics is not trivial; they are inherently dissimilar and the computational methods for each have been developed largely independently of each other. Com bi n i ng computationa l f lu id dy na m ics (CFD) and computational structural dynamics (CSD) solvers for simulating non-linear fluid-structure interaction problems demands a carefully designed implementation to ensure robustness, stability, accuracy and efficiency of the resulting CAe capability [2].

d Mapping and data exchange must be robust and efficient: The CFD and CSD grids are often non-conformal, each requiring different grid densities a nd topolog ies. I n add ition, the wetted areas on each model do not always geometrically match, making it challenging to identify the proper interface regions for interpolating data. Typically, the mapping and synch ronization for data exchange in CAe are performed with third-party inter-code communication software or via file transfer,

greatly i ncreasi ng overhead a nd reduci ng efficiency. In addition, if the interpolation of geometrical and loads data is not implemented carefully, an imbalance in the transfer of energy between the models can lead to inaccurate and unstable solutions especially when predicting highly non-linear aeroelastic phenomena such as flutter.

d Coupling strategy affects accuracy, sta-bility and flexibility: The approach to use for coupling the fluid and structure highly depends on the type application and the complexity of the cross-coupling between the disciplines. Coupling methods can be largely divided into two classes: explicit and implicit. A trade-off between f lex i bi l ity a nd acc u racy must be

Figure 1: Experimental set-up Figure 2: Resonance on plate

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Fluid Domain

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Th i s s i m p l e c a s e s t u d y i nves t i gates t he s t ron g t w o - w a y a e r o e l a s t i c coupling between an elastic

plate with two fundamental modes (a 4 Hz 1st bending mode and a 20 Hz 1st twisting mode) and compressible air moving normal to the plate at 10 m/s (Figures 1 and 2). The two-way coupled STAR-CCM+/Abaqus FEA co-simulation was used to perform an aeroelastic analysis and to demonstrate the presence of resonance resulting from damping of the 1st bending mode and excitation of the 1st twisting mode during this experiment.

To validate the use of the STAR-CCM+ SST k- tu rbu lence model for th is application, rigid unsteady Reynolds-Av e r a g e d N a v i e r- S t o k e s ( R A N S) computations were initially performed. Both the computed time-averaged drag coefficient and Strouhal number (a non-dimensional number describing the frequency of vortex shedding) matched we l l w it h pre v iou s ly do c u me nte d

e x pe r i me nta l res u lts , con f i r m i n g that the turbulence model accurately predicts the complex turbulent flow patterns in the wake behind the flat plate.

T he s hedd i n g f req ue nc y of t he vortices in the wake was predicted at 19.5 Hz, nearly identical to the natural frequency of the twisting mode. These results suggest that resonance will likely occur when aeroelastic effects are included in the simulation.

Du ri ng the two-way aeroelastic computation with STAR-CCM+/Abaqus FEA, the elastic deformation of the plate was initially as expected: bending in the direction of the wind. As the solution progressed in time, the bending mode of the structure was damped out by the air flow and the plate began to twist as the periodic driving force of the vortex shedd i ng exc ited the 1st tw i sti ng mode. The simulation was successful in predicting the resonance expected to occur during this experiment.

PLATE VORTEX INDUCED VIBRATION

CASE STUDY

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FEATURE AEROSPACE

Above: Streamlines from a MQ-1 Predator. The aerospace industry has entered uncharted territory with the introduction of revolutionary concepts such as innovative unmanned aerial vehicles. CAe is a key enabling technology to accurately predict the aeroelastic phenomena critical to flight safety of these unconventional designs.

FLUTTER OF THE AGARD WING 445.6

CASE STUDY

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Figure 3: Surface mesh on Wing 445.6

Figure 4: Flutter boundary (Wing 445.6)

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The AGARD Wing 445.6 is a stan-dard aeroelastic configuration s p e c i f i c a l l y d e s i g n e d f o r dynamic aeroelastic response.

This geometry was extensively tested in the 16-ft Transonic Dynamics Tunnel at the NASA Langley Research Center and resulting data has been widely used for verification of vari-ous CAe codes for the past 20 years [3, 4]. This study was performed to validate the two-way coupled STAR-CCM+/Abaqus FEA co-simula-

tion for flutter prediction.Using the polyhedral meshing capability in STAR-CCM+, a fine viscous mesh with prisms in the boundary layer was generated, su f f ic ient ly ref i ned to en su re acc u rate capture of the shock locations at transonic conditions. The surface mesh is depicted in Figure 3.

