eK

FrigateBluff-body owValidation

icctuudieormrovctrhi

data has been compared to full-scale experimental results obtained at sea. It is shown that the inclusion

pters tding dch, roluperstrevailiover th

consuming First of Class Flight Trials (FOCFTs) [1].Modelling and simulation of the ship-helicopter dynamic inter-

face (DI) has been an active area of research in recent years. Theuse of ight simulation to aid pilot training for deck landings isnow commonplace, but its effectiveness is highly dependent onsimulator delity. One of the key areas for improvement in this re-

velocity gradients. In [4] turbulent uctuations were applied with-in the simulator based on local ow gradients, however this canonly be classed as a rst-order approach as the physical processesresponsible for turbulence generation are not represented. It hasbeen suggested that time-accurate CFD simulations which can cap-ture the unsteady effects of ship airwake turbulence may providethe required delity [6,7]. Such computations, which typically re-quire thousands of hours of CPU time, have recently become feasi-ble due to advances in computing power and, in particular, theincreased availability of parallel computing. Numerous studies of

* Corresponding author. Tel.: +44 (0) 151 794 4692.E-mail addresses: james.forrest@liverpool.ac.uk (J.S. Forrest), i.owen@liverpoo-

Computers & Fluids 39 (2010) 656673

Contents lists availab

rs

lsel.ac.uk (I. Owen).the ship airwake. Due to bluff-body ow separation and the com-plex interaction of unstable separating shear layers and vortices,the airwake contains time-varying turbulent structures whichcan have a signicant impact on aircraft handling qualities. At cer-tain wind-over-deck (WOD) conditions the airwake can cause suf-cient pilot workload to reduce the probability of a safe landing tounacceptable levels. To reduce the risks associated with line pilotsoperating helicopters from ships, operators must develop shiphelicopter operating limits (SHOLs) through hazardous and time-

airwake turbulence is a key factor in replicating workload levelsexperienced at sea [3], however this is proving to be a particularlychallenging aspect of DI modelling and simulation.

One approach to modelling ship airwakes is through the use ofComputational Fluid Dynamics (CFD). In this method CFD is used tosolve for the ow over the ship geometry, with the resulting veloc-ity eld data exported to a ight simulation environment as look-up tables. Early work in this eld solved the steady-state equationsof uid ow [4,5], providing airwakes which consisted of mean1. Introduction

The launch and recovery of heliconicant challenges for the pilot. Lanand are often moving due to the pitthe ship. Air passing over the ships snation of its forward speed and the pmation of a region of disturbed ow0045-7930/$ - see front matter 2009 Elsevier Ltd. Adoi:10.1016/j.compuid.2009.11.002of an atmospheric boundary layer velocity prole in the CFD computations improves the agreement withfull-scale data. Qualitative comparison between the simple frigate shape and T23 airwakes shows thatlarge-scale ow patterns are similar; but subtle differences exist, particularly at more oblique WODangles.

2009 Elsevier Ltd. All rights reserved.

o naval ships poses sig-ecks are typically smalll, and heave motions ofructure due to a combi-ng wind causes the for-e ight deck known as

gard is the modelling of ship airwakes. It has been suggested thathigh delity piloted ight simulation could also be used to supple-ment SHOLs obtained at sea [2], providing a much safer alternativeto sea trials and giving the added benet of allowing investigativeight tests while new ships are still at the design stage. One of themain barriers to these advances is a lack of condence in the accu-racy of airwakes currently deployed in ight simulators. The accu-rate representation of time-varying disturbances resulting fromAirwakeDetached-Eddy Simulation

wake ow topology at headwind and Green 45 conditions highlights the dominant ow features over theight deck and it is shown that signicant differences exist between the two WOD angles. T23 airwakeAn investigation of ship airwakes using D

James S. Forrest *, Ieuan OwenDepartment of Engineering, University of Liverpool, Brownlow Hill, Liverpool L69 3GH, U

a r t i c l e i n f o

Article history:Received 9 March 2009Received in revised form 5 June 2009Accepted 5 November 2009Available online 12 November 2009

Keywords:Computational Fluid Dynamics

a b s t r a c t

Computational Fluid DynamSimulation (DES) on unstruFrigate (T23) have been sttion exercise has been perfquality wind tunnel data pquantities and velocity spescale turbulent structures w

Compute

journal homepage: www.ell rights reserved.tached-Eddy Simulation

s simulations of ship airwakes have been performed using Detached-Eddyred grids. A generic simple frigate shape (SFS2) and a Royal Navy Type 23d at several wind-over-deck (WOD) conditions. A comprehensive valida-ed, comparing CFD results of the airwake calculated for the SFS2 with highided by the National Research Council of Canada. Comparisons of meana show good agreement, indicating that DES is able to resolve the large-ch can adversely impact helicoptership operations. An analysis of the air-

le at ScienceDirect

& Fluids

vier .com/locate /compfluid

the ow eld around a 1:100 model of the SFS2. Mean velocity dataand turbulence statistics were obtained at a number of locationsover the ight deck and the forward superstructure; long acquisi-tion periods also allowed accurate velocity spectra to be derived.

Several authors have published the results of computationalstudies on the SFS/SFS2 geometries. Reddy et al. [21] used the com-mercial CFD code, FLUENT, with the k e turbulence model to

Fig. 1. SFS2 geometry; extent of original SFS geometry shown by darker shading.

ers &time-accurate ship airwakes using the CFD code COBALT have beenperformed by Polsky at NAVAIR [8,9] in support of the US Depart-ment of Defense Joint Shipboard Helicopter Integration Process(JSHIP) project [10]. Using the JSHIP Dynamic Interface Modelingand Simulation System (DIMSS), a comprehensive series of simu-lated ight tests were performed by four pilots ying the UH-60A Black Hawk to an amphibious assault class (LHA) ship [11].It was shown that the inclusion of the unsteady component ofthe airwake signicantly increased pilot workload during ship-board manoeuvres. Lee et al. also generated unsteady CFD airwakedata for the LHA using the parallel ow solver PUMA/PUMA2 [1214]. In these cases the data was used for a shipboard approach andlanding task driven by an optimal control model of a human pilot.During these studies, it was also found that the time-varying air-wake effects had an impact on the control activity predicted bythe pilot model.

