Large Eddy Simulation of Downwind Sailing

Transcription

1 Large Eddy Simulation of Downwind Sailing S Nava, J. Cater, S Norris To cite this version: S Nava, J. Cater, S Norris. Large Eddy Simulation of Downwind Sailing. Patrick Bot. INNOVSAIL International Conference on Innovation in High Performance Sailing Yachts, Jun 2017, Lorient, France. pp , 2017, INNOVSAIL 2017, 4th Edition. < <hal > HAL Id: hal Submitted on 7 Sep 2017 HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

2 Large Eddy Simulation of Downwind Sailing S. Nava, University of Auckland, New Zealand, J. Cater, University of Auckland, New Zealand, S. Norris, University of Auckland, New Zealand, This work presents an investigation into the aerodynamics of downwind sailing using different methods for modelling turbulence, comparing Large Eddy Simulation (LES) and Reynolds-Averaged Navier- Stokes (RANS) methods. Pubblished experiments on a downwind sail plan [2] [8] showed areas of flow separation and vortex structures at the leading and trailing edge of the gennaker. The ability of LES to model the transition to turbulence within the shear layer leads to an accurate prediction of the leading edge separation bubble, which significantly influences the flow field around the top half of the sail. The transient nature of the LES solution allows the computation of the creation and shedding of unsteady vortices at the leading edge and downstream of the sail draft. The effect of the vortex rolls being convected towards the trailing edge is to generate a boundary layer which is more resistent to separation. Comparison with the experimental pressure distribution shows the correct prediction of the separation by LES, while the RANS result shows a large area of stalled flow which limits the suction on the sail. As a result, the overall drive and side forces computed by the LES are in good agreement with the experiments with less than 3.5% error, while RANS underestimates their magnitude by more than 14%. 1 INTRODUCTION The fluid-dynamics that characterises downwind sails is extremely complex, due to the fact that the flow is highly three-dimensional and turbulent, with large separation regions. Computational modelling of such flows has become common in high-performance yacht design. Although the physics of the problem has been investigated through numerous experimental and numerical studies, the predictive methodologies are still affected by uncertainties. The need to develop, improve and validate numerical models is the reason for this work. There have been a number of studies of the aerodynamics of downwind sails using RANS (Reynolds-Averaged Navier- Stokes equations). Early studies had only experimental force data for validation. Hedges et al. [6] published the first RANS model of yacht downwind sails, using CFDS- FLOW3D (a precursor to CFX) to model the aerodynamics of a spinnaker-mainsail combination for the Whitbread 60 race yacht Winston. The model simulated the flow around the two sails and calculated the lift and drag coefficients at different apparent wind angles (AWA) using different onset flow profiles. The solver used the standard k ɛ turbulence closure model. The comparison between the CFD and the wind tunnel data showed differences of less than 15% for the lift coefficient and less that 3% for the drag coefficient. Collie [3] went on to investigate the flow around a curved plate, representative of the flow around downwind sails. Three different turbulence models in a two-dimensional CFD simulation were applied, using the flow solver CFX. The computed lift and drag coefficients were in good agreement with the experimental data, especially for the lowest angles of attack, when there is minimal separated flow. However, the model failed to accurately predict the flow around the plate at higher angles of attack, when separation extended for more than 50% of the plate chord. Different turbulence models were used (i.e. k ɛ, Shear-Stress Transport (SST) and k ω), and SST proved to be the most accurate The SST turbulence model was utilized by Moore et al. [9] in an investigation carried out on different spinnaker shapes. Three rigid model sails were tested at the University of Aucland Yacht Research Unit wind tunnel, reproducing three 1/25 th scale spinnaker designs. A computational model using CFX showed good agreement between experimental and CFD trends for the force coefficients, although the numerical data showed differences with the wind tunnel measurements, with errors up to 25%. The incorrect results were thought to be due to not fully resolving the boundary layer on the sails. Further refinement was introduced by Lasher & Sonnenmeier [7] who modelled the atmospheric boundary layer (ABL) in the simulation of the flow around three different spinnakers. While the wind profile was reproduced with high fidelity in both the wind tunnel and in the CFD tests, it proved harder to achieve the correct turbulence kinetic energy (TKE) profile, because of a large, very low-frequency component. The realizable version of the k ɛ model performed better than the standard model, with the average error in lift coefficient being approximately 4 5% and 8 9% for the drag. Viola [16] performed an investigation assessing the performance of the commercial package STAR-CCM to model downwind sailing aerodynamics. The reference geometry 127

