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INTRODUCTION

Estimation of helicopter parasitic drag is an important step in the design process that will dictate the p o w e r a n d p r o p u l s i v e f o r c e

requirement at high speeds. The total drag on a helicopter is the sum of the parasitic, frictional and lift-induced drag. Parasitic drag is due to the non-lifting parts, frictional drag is caused by the frictional resistance of the blades and lift-induced drag, as the name implies, is a result of the lift production. In single-rotor helicopters, nearly 33% of the total vehicle drag can be caused by the parasitic drag from the hub. Mini-mum possible drag is a key requirement in any helicopter design and reducing the hub drag plays a major role in achieving this.

One way to reduce hub drag on conventional articulated rotors is to use a fairing, which while minimizing drag, leads to increased mainte-nance and inspection workload. Due to this, alternate methods of reducing hub drag are desirable and one approach is to design the

SIKORSKY AIRCRAFT PREDICTS DRAG OF PRODUCTION ROTOR HUB GEOMETRIES USING CFDALAN EGOLF MIKE DOMBROSKI CD-adapco

Above: Sikorsky S-92 Helicopter

components of the hub such that they gener-ate less drag as a whole when installed in the hub. Traditionally, hub drag estimation involved predicting the drag build-up of the components based on empirical drag data from components of similar or almost similar shapes and sum-ming up their individual contributions. Aside from being based on historical data, this method also involves estimation of interference effects and is less valuable in a production environ-ment where optimization of component shapes is important. Eventually, the rotor hub designed based on this subjective process is tested in a

wind tunnel, leading to an expensive process if design changes and improvements are to be implemented and tested again.

Si korsky A i rcraft set out to explore a n alternate method of predicting hub drag of production geometries based on numerical sim-ulation. This method can provide a reasonable prediction of hub drag for different designs in a short time period, allowing easier optimization of component design in a production environ-ment. This article showcases the application of CD-adapco’s unstructured Navier Stokes solver, STAR-CCM+, to the blind prediction of hub drag on two production rotor hub geom-etries, the S-92A hub and the UH-60A hub.

COMPUTATIONAL GEOMETRYAside from time savings in the design process, the real value of numerical simulation lies in the accuracy of the prediction of hub drag, particu-larly in blind calculations with no knowledge of experimental data. The two rotor hubs in

STAR-CCM+ IS WELL-POISED TO TAKE ON THE CHALLENGE OF PREDICTING THE WAKE STRUCTURE DOWNSTREAM OF THE HUB WITH HIGH FIDELITY.

..::FEATURE ARTICLE Aerospace

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conditions. The surface representation of the S-92A and UH-60A hub are shown in the accompanying image, in addition to the hub/pylon geometry and the com-putational domain.

MESHThe hub geometry was discretized at the surface level using the ‘surface wrapper’ method in STAR-CCM+ before remesh-ing the surface. The surface wrapper shrink wraps a mesh onto the geom-etry and creates a water-tight surface, preserving the geometric fidelity of the surface, including minor details like nuts and bolts. The computational domain i s t he n d i sc ret i z ed u s i n g t r i m med hexahedral cells in the volume, with a prismatic boundary layer mesh near the surface to capture the boundary layer

this analysis, S-92A and UH-60A were tested at a 1/2 size scale in 1994 in the UTRC main wind tunnel as part of the S-92A aircraft development process. Even though data on the drag build-up of individual components was available from this test, the numerical simulations were performed as blind calculations without knowledge of the experimental results. The simulations were carried out including the wind tunnel walls and test pylon/splitter plate assembly, with-out considering the support structure for the assembly. The swash plates in the experiments were non-functional and hence the link between the plates and their servos was removed in both the experimental tests and the simula-tions. The hub was tilted forward by five degrees, while the test pylon/splitter assembly was kept level as per the test

Above: Surface representation of 1/2 scale S-92A hub

SIKORSKY AIRCRAFT

CORPORATIONis a world leader

in the design, manufacture and service of military

and commercial helicopters; fixed-

wing aircraft; spare parts and

maintenance, repair and

overhaul services for helicopters and fixed-wing

aircraft; and civil helicopter

operations.