To compute the f lutter bou nda ry, the motion of the wing was initiated by applying an impulse load to the structure, and unsteady RANS time-marching calculations using the two-way coupled STAR-CCM+/Abacus FEA co-simulation were performed.

The computed f lutter cha racteristics, represented in terms of the flutter velocity index (FVI), are depicted in Figure 4. Results demonstrate that the method nicely captures the experimental flutter boundary including the significant drop in the flutter speed at transonic conditions (also called the flutter dip). Similar results were observed when

analyzing the f lutter frequencies. W hen comparing these results to published results of other codes [2], the located bou nda ry is inside the range of the published data spread with an error of less than 15%. This is considered good from an engineering point of view and validates the ability of the CAe capability to accurately predict flutter.

considered when deciding on a strategy.With explicit coupling, the effects of

moving the fluid mesh lags the solution by one time step. This coupling approach results in greater flexibility for the user but can be unstable and less accurate, especially in applications when light and compliant structures interact with heavy fluids.

Fully implicit coupling is the most rigorous and robust approach because it solves a full system of cross-coupled fluid and solids equations. However, this coupling requ ires a much more inti-mate integration of the solvers, and it becomes increasingly difficult to imple-ment as more complicated physics are introduced into the system.

d Dynamic mesh evolution is cum-bersome and costly: One of the key difficulties in CAe is that the physical motion of the geometry calls for a capa-bility to move all nodes in the CFD grid at every time step, a costly task when run-ning unsteady simulations on complex geometries. In addition, it is impera-tive that the mesh movement does not introduce skewed cells and grid density changes as this can have a negative effect on the convergence and accuracy

GLOBAL VIGILANCE, REACH, AND POWER AT HOME AND ABROAD REQUIRE VAST AMOUNTS OF ENERGY - WHETHER IT IS FUEL FOR OUR AIRCRAFT, GAS FOR OUR VEHICLES, OR ELECTRICITY FOR OUR SPACE AND CYBERSPACE EFFORTS. AS THE LARGEST ENERGY USER IN THE FEDERAL GOVERNMENT, THE AIR FORCE MUST FIND WAYS TO REDUCE OUR ENERGY CONSUMPTION, ESPECIALLY GIVEN THE CURRENT ECONOMIC ENVIRONMENT. - Secretary of the Air Force Michael Donley

of the solution. With so many obstacles, dynamic mesh evolution is one of the most difficult problems to handle, often leading to costly and time consuming road blocks where the user needs to step in to manually fix or regenerate the mesh.

d Ease-of-use and automation are imperative: Up until now, high-fidelity CAe has been mostly a research exer-cise, and running the simulation has requ i red a great dea l of specia l i zed knowledge and training. This has often be e n a s how s toppe r whe n compa-nies are considering integrating the capability into the engineering design process. In today’s fast-paced produc-tion environment, companies demand ease-of-use and automation, so they can focus on results rather than on fig-uring out how to set up, prepare, and run the simulations.

STAR-CCM+ PROVIDES THE SOLUTIONS STAR-CCM+, CD-adapco’s flagship software, offers practical solutions for many of the challenges encountered when tackling highly non-linear fluid-structure interaction problems such as aerodynamic flutter and buffet.

d Built-in mapping and co-simula-tion coupling are robust and efficient: STAR-CCM+ has a direct link to Abaqus finite element analysis (FEA) through a co-simulation application programming interface (API) developed by SIMULIA, delivering a fu lly coupled, two-way, fluid-structure interaction. Direct co-simulation coupling provides efficiency and reduced overhead associated with data transfer through file exchanges and use of external middleware soft-wa re. A s d ata i s pa ssed bac k a nd forth via sockets, the API manages all exchange synchronization (how often data is passed back and forth) while both codes are running in memory. In addition, the STAR-CCM+/Abaqus co-simulation gives the user the flexibility to choose between explicit or implicit coupling, depending on the application.

The built-in mapping implemented in STAR-CCM+ is robust and accurate and efficiently handles non-conformal meshes with no need for writing scripts a nd i nput f i les. Mappi ng is done i n a distributed manner (local on each p ro c e s s or) e n s u r i n g t h at t he re i s enough memory available to reliably handle the most complex geometries.

d Java automation results in flexibility and customization: In addition to direct

Below: State-of-the-art meshing in STAR-CCM+


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Figure 8: Phase from prescribed motion

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STATIC & DYNAMIC COMPUTATIONS ON THE HIRENASD WING

comparisons with experimental data [6].