3 was the downwind sailing configuration of a hypothetical AC33 class yacht. The mesh involved was an unstructured mesh composed of 1.5 million tetrahedrons while the code implemented the realizable k ɛ with the two-layer all-y + wall function model. This model switches from the standard wall function model to the standard low-reynolds number model using a blending function on the cells close to the walls. The model provided results close to the experimental values carried out in a previous wind tunnel test with the force coefficients for the drive and side force differing by less than 0.5% from the experimental values. More recently, increased computational resources have enabled the testing of Detached Eddy Simulation (DES) solvers in downwind sailing yacht investigations. Viola [18] used two different meshes of 4 and 32 million cells and tested both Fluent RANS and DES solvers. Despite the increase of mesh resolution with the wall distance y + ranging from 0.2 to 5, only slightly better prediction were made by the DES over the RANS, while in few regions of the gennaker, the pressure coefficients were still substantially incorrect. The reason that DES did not behave as expected was thought to be due to the use of the one-equation Spalart-Allmaras model for the RANS part of the calculation. In addition, the author suggested that the inlet turbulence length scale which was set to 0.01 m might have been underestimated by an order of magnitude, inducing further error in the solution. In this study Large Eddy Simulation (LES) is used in order to evaluate its ability to model the separated flow during downwind sailing. The ability of LES to model separation on airfoils has been demonstrated by previous authors, modelling an A-airfoil at a high angle of attack. For example, Frolich [5] used the Smagorinsky sub-grid scale (SGS) model and found that the model predicted the presence of the trailing edge separation only when it correctly computed the transition position after the leading edge separation bubble. Sagaut [13] used the selective mixed scale model. The trailing edge separation area was correctly predicted and the velocity profiles matched the experiment. However, the Reynolds stresses were overestimated which the authors attributed to the short width of the domain in the spanwise direction. The authors suggested that to correctly capture the flow features, the grid size should not exceed the following values in the airfoil proximity: y + =2, x + =100, z + =20, where y is the normal to the wall direction, x is the streamwise direction and z is the spanwise direction. LES has also been shown to be able to model leading edge separation, which is a flow structure that characterizes downwind sails [19]. Sampaio et al.[14] modelled the flat plate experiment of Crompton and Barret [4], using RANS and LES. Comparison between RANS and LES solution showed that the RANS solver, using the SST turbulence model, failed to predict the presence of the secondary recirculation bubble, while it was predicted by the LES solution using the Dynamic Smagorinsky SGS model. An analysis of the Reynolds stresses showed good agreement between LES and experiments, while the RANS calculations gave results far from the correct values. Sampaio et al.[14] s investigation is particularly relevant for sailing modelling, as the flat plate leading edge bubble features characteristics similar to the separation bubble forming at the sail leading edge. Nava et al.[10] applied the LES methodology to the reproduction of an upwind sailing wind tunnel experiment, highlighting the superior ability of LES compared to RANS in predicting the leading edge structure forming at the luff of the sails and reproducing the experimental pressure distribution. To date LES has only been used for upwind sailing aerodynamics [10], and the experiment of Lebret [8] allows its validation for downwind sailing. A high resolution grid was used to capture the turbulent flow structures at the leading and trailing edge, and to correctly predict the pressure distribution on the sails, especially in regions of flow separation. Simulations were performed using RANS and LES on the same mesh, allowing a direct comparison between the two methods. In the next section the numerical methodology will be discussed; the subsequent sections will focus on modelling the experiment of Lebret [8], with a comparison between the RANS, LES and experimental results. The later section will describe the LES prediction of the unsteady turbulent structures developping around the gennaker. 