Out

Splitter Plate Walls

Hub

Inlet

flow. The body-fitted boundary layer mesh had four prismatic cells, with ten layers of cells used on the hub cover to accurately resolve the thick boundary layer on this surface. Focused volumet-ric refinement based on the solution from a coarse grid was used behind the hub to capture the hub wake. A sliding mesh was used around the hub assem-bly which will be rotational. The final volu metric mesh for the S-92A hu b consisted of 14.8M trimmed hexahedral cells, with the prismatic boundary layer mesh accounting for 8.2M cells. Simi-lar process for the UH-60A hub yielded 13.1M advanced hexahedral cells, with 7.1M cells in the boundary layer. Details of the volume mesh are shown in the accompanying images.

SOLUTION METHODOLOGYThe solution methodology was a blind ca lcu lation follow ing the best prac-t ic e s fo r m o v i n g b o d y s i mu l at io n within STAR-CCM+. Initial runs were performed on a coarse grid to obtain an in itial solution that was used for verification of the setup and to identify zones for mesh refinement. The solu-tion process followed the wind tunnel tests in reverse, with a full configuration for the S-92A hub initially, followed by removing the beanie, pushrods, scis-sors & servos, swash plate and bifilar in consecutive runs. Similarly, the UH-60A runs were started with a full configura-tion, followed by removal of bifilar and pitch-link rods in subsequent steps. In

total, there were 6 and 3 configurations each for the S-92A and UH-60A hubs respectively. Steady state simulations were conducted on both hubs with an inlet velocity of 150 knots, hub rota-tional rate of 500 rpm and an advance ratio of 0.36, similar to advance ratios on a full scale rotorcraft. The simulations were run at the same Reynolds number and Mach number as the experiments but at ½ scale values compared to flight

Above: Surface representation of the 1/2 scale UH-60A hub

Above: Wind tunnel model and solution domain

..::FEATURE ARTICLE Aerospace

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..::FEATURE ARTICLE Aerospace

Above: Volume mesh on horizontal plane near UH-60 hub

Above: Volume mesh on vertical plane near S-92A hub

01 Predicted pressure contours on S-92A hub - DES solution. 02 Predicted pressure contours on UH-60A hub - DES solution. 03 Predicted velocity magnitude contours for S-92A hub - DES solution. 04 Predicted velocity magnitude contours for UH-60A hub - DES solution

cond ition s. No g r id sen sitiv ity study was performed.

Steady state runs were con-ducted with the moving reference frame (MRF) approach on coarse grid for the hubs, where the hubs don’t physica l ly rotate but the effect of rotation is included in the flux calculation. The finer mesh runs were started from the steady state solution, using rigid body motion for the rotor hu bs. Two unsteady runs, unsteady RANS (URANS) and detached eddy simu-lation (DES), were performed. The URANS runs were restarted from steady state solution and DES runs restarted from URANS solution with SST (Menter) k- turbulence model. The unsteady runs were performed with a time step size varying from 0.5 to 5 degrees of hub rotation per time step.

RESULTSThe drag from the steady state runs with MRF was equal to the maxi-mum drag predicted in the DES runs with a 5 degree hub rotation per time step. Drag convergence with time for the unsteady runs and comparison of predicted and test drag is shown in the accom-pa ny i ng i ma ges. A s ex pec ted , t h e m a x i m u m d r a g o c c u r s when the blade attachments are

4 Base + Bifilar

4 Base + Swash Plate + Scissor & Servos + Pushrods

4 Base + Swash Plate

4 Base + Swash Plate + Scissor & Servos + Pushrods + Beanie

4 Pitch-link Rods Added

4 Base Hubs

4 Base Hubs

4 Base + Swash Plate + Scissor & Servos

UH

-60A

HU

B B

UIL

D-U

P M

OD

ELS

S-92

A H

UB

BU

ILD

-UP

MO

DEL

S

0301

02 04

4 Bifilar Added

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perpendicular to the flow with large frontal area (90°) while minimum drag occurs when the blade attachments are at 45° angle to the flow with minimum frontal area. The time-averaged drag value differed by 4% between the 5° and 0.5° time steps, while the difference in averaged drag between URANS and DES runs was 0.6%. The DES method resolved the turbulence in the hub wake better but the spectral content of this turbulence did not have much impact on over-all hub drag. The final validation simulations for both hubs with their different configurations were performed as DES runs with a time step of 5°.