Although the use of time-accurate airwake data has been shownto be an important factor in improving the delity of DI simula-tions, it is equally important that the spatial and temporal charac-teristics of turbulence introduced to the simulator are representedaccurately. CFD methods are routinely validated using full-scaleexperimental data obtained in situ, or model-scale data from the

Nomenclature

b ship beam (m)d domain depth (m)fb SST kx eddy-viscosity constanth hangar height (m)l deck length (m)ls ship length (m)r domain radius (m)Dt non-dimensionalised time-stepu longitudinal velocity (m/s)v lateral velocity (m/s)w vertical velocity (m/s)x longitudinal distance (m)y lateral distance (m)y non-dimensionalised wall distancez vertical distance (m)

J.S. Forrest, I. Owen / Computwind tunnel. However, many of the published airwake studies con-tain inadequate validation. Where attempts are made to compareCFD results with experimental data, the comparison is often qual-itative (e.g. [15]), turbulent characteristics are frequently neglected(e.g. [16]) and in many cases the analysis is limited to a singleWODcondition (e.g. [4,17]). Sometimes the lack of detail is due to thesensitive nature of military naval hardware, which means thatthere has been a relative lack of high quality experimental datamade available for ship airwake validation studies.

In an effort to develop a ship airwake validation database, andto facilitate the dissemination of best-practices amongst the DIsimulation community, a collaborative ship airwake modellingactivity was set up under the auspices of The Technical Co-opera-tion Programme (TTCP) [18]. The simple frigate shape (SFS), ahighly simplied ship geometry, was created to provide an easilyrepeatable benchmark case for validating CFD codes. Fig. 1 showsan updated version of this geometry, the SFS2, with an elongatedsuperstructure and a pointed bow. The National Research Councilof Canada (NRC) has performed a series of wind tunnel tests onboth geometries. Cheney and Zan [19,20] studied the mean surfaceow topology on a 1:60 scale model of the SFS using oil and pres-sure tappings; off-body ow was examined using smoke visualiza-tions. Following this, hot-lm anemometry was used to investigatezref reference height (m)CDES DES length scale constantI turbulence intensity, normalised by V1 (%)Lt turbulent length scale (m)Re Reynolds numberSt Strouhal numberU local velocity magnitude (m/s)Uref velocity magnitude at reference height (m/s)V1 freestream velocity (m/s)a surface roughness constantD local grid spacing (m)D0 grid spacing in focus region (m)k2 second eigenvalue of S

2 X2

Fluids 39 (2010) 656673 657solve the steady-state ow around the SFS on structured grids. Sig-nicant variations in ow topology were seen for different griddensities, although grid independence was approached as the cellcount rose above 1 million. Reasonable qualitative agreement withoil ow visualizations was shown, however the locations of reat-tachment points and vortices differed from the experiments. Liuet al. [22] also analyzed the SFS, obtaining inviscid, unsteady re-sults from mean ow eld computations using non-linear distur-bance equations (NLDE). Calculations were performed usingparallel processing on structured grids and identied regions ofhigh turbulence and vortex shedding over the ight deck. As partof an analysis of rotor loads in ship airwakes, Wakeeld et al.[23] computed ow over the SFS using a steady-state NavierStokes solver. Many of the large-scale ow features seen in thewind tunnel were replicated by the CFD, although at certain condi-tions the computational results predicted ow separation over re-gions of the ight deck which was not observed experimentally.Roper et al. [5] performed validation studies of both the SFS andSFS2 before the resultant ship airwake data was integrated into apiloted ight simulation environment. Solution dependent gridadaption was used to rene the mesh in areas of large ow gradi-ents. A comparison of surface pressure coefcients and off-bodyvelocity components showed reasonable agreement to the NRC

during the ship landing task.The main purpose of the current study is the generation of time-

FLUENTs pressure-based NavierStokes solver was used, with asecond-order pressure interpolation scheme. Convective termswere discretised using a third-order Monotone Upstream-centeredSchemes for Conservation Laws (MUSCL) scheme [34] consisting ofa blended central differencing/second-order upwind formulation.Time integration was performed implicitly using a second-orderaccurate scheme with dual time stepping.

2.2. Solution strategy

Solutions were iterated to steady-state before the unsteady sol-ver was activated. A baseline time-step of Dt 1:88 102 (nor-

ers & Fluids 39 (2010) 656673accurate ship airwake data for use in the University of LiverpoolsHELIFLIGHT simulation environment [29]. Flight trials using thisenhanced airwake model have been performed and pilot com-ments are encouraging. Some preliminary results have alreadybeen obtained [30,31] and a comprehensive analysis of recent sim-ulation trial data in terms of pilot control activity and workloadratings will be presented in due course.

2. Computational details

2.1. Numerical method

Computations were performed using the commercially avail-able nite-volume code FLUENT [32], employing DES with theshear stress transport (SST) kx turbulence model for closure.The DES approach uses a modication to the underlying turbulencemodel to allow medium to large-scale turbulent structures to beresolved in regions where the computational grid is ne enough.This modication links the turbulent length scale to the local gridspacing, D, such that levels of eddy-viscosity are suppressed whereD is small. The term which governs the dissipation of turbulent ki-netic energy in the SST kx model involves the parameter, fb ,which is a constant equal to 1 in the standard model, but modiedaccording to the following expression in the DES implementation[33]:

fb max LtCDESD ;1

1

where Lt is the turbulent length scale and CDES is a constant withinthe DES model usually given a value of 0.61 (retained in the currentstudy). In regions of the ow where D is small (i.e. below the localturbulent length scale), the value of fb is increased above unity,thus causing an increase in dissipation of turbulent kinetic energy.data for steady-state computations with the realizable k e turbu-lence model. More recently, Yesilel and Edis [24] showed somelimited improvements to the comparisons presented by Roper,through the use of unsteady simulations with CFX and FLUENT. Fi-nally, Syms [25] used the lattice-Boltzmann technique to performtime-accurate solutions of SFS and SFS2 airwakes. Good agreementto the experimental data is shown, despite a slight over-predictionof RMS turbulence.

