J70 SAIL AND RIG TUNE AERODYNAMIC STUDY

5 th High Performance Yacht Design Conference Auckland, 10-12 March, 2015 J70 SAIL AND RIG TUNE AERODYNAMIC STUDY Connor Anderson 1, connora@stanford.edu Tyler Doyle 2, tyler@doylecfd.com Duncan Swain 3, duncan@doylecfd.com Margot Gerritsen 4, margot.gerritsen@stanford.edu Abstract. Optimal sail design and rig tune of a J70 are studied using RANS CFD, FEA and a low cost data acquisition system. In one design racing sail design and rig tune are the biggest variables that can be changed from one boat to another and figuring out the correct balance between the two is critical to good performance. One design sails are challenging to model computationally because they deform under sailing loads and are sensitive to rig tune. Using accurate flying shapes in CFD models is key to picking out the slight aerodynamic differences in sail and rig setup. We develop a system to monitor sail flying shapes, trim, rig load and performance data based on cheap hardware components and open-source data processing libraries. The sail shape and rig data are used to drive the geometry used in the aerodynamic RANS CFD simulations. A series of J70 sails and rig setups with known performance characteristics are modeled and tested on the water, and the proposed camera system was able to differentiate between different rig tunes and recreate them in CAD software. CFD force data and post processing information is studied to understand what are the optimal aerodynamic characteristics and how are they effected by sail design and rig tune. NOMENCLATURE AWA AWS F d F h Apparent wind angle Apparent wind speed Driving Force Heeling Force 1. INTRODUCTION In one design racing small differences in rig tune and sail design can lead to large differences in relative performance. Both rig tune and sail design effect a sails flying shape and thus the aerodynamic performance. In order to computationally study one design aerodynamics the system used must be able to correctly model the flying shape and be able to detect small differences is geometry. Our approach to correctly modelling the sail flying shapes is to use a data acquisition system that records sail flying shapes along with performance data. By recording sail flying shapes along with performance data we are able to identify sailing states that are of interest and then create a CFD simulations that accurately represent the aerodynamics of the boat in that condition. Another option for modelling flying shapes is to use fluid-structure interaction simulations to predict flying shapes. One design sails are a challenge to model computationally with FSI because their flying shape is sensitive to rig tune and deforms under sailing loads. In the future FSI capability will be tested for use with this sytem and the data used in the paper can serve as a validation. To accurately capture small changes in geometry as well as turbulent flow effects we use steady state RANS CFD to model the flow. The goal of this project is to develop a low cost data acquisition system capable of feeding flying shapes into a RANS CFD system and to test the sensitivity of the system to small changes is sail design, rig setup and sheeting. Modern grand prix race boats often have a wide array of sensors that keep track of performance, loads and sail flying shapes however these systems are expensive and often not accessible to one design development programs. The data acquisition system described in this paper is based on cheap sensor hardware, a central processing minicomputer running a version of linux and open source data acquisition and processing software. At every step in the design of the data acquisition system we tried to find the most economical solution that would meet the accuracy and durability criteria needed to make the system function on a small one design boat. To capture the sail flying shapes a deck mounted camera system picks up sail shape stripes and for manual digitization. This process builds off of the VSPARS system proposed by David Le Pelley [5]. The headstay sag and mast bend are calculated by keeping track of the inboard end of the shapes stripes. The flying shapes of the sails are then rebuilt by lofting the shape stripe curves into sail surfaces. There are a number of other approaches to capturing sail flying shape data such as integrating fibre optic threads into sails or by using radio signal emitters placed along lines in the sail. The deck mounted camera system was chosen because it provides a good balance between accuracy, cost and ease of use. A J70 was selected to test the system on because one of the authors actively campaigns one and has access to a large number of different sail designs. 1 Undergraduate, Department of Mechanical Engineering, Stanford University 2 President, Doyle CFD 3 Undergraduate, Department of Mechanical Engineering, Tufts University 4 Professor, Institute for Computational and Mathematical Engineering, Stanford University 60