2 NUMERICAL METHODOLOGY The computational modelling was performed using ANSYS Fluent [1]. This software solves the Reynolds Averaged Navier Stokes equations, or the spatially filtered Navier- Stokes equations used in Large Eddy Simulation. A block structured hexahedral mesh was used for downwind sails model, which was generated with ANSYS ICEM-CFD. This type of mesh was preferred since it allowed fine control over cell dimensions, enabling the specification of different refinement levels in different axes, and it generated a highly refined computational grid in regions of interest. The RANS calculations were performed using the SST turbulence model with a SIMPLE solver. Second-order upwind differencing was used for the momentum equations, while the turbulence scalars were discretized using first-order upwinding, since higher order schemes did not improve the results. The LES calculations used the fractional step solver as the low Courant number ensured the stability of the calculation. The momentum equations were discretised with the secondorder central difference scheme, whilst time stepping used a second-order formulation. The timestep size was based on the smallest dimension of the smallest cell and the onset flow velocity corresponding to a maximum calculated Courant number of 1. The subgrid scale turbulence was modelled using the dynamic Smagorinsky-Lilly model as adopted Nava et al.[10]. 128

4 The Fourth International Conference on Innovation in High Performance Sailing Yachts, Lorient, France For the LES simulations, onset turbulence was created through the generation of fluctuating inlet velocity profiles. The spectral synthetizer method [1] was used, and reproduced the experimental inlet turbulence profiles in terms of turbulence intensity and turbulence length scale. All the simulations were executed until the value of the residuals converged, for the momentum equations, to 10-6, while the continuity equation residual was 10-4 for the RANS solution and 10-6 for the LES solution. For the LES calculations, the flow variables were averaged over a period of 3.5 s, after an initial period of 3.5 s, to allow the flow field to develop. The LES simulations was initialised using the result of the RANS calculation. The calculations were performed on an Intel-based high performance computational cluster, with two Intel Xeon E Sandy Bridge 2.70 GHz processors per Figure 2: Pressure tap locations for the experiment of node, with an infiniband interconnection. The wallclock time Lebret[8]. for the RANS calculations was 6 hours, using 128 cores. For the LES simulations 720 hours (30 days) were required, using 60. The yacht model was mounted on a six component force 128 cores. balance, which recorded the forces and moments in the three orthogonal directions at a rate of 600 Hz with an accuracy of 3 DOWNWIND SAILS EXPERIMENTS +/ N. The force balance data were averaged over a pethe experiment of Lebret [8] used two thick fiberglass sails riod of 90 s. A photogrammetry technique was used to record to model a 1/15th scale downwind sail plan of a hypothetical the shape of the model sails during the experiment. AC33 class yacht design. The distance between the top of the mainsail and the foot of the gennaker was 2.25 m with the 4 DOWNWIND SAILS COMPUTATIONAL MODEL maximum chord-length of the gennaker and mainsails being 1.28 m and 0.65 m respectively. The sails were approximately The geometry for the model was obtained from Vicariot [15], 4 mm thick with a blunt leading edge for the mainsail and a who digitized the data provided by the photogrammetry of 45 chamfered leading edge for the gennaker. The mast and Lebret [8]. The model centreline was rotated by an angle hull were included in the experimental model. Twist vanes of 60 to match the experiment. The hull was added to were not used to ensure the sails were tested in a straight flow the computational model to take into account its effect on (Figure 1). the flow. The mast was omitted from the calculation as its inclusion would have added substantial complication in the meshing process, affecting the mesh quality, especially at the top of the rig, where the sails are close to each other. The sails were modelled as thick surfaces and although the inclusion of thickness resulted in a small increase in the number of cells in the mesh, this improved the prediction of the location of the stagnation point at the leading edge of the sails. The sail leading edges were reproduced by using a square leading edge for the mainsail and by chamfering the gennaker leading edge by 45. Figure 1: University of Auckland Twisted Flow Wind Tunnel experimental apparatus. The gennaker and mainsail were both equipped with