This paper reports a signicant step forward in DI research bypresenting the results of time-accurate CFD computations of theSFS2 airwake, computed using Detached-Eddy Simulation (DES).DES is a relatively new approach to turbulence modelling whichis promising for ship airwake applications due to its ability toexplicitly resolve turbulent structures for massively separated highReynolds number ows around bluff bodies [2628]. Its behavior issimilar to Large-Eddy Simulation (LES), but is computationallycheaper; closer to unsteady Reynolds-averaged NavierStokes (UR-ANS) in terms of required CPU time. A description of the solutionstrategy will be given later in the paper and this will be accompa-nied by a detailed comparison of the CFD results with the NRCwind tunnel data. The ship airwake generation technique de-scribed has also been applied to a Royal Navy Type 23 Frigate;some limited validation of these airwakes against full-scale ane-mometer data will be presented. An analysis of the SFS2 and Type23 Frigate airwake ow topologies will be performed, highlightingthe dominant ow features which contribute to pilot workload

658 J.S. Forrest, I. Owen / ComputIt is the resultant decrease in eddy-viscosity which prevents oweld perturbations from becoming articially damped by the turbu-lence model and allows turbulent structures to propagate.malised by freestream velocity and ship beam) was chosen basedon guidelines given in [35] and through comparisons with non-dimensionalised time-steps used in other DES studies [36,37].Computations were also performed using time-steps scaled by afactor of 2 in each direction to test solution sensitivity. Comparisonof mean ow statistics to experimental data showed that changingthe time-step had very little effect on the solution. Spectral analy-sis of velocity uctuations over the ight deck showed that smallertime-steps were able to resolve progressively more energy at fre-quencies above 10 Hz. However, at full-scale the majority of turbu-lent energy in the airwake is known to be in the range 0.11 Hz andit is known that disturbances at frequencies above 2 Hz have littleeffect on pilot workload [3], therefore the increased computationalexpense of the smaller time-step is not justied.

An exercise was performed to test the optimal number of sub-iterations; this showed that 10 iterations per time-step gave atleast two orders of magnitude drop in the continuity residualand at least three in the others. Increasing the number of sub-iter-ations beyond 10 did not increase convergence signicantly andadded considerably to the required run time.

Approximately 23 time units were computed to remove tran-sients before unsteady sampling began. Flow statistics were thenaveraged over the next 90 time units. A complete CFD run con-sisted of 6000 time-steps, with 4800 used for sampling. Computa-tions were performed to match the experimental wind directionswhich are dened using naval terminology, with winds from star-board denoted as Green and winds from port as Red. Conditionswere therefore computed for a headwind, Green 45 and Green90; each run taking approximately 200 h of wall clock time on32 processors of the University of Liverpools high performancecomputing cluster.

2.3. Grid generation and boundary conditions

The SFS2 geometry was placed in the centre of a cylindrical do-main with a radius r 4:5ls, where ls is the ship length; the depthof the domain was set to approximately d 0:75ls. This congura-tion (shown in Fig. 2) was chosen as it allowed the WOD conditionFig. 2. Schematic showing SFS2 grid conguration.

to be altered simply by changing the x and y components of free-stream velocity. The outer, curved, boundary was specied as apressure far-eld, both upper and lower surfaces set as walls withzero shear-stress and the ship surface was modelled as a wall witha no-slip boundary condition imposed.

The SFS2 is a relatively simple geometry and could easily bemeshed using hexahedral cells on a block-structured grid. How-ever, the main purpose of these computations was to serve as a val-idation exercise for DES applied to ship airwakes, with the aim ofapplying the same methods to more complex ship geometries.Ships such as the Type 23 Frigate have superstructures with intri-cate geometric features which would prove extremely difcult tomesh using a structured approach. Such geometries are ideally sui-ted to unstructured meshes as cells can be tightly clustered aroundthe smaller features and allowed to grow larger away from the shipwhere ow gradients are smaller. The SFS2 was therefore meshedusing a hybrid structured/unstructured mesh to test whether satis-

Both qualitative and quantitative comparisons will be made of

Fig. 3. Surface mesh covering the SFS2 and the lower wall boundary.

J.S. Forrest, I. Owen / Computers & Fluids 39 (2010) 656673 659factory results could be obtained using this approach. Some suc-cess has already been reported using unstructured grids withDES [38], as the isotropic nature of tetrahedra suits the methodwell.

The ship surface was meshed with triangular elements and 15layers of prisms were grown from the surface to resolve the viscousboundary layer. Spacing normal to the wall was chosen to give wallunit values of y O(10) and an expansion ratio of 1.3 was applied.The geometry was placed inside an unstructured sub-domainwhich was meshed using tetrahedral elements. Size functions wereapplied to ensure smooth cell growth away from the superstruc-ture and also to create a renement region over the ight deck.This is analogous to Spalarts Focus Region [35], where cellsshould be small and, as far as possible, isotropic in the area of inter-est to fully utilise the power of DES. In regions outside of the sub-domain a structured meshing approach was employed.

To test grid independence, and in accordance with the guide-lines in [35], three target grid spacings D0 were analyzed bychoosing a baseline D0 and scaling this up and down by a factorof

2

p. The test computations were performed for a headwind at

a freestream speed of 40 kts and the grids were denoted A10:4 106 cells, B 5:8 106 cells and C 3:3 106 cells forspacings of D0=h 4:12 102;5:83 102 and 8:33 102,respectively, where h is hangar height. The surface mesh for spac-ing B can be seen in Fig. 3. Time-steps for grids A and C were scaledlinearly with D0 to ensure that temporal and spatial resolution re-mained well-balanced. The requirement for a reduction in time-step with renement of the grid adds further to the computationaltime required for runs on the ner grids.Fig. 4. Comparison of velocity magnitude (a) and turbulence intensity (b) on a lateral linfreestream velocity and ship beam.nicant improvement in comparison to experimental data. Thesimilarity of the three sets of results, despite the difference in cellsize between them, indicates grid-independent solutions. Thecoarsest grid, C, would appear to give satisfactory results in termsof mean quantities, however it is worth re-iterating that the pur-pose of these computations is to provide accurate unsteady datafor use in ight simulation. A coarser grid requires a larger time-step to maintain an appropriate Courant number; for grid C thiswould correspond to a solution frequency of 56 Hz. As the outputfrom these computations would be used to produce unsteady air-wake data for use in piloted ight simulations, it was felt that ahigher airwake update frequency than this may be required to en-sure an appropriate level of simulator delity. The run-time andmemory requirements for grid B were within the capability ofthe compute cluster, therefore it was decided that grid B wouldbe used for all subsequent computations to ensure that spatialand temporal resolutions were adequate.