Results from the DES runs for the S-92A hub showed that the addition of components increased drag and correlated well with the wind tunnel results. The numerical results genera l ly over-pred ic ted the d rag sl ightly and the largest error between simulations and test was below 7%. For the UH-60A hub, the nu merica l resu lts u nder-pred icted the drag while other trends were the same as the S-92A hub. The drag shown in the tables below is the normalized computed drag, based on the experimental base hub drag. Contours of surface pressure and velocity magnitude at mid-plane are shown in the accompanying images. The effect of rotation can be clearly seen in the surface pressure, while the unsteady vortices shed from the rotating hub can be seen in the blue-green regions behind the hub in the velocity magnitude.

The Si korsky S-92A hu b showed a ta i l excitation during tests in the early stages of flight development that were attributed to the wake coming from the scissor and associated fittings on the rotor hub, the only structures that cou ld i nduce a 2 per rotor revolution

Base

Base + B

ifilar

Base + Sw

ash Plate

Base + Sw

ash Plate +

Scissors &

Servos

Base + Sw

ash Plate + S

cissors &

Servos + P

ushrods

Base + Sw

ash Plate + S

cissors &

Servos + P

ushrods + Beanie

1.4

1.2

1.0

1.8

0.6

0.4

0.20

Prediction

Test

Above: Normalized drag of S-92A hub configuration Above: Normalized drag on UH-60A hub configuration

forcing function. This was resolved by raising the vertical position of the hub and by making changes to the pylon. The unsteady analysis on the S-92A hub compared the fast fourier transformation (FFT) of the unsteady rotor hub drag with and without scissor and scissor fittings. The graph on the left shows that the simulations with all components included lead to large 2p drag force, while configurations without the scissors still exhibit a small 2p content com i ng f rom sma l l d rag f rom the scissor fittings.

CONCLUSIONSikorsky Aircraft set out to study the feasibility of using numerical simulations to accurately predict hub drag of new design hubs early in the design phase. The blind numerical simulations showed that STAR-CCM+ can predict the hub drag reasonably well, with the largest error being 7% compared to the experiments. For an initial study without grid convergence analysis, t hese a re accepta ble a nd t he pred ic t ion s will only improve with solution-based grid refinement and time step studies. The time taken to go from CAD to results was around 14 man-hours and ~30 CPU-hours for MRF studies and ~75 CPU-hours for DES studies. These numbers show that an experienced user can conduct a hub drag analysis as part of the design study very quickly and effectively with acceptable accuracy, thereby providing early insight into the hub design. The results from this study indicate that in addition to generating grids in a timely manner for complex hub drag studies, STAR-CCM+ is also well-poised to take on the challenge of predicting the wake structure downstream of the hub with high fidelity.

Normalized computed Drag Relative to Experimantal Base Normalized compute Drag Relative to Experimantal Base

1.4

1.2

1

0.8

0.6

0.4

0.2

0Base Base +

PushrodsBase + Pushrods + Bifilar

Above: Drag convergence history in unsteady modes of operation

Drag

URANS, 5 degrees / time step

DES

,5 d

egre

es

DES

, 0.5

deg

rees

0 1.0 1.5

Above: Harmonic content of unsteady drag for selected S-92A configurations

FFT of Unsteady Hub Drag

0p 1p 2p 3p 4p 5p 6p 7p 8p 9p 10p

Base + Swash Plate + Scissors & Servos + PushrodsBase + SwashPlateBse Hub