pressure taps, which were distributed in ten rows, five on the gennaker and five on the mainsail, as shown in Figure 2. The wind tunnel onset flow was 3.4 m/s, with the turbulence intensity at the model sails location being approximately 1.8%. The sails and hull were oriented to give an apparent wind angle of Previous studies [17, 12, 11], found that the choice of domain size and boundary conditions greatly affected the solution. In the wind tunnel the sails are in a open test section and a open jet flows around the sails, rather than the constrained flow of a closed test section. This must be replicated in order to reproduce the experiment. Therefore, an outer box 13 m wide and 15 m long was used with the sails placed at the centre. The height of 3.1 m matched that of the wind tunnel, the inlet only covered a portion of the front surface, while all the other bounding surfaces were set to an constant pressure outlet boundary condition (Figure 3). The wind tunnel walls were modelled as free-slip walls as was the roof, in order 129

5 to minimise the computational effort in calculating the flow behaviour close to these surfaces. The floor was modelled as a no-slip wall to take into account the wind tunnel floor boundary layer. Finally the sails were given a no-slip wall boundary condition. Figure 4: Detail of the mesh around the sail location used for the downwind sails model. turbulence length scale was set to 0.4 m which is three times the mesh size at the inlet section, allowing sufficient nodes for the LES model to resolve the large scale turbulent structures. Figure 3: Computational domain and boundary conditions used to reproduce the open jet wind tunnel. An H-type block-structured mesh was used with refinement around the sails and in the wake region (Figure 4). Different mesh sizes were tested and a mesh of approximately 11 M cells was chosen for the final simulation. Particular care was taken when meshing the top section, the leading edge, and the trailing edge of the gennaker, where the experiment showed the formation of turbulent structures and highly threedimensional flow. A fine mesh resolution on the gennaker was achieved by reducing the mesh density in other areas of the domain, such as the mainsail and the hull, to control the overall mesh size. The spacing normal to the wall was set to 0.1 mm giving a y + less than 1. The streamwise direction was discretized with 140 cells for the gennaker and 40 cells for the mainsail, with the cell length at the leading and trailing edge set to 1 mm for the gennaker and 2 mm for the mainsail, with the length increasing to 17 mm in the middle of both sails. The growth ratio normal to the wall was approximately 5% on the leeward surfaces and 15% on the windward surfaces, while it was 10% in the streamwise direction for the gennaker and 20% for the mainsail. Finally, an approximately uniform discretization was used in the spanwise direction, with the nodes separated by approximately 20 mm. This value was decreased near the edges to 3 mm. The inlet flow velocity was set to 3.4 m/s with a velocity profile that reproduced the experiemtal measurement at the model location. The inlet flow turbulence intensity was set to 3% and this decayed to turbulence intensities of 2% and 0.6% at the location of the sails for the RANS and LES models respectively. These value for the RANS simulation approximated the turbulent intensity of the tunnel which is approximately 1.8%, while the LES underestimated it. The The timestep size for the results in the next section was 10 µs corresponding to a maximum Courant number less than 1. The simulation was run for 700,000 timesteps to let the solution converge as described in section 2. 5 RESULTS In the next subsections, an overview of the flow field will be provided, highlighting the turbulent structures that characterize it. The pressure distribution computed by RANS and LES will be compared to the experimental results, showing the solvers different predictions of the turbulent structures. Finally, the unsteady flow features computed by LES will be analyzed and related to the force coefficient predictions. 5.1 Flow Field Figure 5 illustrates the mean flow field around the gennaker and mainsail calculated with LES, showing contours of the pressure coefficients, surface streamlines of the wall shear stress on the leeward side of the gennaker, and threedimensional velocity streamlines. The gennaker experiences the upwash generated by the mainsail and the velocity streamlines approach the luff of the sail with an increased angle of attack. Here the flow separates due to the sharp leading edge and forms a leading edge separation bubble, which reattaches to the surface between 5% and 10% of the chord depending on the height. The wall shear stress streamlines indicate a strong vertical component of the flow inside the separation bubble, which is attracted upwards by the low pressure area formed at the head of the sail. The vertical spiraling flow increases the leading edge suction coefficient highlighted by the C p contours in Figure 5, with the maximum suction exceeding -4. The low pressure area established at the head of the sail is 130