The wing aeroelastic equilibrium shape and pressu re coef f ic ients (C p) were computed for va rious a ngles of attack ( ) usi ng the two-way coupled STAR-CCM+/Abaqus FEA co-simulation. Figure 6 depicts chord-wise Cp distributions on an outboard section of the wing (M=0.8, =2°), dramatically showing the effect the deformation of the structure has on the loads and indicating a good comparison with experiments. Similar results were obtained when looking at the span-wise displacements and lift/drag distributions vs. . [7]

The accuracy of STAR-CCM+ was further validated using the one-way coupled prescribed motion technique. In this approach, the second eigenmode was extracted from Abaqus and used to prescribe a harmonically-varying grid motion about the wing aeroelastic equilibrium. The geometry was moved using mesh morphing in STAR-CCM+. Several periods of prescribed vibration were computed, aimed at verifying S T A R - C C M + ' s a c c u r a c y c o m p a r e d t o ex per i ments a nd other si mu lation codes. Figures 7 and 8 depict the magnitude and phase of the Fourier transforms of Cp on the upper surface on an outboard station of the wing (M=0.8, =-1.34°), showing good comparison

to experimental values. Similar results were obtained on the lower surface and on span stations further inboard on the wing. Although not shown, these results also compare well to results of other CAe codes [8]. Free and forced wind-on vibrations using 2-way coupling with STAR-CCM+/Abaqus FEA co-simulation were also performed and results compared well to the published experimental results [9].

Th is i nvestigation con fi rms that the S TA R- C C M +/A b a q u s F E A c a p a b i l i t y accurately predicts both static and dynamic aeroelastic effects at transonic conditions and realistic flight Reynolds numbers.

GOAL FOR SUSTAINING OUR FUTURE: TO DEVELOP AND OPERATE AN AVIATION SYSTEM THAT REDUCES AVIATION’S ENVIRONMENTAL AND ENERGY IMPACTS TO A LEVEL THAT DOES NOT CONSTRAIN GROWTH AND IS A MODEL FOR SUSTAINABILITY. - FAA Strategic Plan - Destination 2025

Figure 5: HIRENASD surface mesh

Figure 6: Static aeroelastic solution

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ExperimentsSTAR-CCM+ rigidSTAR-CCM+ static aeroelastic

CASE STUDY

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Figure 7: Magnitude from prescribed motion

For this validation study, static and dynamic aeroelastic com-putations were performed on the H igh Rey nolds Nu m ber

Aeros t r uc tu ra l D y n a m ic s (H I R E NA SD) wing (Figure 5). The wing was originally tested in the European Transonic Wind Tunnel [5] and offers both static and dynam-ic measurements at transonic conditions with realistic flight Reynolds numbers. This work was pa rt of the f i rst Aeroelastic Prediction Workshop.

To ensure CFD solution accuracy, a grid density study was performed on the rigid wing at transonic conditions. As the mesh was refined, lift and drag were compared w it h pre v iou s ly pu bl i s hed r i g id body computational data and a drag converged mesh was identified for this validation study [6].

To v a l i d a t e t h e F E A m o d e l , a n eigenfrequency extraction analysis was performed i n A baqus resu lti ng i n good


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c o - s i m u l a t i o n c o u p l i n g w i t h Abaqus, STAR-CCM+ enables CAe computation s th roug h i mpor t-i n g/e x por t i n g CSD mes hes i n other native formats (e.g. Nastran, Ansys) as it leverages the power of Java to give users the ability to customize every step of the simu-lation workf low. A lthough th is feature requires work up front to set up and manage the simulation, the pay-off is greater flexibility to use legacy codes and simplified CSD models.