3. Results and comparison

In this section results from the CFD computations will be com-pared with wind tunnel data to validate the CFD methodology.When comparing mean ow quantities over the ight deck forthe three grids little difference was found between the results.Fig. 4 shows mean velocity magnitude and turbulence intensityalong a lateral line above the ight deck, with wind tunnel dataalso plotted for reference. It is clear that none of the grids offer sig-e over the ight deck for computational grids A, B and C. Results are normalised by

time-averaged ow quantities and velocity spectra at several WODconditions. All velocity data is normalised with respect to the free-stream velocity magnitude. Distances are dened relative to theairwake origin (located at the base of the hangar on the ship centr-eline) with longitudinal, lateral and vertical locations normalisedby deck length, ship beam and hangar height, respectively. The axissystem is dened such that x is positive to stern, y is positive tostarboard and z is positive up.

3.1. Experimental details

The experiments were conducted in the 2 m 3 m low-speedwind tunnel at the Aerodynamics Laboratory of the National Re-search Council, Canada. Hot-lm anemometry was used to obtainuv and uw data consisting of mean velocities and turbulenceintensities along a series of experimental maps over the SFS2(Fig. 5). Unsteady velocity spectra were also recorded at severalpoints over the superstructure. The 1:100 scale model was

mounted on a turntable and boundary layer suction was employedto ensure a uniform incident velocity prole. Fig. 6 shows the scalemodel of the SFS2 mounted inside the wind tunnel.

3.2. Reynolds number dependence

The SFS2 geometry is a bluff-body, consisting of rectangularsurfaces and sharp edges. It is generally assumed that the ow oversuch structures is insensitive to Reynolds number, so the owtopology at full scale should be replicated at model scale. Conse-quently, all CFD runs were performed at full scale, equivalent toa Reynolds number of 2:26 107 where ship beam, b, is used asthe characteristic length. However, to test Reynolds number sensi-tivity a single headwind case was computed to match the windtunnel conditions, at a Reynolds number of 6:58 105. It wasfound that the ow patterns were essentially the same, with someslight differences in the location of features such as reattachmentpoints and vortex cores. This is illustrated in Fig. 7, where the cen-

Fig. 5. Location of experimental maps during wind tunnel testing. Black dots indicate spectra recording points. Distance along deck measured from the hangar given inpercent.

660 J.S. Forrest, I. Owen / Computers & Fluids 39 (2010) 656673Fig. 6. SFS2 model mounted inside the NRC 2 m 3 m low-speed wind tunnel (image courtesy of NRC, reprinted with permission).

tres of the recirculation bubbles are indicated; the model scale casebeing below and slightly aft the location of the full scale case. Sim-

approximately 5% higher than the other two components overthe ight deck. This is not seen from the CFD results, where the

Fig. 7. Location of recirculation centres (circles) and reattachment points (bold arrows) for CFD calculations at model scale and full scale.

J.S. Forrest, I. Owen / Computers & Fluids 39 (2010) 656673 661ilarly, ow is shown to reattach to the deck slightly earlier at modelscale, at 45% of deck length compared to 49% at full scale. These ef-fects are not anticipated to have a signicant impact on the datacomparison exercise, although subtle differences in the locationof dominant ow features such as vortices or shear layers can af-fect agreement when plotting at a limited number of data points.

3.3. Comparison to experimental data

Data plots have been drawn along a lateral line located at Map1c/3c in Fig. 8 for comparison. The line is located at 50% of theight deck length, at hangar height, and spans the equivalent oftwo beam widths. Many such plots have been studied and showsimilar trends; this location was chosen as it represents the regionclosest to where a helicopter would be hovering during a decklanding.

Fig. 8 shows mean velocity components and turbulence intensi-ties for the headwind case. A reduction in longitudinal velocity canbe seen towards the centreline, indicating that the plotting locationintersects the wake behind the hangar. A corresponding downdraftis also seen, showing that the ow separated from the hangar roofis heading down towards the deck. The slight asymmetry shown inthe experimental results and as computed by Syms [25] is not evi-dent in the present computations. However, all of the trends exhib-ited in the wind tunnel data are replicated by the CFD and, in mostlocations, excellent agreement is shown. Studying the turbulencedata in Fig. 8, again the trends are in agreement although thereis some discrepancy, particularly in the longitudinal component.The experimental data shows that the longitudinal turbulence isFig. 8. Headwind mean velocities (a) and turbulence intensities (b) normalised by V1 atx-component is only slightly higher. Overall though, the agreementfor the headwind case is very good, with turbulence intensity lev-els matched well, particularly in the lateral and vertical directions.

As the wind direction changes from a headwind around to aGreen 45 condition (wind at 45 from starboard), the ow patternover the deck is altered dramatically. Weak vortical structures areseen to appear over the superstructure as the wind moves off thebow, before strengthening considerably and impacting the ightdeck as the wind reaches Green 30 [19]. At Green 45, ow overthe ight deck is dominated by separation from the windward deckedge and a vortical structure is formed from the upper corner ofthe windward edge of the hangar. These conditions prove challeng-ing for numerical methods to predict as the off-body ow patternsare sensitive to the shear layer separation angles. Although separa-tion points are generally xed by the sharp edges, strong stream-line curvature in the separating shear layer is difcult for eddy-viscosity based turbulence models to capture and this causes dif-culty in obtaining accurate predictions of shear dominated owtrajectories [39].