6 characterized by low momentum. The lack of momentum at the trailing edge is due to the separation affecting the flow after the suction at the sail draft position. Part of the flow near the clew of the sail is directed downwards, convected into the foot vortex. On the mainsail the flow is completely stalled, with the wall shear stress streamlines showing reversed flow on the majority of the surface, only deviating at the top and the bottom section towards the head and the foot due to the influence of the tip vortices. Unfortunately, the lack of the mast in the computation introduced a large discrepancy between the CFD and the experiment in the first half of the mainsail, with a suction that in reality is not present due to the separation induced by the mast. For this reason the pressure results on the mainsail will not be discussed. 5.2 Pressure Distribution The mean pressure coefficient distribution on the five gennaker sections in Figure 2 are shown in Figure 6 for the experiment, the RANS, and the LES solutions. At the luff of the sail, for the three lowest sections named G1, G2, and G3, the experiment shows a low pressure peak at the leading edge due to the flow separation induced by the sharp leading edge. The pressure recovers straight afterwards, with the C p values just smaller than -1. The RANS result matches the experiment, by predicting the pressure peak followed by the immediate pressure recovery. The LES overpredicts the length of the separation bubble, with the flow reattaching to the surface between 5% and 7.5% of the chord. The pressure distribution inside the bubble follow the distribution on a flat plate at an angle of attack shown by Sampaio et al. [14] and Nava et al. [10], with a initial constant region followed by a short suction peak before the pressure recovery. Figure 5: Three dimensional mean velocity streamlines, and mean pressure coefficient, and wall shear streamlines for the leeward sail surfaces. generated by the head vortex highlighted by the velocity streamlines in Figure 5. The proximity of the mainsail and the gennaker is small enough so that only one large vortex develops around the head of both sails. The large chord of the mainsail and the high flow velocity induced by the curvature of the leeward side of the gennaker produce a vortex characterized by high vorticity, at the center of which there is a low pressure coefficient of approximately -3, which attracts the flow of the leading edge bubble. The other effect of the low pressure at the top of the gennaker is to alter upwards the trailing edge flow at the lowest section, The delayed reattachment of the leading edge separation bubble is due to the lower freestream turbulence intensity found in the LES solution at the model location. The large mesh size in the upstream region of the domain is excessively dissipative and damps the velocity fluctuations introduced at the inlet, so that the free-stream flow at the model only has approximately 0.6% turbulence intensity, compared to 1.8% for the experiment and 2% of the RANS solution. Because of the larger length of the separation bubble, a stronger spiraling flow directed upwards is established, resulting in the overprediction of the suction within the leading edge bubble. At higher sections (G4 and G5) the trend is reversed. In section G4 in Figure 6 the LES matches the experimental data, showing high suction at the leading edge (C p 3.5) followed by a steep pressure recovery, characteristic of the leading edge separation bubble shown in Figure 7. The RANS pressure coefficient only reaches -1.2 at the luff of the sail and then decreases to a steady value of -1 for the rest of the chord, predicting the flow as fully stalled. The different prediction of RANS and LES is due to the ability of the LES to predict the transition in the shear layer [14] downstream 131