F o r e x a m p l e , w i t h t h i s approach, it is possible to perform a n aeroelastic a na lysis on the rotating blades of a helicopter by using high-fidelity CFD in STAR-CCM+ in conjunction with a simple beam-rod approach for modeling structural deformation and pitch of the blades.

d State-of-the-art meshing and morphing reduces turnaround time: The viability of deploying a CAe simulation in a production env i ron ment strongly depends on the ability to quickly generate h i g h - q u a l i t y c o m p u t a t i o n a l meshes. With unrivaled polyhedral a n d t r i m m e d c e l l m e s h i n g , S TA R - C C M + c u t s g e o m e t r y prepa ration a nd mesh i ng ti me o n v e r y c o m p l e x ge o m e t r i e s dow n from months to hours. In

add it ion , t he mu lt i- q u ad rat ic morphing capability robustly and smooth ly moves these meshes (of a ny topology) based on the deformation it receives from the CSD solver. T he resu lt i ng C F D mesh conforms to the shape of the deflected structure, and the redistribution of the mesh vertices nicely preserves the quality of the original mesh.

In addition to a superior mor-ph i ng tec h nology, STA R-CC M+ also has an overset mesh capabil-ity. When using overset meshes, in the case of flow around bodies at various relative positions, one needs to generate individual grids only once and then compute the f low for ma ny com bi nations or relative positions and orientations by simply moving grids, with no need to re-mesh or change bound-ary conditions. This capability is a key enabler for applications in aero-servo-elasticity such as gust load alleviation, where it is vital to model the control surfaces deflec-tions as part of the simulation.

d Intuitive user simulation envi-ronment with high-fidelity physics delivers engineering solutions: STAR-CCM+ drives innovation as it seamlessly fits into any exist-ing engineering process and gives the designer the power to handle

the most complex multi-physics problems with ease. In addition to performing high-fidelity CAe simulations, with the same code, the user can easily include tem-perature effects, aero-acoustics, 6 deg re e s- of-f re e dom (6 D OF) and other high-fidelity physics in a fully coupled manner. The net result is more time analyzing data and less time preparing and set-ting up simulations.

CONCLUSION I n today’s competitive c l i mate, driven by climbing fuel costs and increasing demand for air travel, high-fidelity CAe is a key enabling technology for aerospace compa-nies to develop innovative mini-mum structural weight designs while meeting the tight schedule a nd cost constra i nts of a ty pi-cal production environment. It is imperative that the CAe capabil-ity is accurate, robust and efficient and that it easily fits into the cur-rent engineering design process-es, producing high-quality results with minimum user efforts. With its unrivaled meshing technology, h igh-fidelity physics, intu itive user environment and direct link to Abaqus FEA for co-simulation, S TA R- C C M+ s e a m l e s s l y i nte -grates CAe into the design process, driving innovation and resulting in engineering success.

REFERENCES

01. NASA Facts NF-2010-07-500-HQ

02. Schuster, D., Liu, D. and Huttsell, L., “Computational Aeroelasticity: Success, Progress and Challenge”, AIAA -2003-1725

03. Yates, E.C., “AGARD Standard Aeroelastic Configurations for Dynamic Response. Candidate Configuration I. – Wing 445.6”, NASA TM 100492, Aug. 1987

04. Yates, E.C., Land, N.S., and Foughner, J.T., “Measured and Calculated Subsonic and Transonic Flutter Characteristics of a 45 Degree Sweptback Wing Platform in Air and Freaon-12 in the Langley Transonic Dynamics Tunnel”, NASA TN D-1616, March 1963

05. Ballmann, J. et al., “Aero-Structural Wind Tunnel Experiments with Elastic Wing Models at High Reynolds Numbers (HIRENASD-ASDMAD)”, AIAA -2011-0882, January 2011

06. Florance, J. Chwalowski, P. and Wieseman, C., “Aeroelasticity Benchmark Assessment”, Aeroelasticity Branch, NASA Langley Research Center, Subsonic Fixed Wing Program, Interim Report, March 2010

07. Heeg, J., Florance, J., Chwalowski, P., Perry, B. and Wieseman, C., “Information Package: Workshop on Aeroelastic Prediction”, Aeroelasticity Branch, NASA Hampton, Virginia, October 2010

08. Schuster, D., Chwalowski, P., Heeg, J., and Wieseman, C. “Summary of Data and Findings from the First Aeroelastic Prediction Workshop”, ICCFD7-3101

09. Ballmann, J. et al., “Experimental Analysis of High Reynolds Number Aero-Structural Dynamics in ETW”, AIAA 2008-841, Presented at the 46th AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV, January 7-10, 2008

Above: Integration of computational aeroelasticity into the design process of unmanned aerial vehicles drives innovation, avoids surprises late in the development cycle and results in engineering success (University of Washington).


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