A comparison between CFD and wind tunnel results for a Green45 WOD condition is shown in Fig. 9. Velocity components arecompared in Fig. 9 and, while qualitatively the trends are well cap-tured by the CFD, there are some obvious differences in both the uand v components. A similar level of disagreement in the lateralvelocity component is also seen in Syms computations [25]. Inter-estingly, disagreement is seen all the way out to y/b = 1, indicatingthat ow is approaching the ship from slightly different directionsbetween the CFD and wind tunnel. Indeed, when velocity vectorsare plotted, as shown in Fig. 10, it is clear that the incident ow50% deck length, plotted at hangar height. Lateral position normalised by ship beam.

at 5

ers &Fig. 9. Green 45mean velocities (a) and turbulence intensities (b) normalised by V1

662 J.S. Forrest, I. Owen / Computin the wind tunnel is aligned approximately 5 closer to the shipcentreline than the CFD calculations. It appears that the upstreamow in the wind tunnel is affected more strongly by the presenceof the ship than in the CFD; this reduces the effective WOD anglecausing two main effects. Firstly, the u and v velocity componentsover the starboard side of the ight deck are higher and lower,respectively, than the CFD results (as seen in Fig. 9). Secondly,the shear layer separating from the windward hangar edge has ashallower separation angle in the wind tunnel, meaning that its ef-fects are seen closer to the starboard deck edge than in the CFD.This can be seen by the difference in locations of the lateral turbu-lence intensity peaks in Fig. 9. These peaks are caused by appingof the shear layer, and while the CFD peak is at the approximatelocation y/b = 0.16, the wind tunnel peak is seen at y/b = 0. Aswith the headwind case, the longitudinal turbulence componentis under-predicted by the CFD.

Despite the differences identied, the ow eld computed byCFD is qualitatively very similar to the wind tunnel data. Fig. 11shows contours of velocity magnitude and turbulence intensityplotted on experimental Maps 3a3c. The location of the hangaredge shear layer can be identied by the transition from high to

Fig. 10. Velocity vectors comparing CFD and experimental results for the Green 45 casez/h = 1 (c).0% deck length, plotted at hangar height. Lateral position normalised by ship beam.

Fluids 39 (2010) 656673low velocity ow. As the rear of the deck is approached the shearlayer moves progressively to port and becomes thicker, althoughit is slightly closer to starboard in the wind tunnel case, for reasonsdescribed above. The velocity decit behind the hangar is morepronounced in the CFD results; however the relatively coarse spa-tial sampling taken in the wind tunnel may mean that local low-velocity regions are missed.

Only a limited experimental data-set consisting of v andw com-ponents was available for the Green 90 WOD condition, so com-parisons are restricted to contour plots. Contours of verticalvelocity and the vertical component of turbulence on Map 6a areplotted in Fig. 12. Good agreement is shown, with an increase inthe vertical velocity component observed close to the starboardedge of the superstructure due to ow separation. A correspondingregion of elevated turbulence is seen close to the superstructureedge, with increasing intensity towards the base of the funnel.

Having performed analysis at headwind, Green 45 and Green90WOD conditions it has been shown that the DES-based CFD ap-proach is able to predict the SFS2 airwake with good accuracy. De-spite slight differences in the location and orientation of separatingshear layers, the magnitude of the unsteadiness in the ow over

plotted on a yz plane at x/l = 0.5 (a), an xz plane at y/b = 0 (b) and an xy plane at

d tu

J.S. Forrest, I. Owen / Computers & Fluids 39 (2010) 656673 663Fig. 11. Contours of velocity magnitude (a) anthe ight deck is well captured; which is of prime importance increating a realistic ship airwake model for ight simulation.

3.4. Spectral characteristics

Plots of Power Spectral Density (PSD) are shown in Figs. 13 and14, where velocity data has been recorded at points on Map 1c andMap 2a (see Fig. 5), respectively. The experimental data has been

Fig. 12. Contours of vertical velocity (a) and turb

Fig. 13. Power Spectral Density plots of longitudinal (a) and laterrbulence intensity (b) for the Green 45 case.scaled using the Strouhal number to match the full-scale CFD con-ditions. Spectral characteristics are extracted from the CFD datausing a fast Fourier transfrom (FFT) algorithm.

At the spectra point on Map 1c (located at a distancex=l 0:5; y=b 0:4 and a height of z=h 0:75 above the deck)agreement between CFD and wind tunnel data is very good, bothin terms of frequency content and power. The longitudinal velocitycomponent (Fig. 13a) exhibits a gradual drop-off in the range 0.2

ulence intensity (b) for the Green 90 case.

al (b) velocity components recorded at Map 1c spectra point.

0.3 Hz, with the CFD results matching the experimental gradientvery well despite a small power decit in the CFD data between0.7 and 0.9 Hz. The lateral component (Fig. 13b) shows a steady in-crease in power to a small peak at 0.9 Hz before dropping off; againthe CFD matches both characteristics well.

The spectra point on Map 2a is located behind the funnel, di-rectly above the hangar on the ship centreline at a heightz=h 1:38 above the deck. Fig. 14 shows a denite peak in thelateral PSD plot at 0.80.9 Hz indicating the possibility of weak

imately St = 0.12, using funnel width as the characteristic length.This is similar to the values reported by other researchers forrectangular cylinders in cross-ow [4042]. However, comparedto these studies, the turbulence levels incident on the funneland the Reynolds number are much higher in the present shipairwake computations; the fact that the funnel is truncatedrather than of innite span will also undoubtedly affect the sep-aration characteristics, thus impacting the shedding frequency. Itis encouraging to see that the CFD technique is able to pick upthe shedding and match the wind tunnel results well, despitethe fact that the computational mesh is not optimised for de-tailed analysis of the funnel wake.

4. Ship airwake ow topology

This section will identify the mechanisms responsible for thegeneration of large-scale turbulent structures over the ight deckand highlight the dominant ow features which are expected toimpact upon helicoptership operations.

4.1. Headwind

At the headwind condition ow separates from the front edgeof the superstructure, resulting in high levels of shear and theformation of turbulent eddies which are convected downstreamtowards the funnel. Figs. 1517 illustrate this clearly, in particu-lar Fig. 17 which uses the k2 criterion [43] to identify the loca-tion of vortex cores (only contours with a value k2 6 0 areplotted). The value k2 is the second eigenvalue of S

2 X2, whereS and X are the symmetric and anti-symmetric parts of the

Fig. 14. Power Spectral Density plot of the lateral velocity component recorded atMap 2a spectra point.