7 which shows the turbulent kinetic energy contours, and this is responsible for the curvature of the shear layer that reattaches to the surface between 15% and 20% of the chord. (a) RANS (b) LES Figure 7: Mean velocity contours and streamlines on the section G4. Figure 6: Pressure coefficient distributions at the five gennaker sections: (x) experiments [8], (- -) RANS, ( ) LES. of the leading edge separation. The higher momentum in the shear layer for the LES solution is indicated in Figure 8 For the RANS solution, the tip vortex at the head of the sail is characterized by a larger core and less momentum than the experiment and the LES, because of the large trailing edge flow stall predicted by the RANS. Therefore, the center of the vortex does not reach the low pressure predicted by the LES, but only a value of -1. This is reflected in the pressure distribution on section G5 (Figure 6) where the RANS predicted pressure is approximately 40% of the experimental value. At this section the LES overpredicts the experimental result by approximately 12%, although the poor result could be caused by an excessively short time averaging window (3.5 s vs 90 s) which did not allow capture of long duration fluctuations of the head vortex. Downstream of the maximum camber on sections G1, G2, and G3, the flow separates, with the pressure distribution assuming an approximately constant value until the trailing edge. The LES reproduces the experimental pressure distri- 132

8 (a) RANS (a) RANS (b) LES Figure 8: Contours of the turbulence kinetic energy on the section G4. bution, although it overestimates the C p values on section G3, while RANS is in poor agreement with the experiment and the LES. The RANS constant pressure region extends for more than 50% of the chord and the suction value upstream is underestimated by more than 10%, indicating an early separation of the boundary layer. The velocity streamlines and contours in Figure 10 show the RANS boundary layer separating just after the maximum camber location and creating a large recirculation region characterized by low fluid velocity. The separation of the boundary layer for the LES solution happens at approximately 0.3 c from the trailing edge. The incorrect RANS prediction is attributed to the lack of turbulence generation near the surface in the pressure recovery region. Figure 11 shows the normalized u v Reynolds stress for RANS and LES on sections normal to the surface at maximum suction and in the pressure recovery region. The RANS stress remains constant but the peak moves away from the surface as the boundary layer separates. Conversely, the LES shows a significant increase with the maximum stress at 0.4 c four times larger than the RANS. The turbulence generation in the LES solution is due to the formation of unsteady vortices that are convected downstream that entrain momentum inside the boundary layer, which can sustain the adverse pressure gradient for longer before separation. (b) LES Figure 9: Contours of the pressure coefficients on the gennaker leeward surface and a transversal plane downstream of the head of the gennaker. 5.3 Unsteady Flow Features The periodic formation of unsteady vortices in the LES at the leading and trailing edges is suggested by Figure 12 which shows the instantaneous pressure distribution on sections G3 and G4. At section G3, the C p values fluctuate from downstream of the suction region to almost the trailing edge, with a decreasing wavelength. The pressure fluctuations are the result of vortices which are convected downstream 133

9 (a) RANS (b) LES Figure 10: Mean velocity contours and streamlines on the section G2. Figure 12: Instantaneous pressure coefficient distribution at sections G4 (top) and G3 (bottom). These vortices are organized in rolls visible in Figure 13 which shows the instantaneous tangential wall shear stress, where the red color indicates fluid moving from the luff to the leech and the blue color fluid moving from the leech towards the luff. The rolls are aligned with the draft location (which moves aft at higher gennaker sections) and start downstream of the maximum suction location. The rolls move towards the trailing edge, increase in size as the mean flow slow down and burst into the trailing edge separation. Figure 11: Normalized u v at three sections normal to the surface: (- -) RANS, ( ) LES. by the mean flow, at a decreasing speed, as the flow loses momentum approaching the separation point. The distance between two pressure peaks at section G3 in Figure 12 (those higlighted show a separation of 0.1c and 0.06c between peaks where c is the 1.2 m chord length at section G3) and the mean flow velocity at a distance of the wall equal to the centre of each vortex (approximately 3.4 m/s for the upstream vortex and 2.6 m/s for the downstream one), can be combined to estimate the time between peaks, which is s and 0.03 s respectively, corresponding to frequencies of 28.5 Hz and 33.3 Hz. Figure 14 shows the energy spectrum of the transient lift force calculated by the LES and highlights a peak at approximately 30 Hz, confirming the periodic nature of the vortex mechanism. 134

10 Figure 14: Energy spectrum of the lift coefficient. compared with the LES, due to the completely stalled flow on the leeward side of the mainsail predicted by both solvers. Figure 13: Contours of the instantaneous tangential wall shear stress on the section gennaker, coloured by orientation: ( ) luff-to-leech, ( ) leech-to-luff. The other peak at approximately 50 Hz can be associated with the shedding of vorticity from the leading edge bubble, highlighted by the fluctuating pressure distribution at the leading edge of section G4 in Figure 12. The vortices are generated at the contact region between the detached shear layer and the backflow inside the leading edge bubble, where the LES computes the highest shear stress. The distance between the vortices is shown to be approximately 0.08 c in Figure 12, where c for the section G4 measures 0.81 m. The vortices are convected downstream by the onset flow at a speed of approximately 3.4 m/s, corresponding to a period of s and a frequency of 52 Hz. The transient nature of the LES computation, which allows the computation of the unsteady periodic vortices forming at the leading and trailing edges, improves the prediction of the mean flow field, with increased accuracy in the reproduction of the pressure distribution in comparison with the RANS. The superior predictive ability of LES is summarized in Figure 15 which shows the drive and side force coefficients for the single sails and the overall values, compared with the experiments. The RANS force coefficients are underestimated by approximately 21% and 17% compared to the LES as the result of the poor prediction of the flow separation region. For the mainsail, the differences are smaller, with the RANS drive and side force underestimated by 6% and 2% Overall, the LES overestimates the drive force by 3.5% and underestimates the side force by 2.5% in comparison with the experiment while RANS underestimates them both by 15%. The overestimation of the LES drive force is due to the higher suction predicted on the gennaker leading edge, at the draft and the head of the sail which can be attributed to the incorrect calculation of the free stream turbulence, the coarsening of the mesh towards the centre of the sail and the short timeaveraging window that does not allow the calculation of the low frequency fluctuation of the tip vortices and the wake. The opposite result for RANS is due to the large separation predicted at the trailing edge of the gennaker, which limits the suction on the sail and the low-pressure value inside the head vortex. 6 CONCLUSIONS The ability to predict the gennaker flow has been tested using RANS and LES, with the results validated against published experimental data. The predictions of the gennaker flow field differ significantly. The largest difference is seen at the trailing edge, where the steady solution from RANS can not compute the unsteady vortices generated in the boundary layer that are responsible for the increased turbulence which delays flow separation. The LES is in much better agreement with the experiment, correctly computing the transient turbulent flow field at the trailing edge. The superior ability of LES in reproducing the separation bubble occuring at a sharp leading edge is responsible for the more accurate prediction of the flow at the head of the sail, with the shear layer reattaching to the surface and the pressure distribution matching the experiment. RANS predicts the flow as fully stalled, due to the inability of the 135

11 flow field further with the LES methodology, in order to investigate the possible connection between the leading edge vorticity shedding and the trailing edge vortex rolls, and moreover, the low-frequency fluctuations in the flow field. Finally, the computational effort required by the LES simulation is much larger than the RANS by a factor of ACKNOWLEDGEMENTS (a) Drive force coefficient The authors wish to acknowledge the contribution of NeSI high-performance computing facilities to the results of this research. NZ s national facilities are provided by the NZ escience Infrastructure and funded jointly by NeSI s collaborator institution and through the Ministery of Business, Innovation & Employment s Research Infrastructure programme. URL REFERENCES [1] ANSYS Fluent v.16 User s Guide. ANSYS Inc., Canonsburg, Pennsylvania. (2015) [2] Bot P., Viola I.M., Flay R.G.J., Brett J.S.: Wind-Tunnel Pressure Measurements on Model-Scale Rigid Downwind Sails, Ocean Engineering, v. 90, pp (2014). (b) Side force coefficient Figure 15: Time averaged drive and side force coefficients: ( ) RANS, ( ) LES, ( ) experiment[8]. SST turbulence model to reproduce the turbulent structures developing within the leading edge bubble. The head vortex is strongly influenced by the prediction of the flow at the head of the sail, and because it is a prominent feature of downwind sails aerodynamics, it has a great impact on the overall flow field and the force generation. However, errors are seen in the LES pressure distribution and can be attribuited to the low free-stream turbulence computed, which is due to the excessively large grid spacing in the upstream region which damps the inlet velocity fluctuations. Other sources of error include the coarsening of the mesh in the central part of the sail and the short time