664 J.S. Forrest, I. Owen / Computers & Fluids 39 (2010) 656673shedding from the funnel which, in effect, is a truncated rectan-gular cylinder. This corresponds to a Strouhal number of approx-Fig. 15. Contours of mean (top) and instantaneous (bottom) velocity magnvelocity gradient tensor, respectively. Values of k2 below zeroshow the presence of a vortex core, with increasingly negativeitude for a headwind, plotted on a plane at z=h 1:15 above the deck.

ers &J.S. Forrest, I. Owen / Computvalues indicating stronger vortices. It is also worth noting thesignicant differences between the mean and instantaneousow-elds; further evidence of the highly unsteady nature ofthe ow. The separated region re-attaches to the superstructureat approximately 3 hangar heights downstream of the frontedge, although the exact location is transient due to appingof the re-attaching shear layer.

The strength of the vortical structures appears to diminish asthey travel down the superstructure, but the ow is then re-energised as it encounters the funnel. Fig. 17 shows the turbu-lent eddies shed from the sharp edges of the rectangular funneland the instantaneous contours of Fig. 15 show the resultantasymmetric wake in the lee. As discussed in Section 3.4, this is

Fig. 16. Contours of mean (top) and instantaneous (bottom) veloc

Fig. 17. Instantaneous contours of k2 indicating the presence of vortex cores foFluids 39 (2010) 656673 665evidence to suggest that this structure exhibits coherent vortexshedding. At high hover over the ight deck a helicopter wouldbe above the hangar roof and directly behind the funnel, there-fore the turbulent structures shed from the funnel would belikely to adversely affect handling qualities in this situation.

As ow approaches the ight deck it separates from thesuperstructure at the top and sides of the hangar, forming a3D recirculation bubble in the lee of the hangar. The separationis less severe than that experienced at the front edge of thesuperstructure, resulting in weaker shear layers and a corre-sponding reduction in the strength of turbulent eddies producedby this separation. Nonetheless, the instabilities in these separat-ing ows is the dominant mechanism for turbulence generation

ity magnitude for a headwind, plotted on a plane at y=b 0.

r a headwind, plotted on a plane at z=h 1:15 (top) and y=b 0 (bottom).

666 J.S. Forrest, I. Owen / Computers &over the ight deck, in addition to the coherent structures shedfrom the funnel.

4.2. Green 45 wind

Flow features at Green 45 are signicantly different to theheadwind condition. As the freestream encounters the starboardcorner of the superstructure two vortices are formed (Fig. 18);one aligned laterally with the front edge of the superstructure,the other oriented longitudinally along the windward superstruc-ture edge which then widens as it approaches the funnel. This owpattern was also observed by Cheney and Zan1 [19] as shown inFig. 19.

The longitudinal vortex is broken up as it passes over the funnel,but the ow passing up and over the starboard side of the super-structure causes a secondary vortex to form between the longitu-dinal superstructure edge and the funnel. This is illustrated inFig. 20, where instantaneous and time-averaged contours of k2

Fig. 18. Iso-surfaces of k2 0 indicating the location of vort

1 Although Cheney and Zan studied the SFS1 which has no pointed bow and ashorter superstructure (Fig. 1), for qualitative comparisons the SFS1 and SFS2geometries can be expected to exhibit similar ow features.

Fig. 19. Smoke ow at Green 45 observed during NRC wind tunnel tests of theSFS1 geometry, indicating superstructure vortices (image from Ref. [19], reprintedwith permission).are plotted on a series of y-z slices. Again, the mean and instanta-neous ow-elds show signicant differences; high strength vorti-cal structures are present throughout the wake in Fig. 20, butlargely disappear in the average. This highlights the complex andchaotic nature of the separated ow over the superstructure. Thesecondary vortex located to the starboard side of the funnel is pres-ent in both diagrams, indicating that it is a relatively stationarystructure.

Fig. 21 shows iso-surfaces of k2 over the ight deck, with threedistinct ow features highlighted. Feature (a) is the starboarddeck-edge vortex, formed as ow travelling along the lower star-board side of the superstructure meets freestream ow at the star-board deck edge. The top of this structure is roughly in line withthe hangar, reaching its maximum height in the middle of the ightdeck. Feature (b) is the starboard hangar edge shear layer, which isformed as the ow attached along the starboard edge of the super-structure separates from the vertical edge of the hangar. In CFDanimations this feature is seen to be unsteady in nature, with a

ex cores over the superstructure for a Green 45 wind.Fluids 39 (2010) 656673apping motion commonly seen in bluff-body separation. This isa major contributing factor to turbulence over the ight deck, par-ticularly in regions below the hangar. Feature (c) is the secondarysuperstructure vortex identied above, which appears to break upand shed helical vortical structures as it encounters the ight deck.This ow feature is responsible for most of the turbulence encoun-tered over the ight deck at locations above hangar height andwould therefore pose signicant difculties to rotorcraft operatingto and from such a ship.

The interactions between the ow features identied above re-sult in a highly complex, unsteady ow-eld over the ight deck.Compared to the turbulent structures observed for the headwind,the Green 45 condition produces larger vortical structures withhigher strength (Fig. 22). The impact of this is likely to be turbulentuctuations with larger spatial scales and higher velocities. This isconrmed through a comparison of Figs. 8 and 9, which show thatturbulence intensities over the ight deck peak at approximately20% for the headwind as opposed to 30% for a Green 45 wind.

5. Type 23 Frigate

Following successful validation of the CFD approach with theSFS2 geometry, the same methodology was applied to a Type 23Frigate (T23), which is a class of warship currently in service withthe UK Royal Navy. A picture of the T23 is shown in Fig. 23 and the

Fig. 22. Instantaneous contours of k2 indicating the presence of vortex cores for a Green 45 wind, plotted on a plane at z=h 1:15 (top) and y=b 0 (bottom).