duration of the simulation, which does not allow the computation of low frequency flow fluctuations. The more accurate prediction of the flow field by LES led to a small error in the computation of the force coefficients, while the RANS is affected by larger errors. This suggests that the LES methodology is a powerful tool in sailing aerodynamics as it resolves the unsteady, turbulent flow field. It would be of interest to model the gennaker [3] Collie S., Richards P.J., Gerritsen M.: Validation of CFD Methods for Downwind Sail Design, in the proceeding of The 1 st High Performance Yacht Design Conference, Auckland, New Zealand. (2002). [4] Crompton M., Barrett R.: Investigation of the separation bubble formed behind the sharp leading edge of a flat plate at incidence, Institution of Mechanical Engineers, Part G, v. 214, pp (2000). [5] Frohlich J., Mellen C.P.: Transition in LES of Bluff Body Flows and Airfoils, Direct and Large-Eddy Simulation- IV, pp (2001). [6] Hedges K.L., Richards P.J., Mallinson G.D.: Computer Modelling of Downwind Sails, Journal of Wind Engineering and Industrial Aerodynamics, v. 63(1), pp (1996). [7] Lasher W.C., Sonnenmeier J.R.: An Analysis of Practical RANS Simulations for Spinnaker Aerodynamics, Journal of Wind Engineering and Industrial Aerodynamics, v. 96, pp (2008). [8] Lebret C: Photogrammetry and Pressure Measurements on Model Downwind AC33 s Rigid Sails, Yacht Research Unit internal report, Department of mechanical engineering, University of Auckland, New Zealand, (2014). [9] Moore W.,Richards P.J., Mallison G.D.: Wind Tunnel and Computational Modelling of Spinnakers, in the proceeding of The 5 th International Colloquium on Bluff Bodies Aerodynamics and Applications, Ottawa, Canada, (2004). 136

12 [10] Nava S., Cater J., Norris S.: Modelling Leading Edge Separation on a Flat Plate and Yacht Sails using LES, Internationa Journal of Heat and Fluid Flow. (2016). [11] Nava S., Cater J., Norris S.: A Comparison of RANS and LES for Upwind Sailing Aerodynamics, in the proceedings of the 22 nd Chesapeake Sailing Yacht Symposium, Annapolis, Maryland, USA. (2016). [12] Queutey P., Guilmineau E., Wackers J., Visonneau M.: RANS and DES CFD Simulation of Rigid Multiple Sails, in the proceeding of The 5 th High Performance Yacht Design Conference, Auckland, New Zealand. (2015). [13] Sagaut P., Mary I.: Large Eddy Simulation of Flow Around a High Lift Airfoil, Direct and Large-Eddy Simulation-IV, pp (2001). at Trinity College Dublin, Trinity College Cambridge, and the University of London, before joining the University of Auckland. He currently researches a variety of fluid flows including aerodynamics and biological fluids using laser measurement techniques and computational models. S. Norris is a Senior Lecturer in the Department of Mechanical Engineering at the University of Auckland, and is a member of the University s Yacht Research Unit. He has a Masters degree from the Yacht Research Unit, and a PhD from the University of Sydney. His research interests include the development of computational fluids dynamics methodologies, and its application to a number of flows including yacht aerodynamics. [14] Sampaio L.E.B., Rezende A.L.T., Nieckele A. O.: The challenging case of the turbulent flow around a thin plate wind deflector, and its numerical prediction by LES and RANS models, Journal of Wind Engineering and Industrial Aerodynamics, v. 133, pp (2014). [15] Vicariot R.: RANS Modelling of Downwind Yacht Sails, Yacht Research Unit internal report, Department of mechanical engineering, University of Auckland, New Zealand, (2014). [16] Viola I.M., Flay R.G.J.: Sail Pressures from Full-Scale, Wind-Tunnel and Numerical Investigations, Ocean Engineering, v. 38, pp (2011). [17] Viola I.M., Bot P., Riotte M.: Upwind sail aerodynamics: A RANS numerical investigation validated with wind tunnel pressure measurements, International Journal of Heat and Fluid Flow, Vol.39, pp (2013). [18] Viola I.M., Bartesaghi S., Van-Renterghem T., Ponzini R.: Detached Eddy Simulation of a Sailing Yacht Ocean Engineering, v. 90, pp (2014). [19] Viola I.M., Flay R.G.J.: Aerodynamics of Headsails: a Review of Measured Surface Pressure and Expected Flow Fields, in the proceeding of The 5 th High Performance Yacht Design Conference, Auckland, New Zealand. (2015). 8 AUTHORS BIOGRAPHY S. Nava is a PhD candidate at the University of Auckland, where he is carrying out a research on computational methods for sails aerodynamics. He also holds the current position of sail designer at Doyle Sails NZ, where he is involved in the design and computational modelling of racing and superyachts sails. J. Cater is a Senior Lecturer in the Department of Engineering Science at the University of Auckland. He studied for his PhD at Monash University in Melbourne, and has worked 137

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