Fig. 21. Iso-surfaces of k2 0 indicating the location of instantaneous vortex cores over the ight deck for a Green 45 wind. Labelled ow structures: deck edge vortex (a);hangar edge shear layer (b); and superstructure edge vortex (c). Outline of SFS2 shown by dashed line.

Fig. 20. Contours of k2 indicating the presence of vortex cores for a Green 45 wind; instantaneous (a) and time-averaged (b).

J.S. Forrest, I. Owen / Computers & Fluids 39 (2010) 656673 667

CAD geometry used is shown in Fig. 24. The geometry is clearly farmore complex than the SFS2 and part of the motivation for thestudy was to determine whether this increase in geometric delityresulted in a noticeable difference in airwake topology. It wasanticipated that the large notch in the starboard edge of the T23hangar may, for certain Green winds, generate ow features notseen on the SFS2. Experimental data was available for validationof the T23 airwakes, in the form of full-scale anemometer readingstaken at sea.

5.1. Experimental details

Full-scale at-sea testing of the T23 airwake were performed byDRA (now QinetiQ) on board HMS Iron Duke in 1994. Mean veloc-ities and turbulence data behind the hangar was obtained using 3-axis propeller anemometers mounted on a mast at heights ofapproximately z=h 0:5, 1.0 and 1.5 above the deck. Longitudinaland lateral anemometer locations are shown in Fig. 25. It is knownthat such anemometers can have a signicant in-built inertia andsuffer attenuation at frequencies above 0.2 Hz [44], so this shouldbe considered when comparing turbulent quantities. Data weremade available at Green 10, Green 30 and Green 90WOD condi-tions. Only comparisons at Green 10 will be presented here forbrevity, although computations were also performed at headwindand Green 45 so that qualitative comparisons to the SFS2 airwakescan be made.

sets. However, it is clear that the trends shown by the at-sea dataare well matched by CFD. This is all the more remarkable given thefact that the CFD geometric model is simplied, lacking many ofthe small-scale features of the full sized ship (masts, netting, an-tenna arrays).

The two CFD computations predict essentially the same wakepattern, with the ABL velocities a factor of approximately 0.8 low-er than the no-ABL results. This is due to the fact that results arenormalised by the velocity at anemometer height. For a uniforminow prole (i.e. the no-ABL case) at a given freestream conditionthe velocity over the ight deck will be the same as the anemom-eter velocity, but where the ABL is included ow velocities over theight deck will be reduced by an amount dependent on the verticaldistance between the deck and the anemometer. It is interesting tonote, however, that the full-scale results show normalised longitu-dinal velocities approaching unity at the windward edge of the

668 J.S. Forrest, I. Owen / Computers &5.2. Computational details

As the overall dimensions of the T23 are very similar to theSFS2, all numerical details (grid spacing, time-step) were retainedfrom the SFS2 computations. Due to the increased geometric com-plexity, the cell count was higher than the SFS2 grid at 7:4 106cells. Computations were performed in exactly the same way asfor the SFS2, as described in Section 2.2.

Fig. 23. A Royal Navy Type 23 Frigate.Fig. 24. Type 23 Frigate CAD geometry used for current study.Due to the fact that the full-scale experimental data was ob-tained at sea, it was necessary to model the effects of the earthsatmospheric boundary layer (ABL) during the T23 CFD computa-tions. The ABL causes a velocity reduction near the sea surfacewhich can be modelled using the following power law [45]:

U Uref zzref

a2

where Uref is the velocity at the reference height, zref , and a is a con-stant which depends on the surface terrain. In the current study val-ues of Uref 35 m=s and zref 300 m were used, giving a windspeed of approximately 50 kts at the nominal T23 anemometerheight. The constant, a, was set to 0.13 as recommended by Couni-han [45] for an ocean surface. During the present study the mod-elled effects of the ABL were limited to an appropriate velocityprole; the effects of increased freestream turbulence were not in-cluded. It was decided that runs would be performed with andwithout the effect of the ABL to determine what, if any, impact thishad on the nature of the airwake. Polsky [46] has shown that forbeam winds (90 from port or starboard) the inclusion of the ABLcan signicantly improve agreement with full-scale experimentaldata.

5.3. Results

Figs. 26 and 27 show a comparison of velocity components andturbulence intensities for the Green 10WOD condition. Map 1a isclosest to the hangar and Map 1c is furthest aft. Due to the smallnumber of experimental locations from the full-scale data it is dif-cult to draw denitive conclusions on agreement between data-

Fig. 25. Anemometer locations during at-sea Type 23 airwake testing.

Fluids 39 (2010) 656673deck, despite these locations being signicantly below the ane-mometer. It is entirely possible that the wind conditions on theday of the at-sea testing differed from the generic power-law

ers &J.S. Forrest, I. Owen / ComputABL model used within the CFD computations. Air ow over theight deck is a result of ship forward speed, prevailing wind, or acombination of both. Ship motion causes uniform ow, whereasthe wind is subject to an ABL prole; the vector sum of both com-ponents will change the effective power-law exponent. It is there-fore expected that this will account for a certain amount ofvariance between CFD and at-sea results.

The turbulence data follows a similar pattern; the intensity isslightly lower for the ABL case in most locations due to the lowerincident velocities. A corresponding improvement in agreementwith full-scale data is found, consistent with Polskys ndings [46].This reduction in airwake turbulence for the ABL computations isan important nding, as it shows that turbulent uctuations in the

Fig. 26. Longitudinal (a), lateral (b) and vertical (c) velocity components in the TFluids 39 (2010) 656673 669airwake are sensitive to such boundary conditions. Therefore, it isrecommended that ship airwake computations for ight simulationpurposes should include an appropriate ABL velocity prole in orderto improve realism.

5.4. Flow topology

Comparing Figs. 16 and 28 it can be seen that the headwindcondition displays very similar characteristics to the SFS2 ow-eld over the ight deck. The main effect of the geometric featureson the superstructure is to retard the ow so that velocities aroundthe ight deck are slightly lower than seen on the SFS2. Neverthe-less, the mean and instantaneous velocity elds shown in Fig. 28

ype 23 Frigate airwake plotted at hangar height for a Green 10 condition.

ers &670 J.S. Forrest, I. Owen / Computexhibit the same large-scale ow features as seen for the SFS2. Onedifference worth noting is the effect of the large mast to the fore ofthe T23, which appears to shed vortical structures. These propa-gate downstream, but are at a height unlikely to affect helicopteroperations.

There are some clear differences between the T23 and SFS2 air-wakes at Green 45. Fig. 29 shows the effect of the windward han-gar notch on the mean ow pattern over the deck. Flow curvessmoothly around the vertical hangar edge of the SFS2, whereason the T23 the presence of the notch causes the formation of a vor-tex which is aligned more longitudinally with the deck than thefreestream. This suggests that relatively small-scale geometric

Fig. 27. Longitudinal (a), lateral (b) and vertical (c) turbulence intensities in theFluids 39 (2010) 656673features can give rise to differences in large-scale stationary (in atime-averaged sense) ow patterns over the ight deck. It shouldbe emphasised that in both cases these features are highly unstea-dy in nature, with both causing the production of turbulence overthe deck. However, it is known that both mean and uctuatingvelocities over the deck can contribute to pilot control strategyduring landing [31], so it is important that large-scale mean owfeatures are captured.

Another difference between the T23 and SFS2 airwakes at Green45 is that there are fewer identiable, coherent ow features overthe ight deck in the T23 case. This is in part due to the hangarbeing set back from the deck edge, resulting in a deck edge vortex

Type 23 Frigate airwake, plotted at hangar height for a Green 10 condition.

comparable to the SFS2 case. The dominant cause of turbulencegeneration at this WOD condition is separation from the windwardcorner of the hangar; these vortical structures appear to be shedmore uniformly than for the SFS2, possibly due to less interactionwith other large-scale ow features. Fig. 30 also shows that high-strength vortical structures are concentrated more on the port sideof the deck for the T23, as opposed to the SFS2 case (Fig. 22) wherevorticity occupies a larger proportion of the ight deck.

6. Summary and concluding remarks

Unsteady computations of the ow over the SFS2 and T23 shipshave been performed using the commercial CFD code, FLUENT. Theuse of DES for turbulent closure is shown to be suitable for suchows due to its ability to capture the large-scale turbulent struc-tures shed from ship superstructures. Unstructured grids wereused to enable the close control of cell size and to simplify the pro-cess of meshing complex ship geometries; this approach hasproved successful in these respects.

A comprehensive validation exercise, comparing CFD results forthe SFS2 to wind tunnel data for three WOD conditions has beendescribed. Mean velocity and turbulence data have been comparedusing line plots, contours and vectors, showing good agreement inmost cases. An analysis of PSD plots showed that the computa-tional method was able to generate levels of turbulent power com-parable to the wind tunnel, and also matched the frequency roll-offover the relevant bandwidth. This is essential if CFD generated air-wake data is to be used for piloted ight simulation of deck landingtasks.

Airwake data for the T23 was compared to full-scale experi-mental data, showing good agreement with trends observed atsea. Computations of airwakes with and without the inuence of

elocity magnitude for a headwind, plotted on a plane at y=b 0.

ers & Fluids 39 (2010) 656673 671which is less pronounced. The staggered vertical hangar edge alsoappears to lessen the intensity of the separating shear layer.

A comparison of Figs. 22 and 30 shows that turbulent structuresover the deck are smaller for the T23, although their strength is

Fig. 28. Contours of mean (top) and instantaneous (bottom) v

J.S. Forrest, I. Owen / ComputFig. 29. Mean pathlines for the Green 45 condition, indicating the separationcharacteristics from the windward hangar edge of the SFS2 (a) and the Type 23Frigate (b). Pathlines coloured by vorticity magnitude.

the

ers &an ABL showed that the inclusion of a representative ABL velocityprole caused lower velocities and a corresponding reduction inturbulence over the deck. This improved agreement with full-scaledata, particularly in terms of turbulence intensity.

Post-processing of the CFD data allowed extraction of the maintopological ow features for winds from ahead and Green 45. Itwas shown that shear layer separation and vortex formation fromsharp edges is the dominant mechanism for turbulence generationover the ight deck. Signicant differences exist between head-

Fig. 30. Instantaneous contours of k2 indicating the presence of vortex cores for672 J.S. Forrest, I. Owen / Computwind and Green 45 airwakes, with the latter containing turbu-lence with larger amplitude and spatial scales.

Differences between the SFS2 and T23 airwakes have beenfound for the WOD conditions studied. The T23 airwake is shownto contain smaller turbulent structures with similar strength tothe SFS2 and less complex interactions between large-scale vorti-cal features are observed. Larger differences exist in terms of themean ow pattern over the deck at Green 45, due to the presenceof the T23 windward hangar notch. It is still unclear whetherthese differences are signicant enough to be detected by a pilotduring simulated deck trials, indeed the use of a simplied shipgeometry may be adequate for many purposes. However, it hasbeen shown that the intelligent use of unstructured meshing tech-niques for complex ship geometries can give an increase in airwakedelity at a modest increase in computational cost. The use of real-istic geometries for use in CFD airwake simulations is, therefore,recommended.

Acknowledgements

The rst author is funded by an EPSRC Doctoral Training Awardand by Westland Helicopters Ltd. ANSYS Inc have been most gen-erous in their assistance. The authors would like to thank Mr CliffAddison for invaluable help with compute cluster congurationand Mr Steven Hodge (BAE Systems) for providing the Type 23Frigate CAD model. The SFS2 validation data was derived by theNational Research Council Canada, and provided under the aus-pices of The Technical Co-operation Program (TTCP). The full-scaleType 23 Frigate validation data was provided by the UK DefenceScience and Technology Laboratory (DSTL).

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J.S. Forrest, I. Owen / Computers & Fluids 39 (2010) 656673 673

An investigation of ship airwakes using Detached-Eddy SimulationIntroductionComputational detailsNumerical methodSolution strategyGrid generation and boundary conditions

Results and comparisonExperimental detailsReynolds number dependenceComparison to experimental dataSpectral characteristics

Ship airwake flow topologyHeadwindGreen 45 wind

Type 23 FrigateExperimental detailsComputational detailsResultsFlow topology

Summary and concluding remarksAcknowledgementsReferences