2. J70 CLASS BACKGROUND The J70 (Figure 1) was introduced two years ago and quickly became one of the hottest one design fleets in the world. The J70 s cost, performance and class rules have attracted a wide range of sailors from amateur to pros. The class has become extremely competitive and like with most one design classes the biggest difference between those winning and those not is small differences in sail design and rig tune along with sailing skill. Figure 1 J70 Dimensions and Features As with any new one design class J70 sail designs and rig tune setups are still evolving. The J70 is a tricky boat to design upwind sails for because although the boat is under powered in light conditions it quickly becomes overpowered above 12 knots true wind speed. Because you are only allowed to race with only one set of sails that means you have to design the sails to go across a large performance range. In light air you want deep powerful sails while in heavy air you want flat efficient sails. Understanding the aerodynamics of the sails across the range of sailing conditions and rig setups is critical to being able to make good design decisions. 3. SENSORS AND EXPERIMENTAL DATA ACQUISITION 3.1 System Overview The goal of the sensor system was to create a low cost and compact data acquisition system that could capture real world conditions to be used in CFD simulations. The variables that were collected were wind speed (AWS), wind direction (AWA), boat heel, boat pitch, magnetic boat heading, boom angle, rudder angle, GPS location, speed, and true heading. Additionally, multiple cameras were used to capture pictures of the sails, which could then be used to in conjunction with shape stripes of known width to generate a three dimensional model of the sails (see Section 4). This data was taken at a low sampling rate, approximately 1Hz, as the goal of the system was to capture steady state loading and positioning of sensors on the boat, not rapid fluctuations or vibrations. Images were captured at 0.1Hz to prevent running out of hard drive capacity. To obtain this data a prototype system consisting of two micro-controllers, an Arduino Mega 2560 and a Raspberry Pi Model B, was used. These two microcontrollers allowed the system to split up the tasks of collecting the data, processing it, displaying the output, and cataloging it for further analysis. The Arduino's primary function was to collect the analog and digital signals from the sensors, convert them to usable values and then export the data to the Raspberry Pi via USB serial connection. The Raspberry Pi functioned as a graphical display system that output the data in real time and saved the data to a hard drive. The Pi also was used to control image capture for the sails. These two controllers were housed in a waterproof module, which also housed the power supply, display module, user interface devices and additional circuitry for the sensors. 3.2 Sensor Setup The experimental data acquisition system had a variety of sensors that were all linked to the central module containing the micro-controllers and display with physical connections. Using hard-wire connections required only one power supply for powering all the sensors and electronics of the system. The central module was located in the bow of J-70 directly in front of the inside mast support. This central location for the main sensor system was ideal for waterproofing, as well as for minimizing the distance between sensors and control circuitry. For determining boom and rudder angle, low friction rotary potentiometers measured the angular displacement of the boom and rudder from their center-line position. Each potentiometer was placed in such a way that the mast of the potentiometer would directly align with the vertical pin of the goose-neck or with the hinge point of the rudder. This positioning enabled the potentiometers to rotate in direct proportion to the rudder and boom to which the masts of the potentiometers were connected via short linkages. The wind speed and direction were determined by a traditional wind vane and cup anemometer that were affixed to the masthead of the J- 70. Heel, pitch and vertical motion of the boat were determined by a triple axis accelerometer that was rigidly attached inside the main control module of the system. The central module in turn was also fitted snugly to the flat portion of the V-berth where it was placed for data acquisition. This acted as a rigid connection between accelerometer module and the boat, to detect any motion that the boat was also undergoing as well as the change in relative magnetic reference of the boat's heading. The GPS module was also placed in the central module for housing the electronics in an identical manner to the accelerometer, but with an additional, external antenna for increased precision. The final components of the data acquisition system, the cameras, were high resolution (3000 2250) web-cams that were modified to fit into custom waterproof camera domes that so that they could be placed on the deck of the boat and not catch any sheets or be damaged from 61

spray or waves going over the boat. Three cameras were used to obtain images of the sails. One was placed in the middle of the foredeck looking directly upwards towards the masthead in a position so that it could capture images of the sails on both tacks. The other two cameras were placed opposite each of other directly behind the shroud mounting plates aimed at the mainsail, capturing images of both sides of the sail in upwind sailing. Although it was not considered in this study, future improvements on this system will begin to include correction for the perspective effects of the image. To ensure that the results were not invalidated by distortion, the 3D generated sails were compared to images, with the camera and focal point at the exact same locations as on the boat. The depths (forward, mid and aft), the position of max camber and twist were all within a 5 percent of the 3D value and often much closer. 4. Automated 3D Sail Generation 4.1 Stripe Identification Prior to on-the-water testing, horizontal blue stripes of thick tape were attached at the 25%, 50%, 75%, and 90% intervals up the height of the main sail, and at the 3%, 25%, 75%, head, and foot of the jib. These stripes show up well even in bright conditions, and are the foundation of reference geometry for creating 3D CAD models of the sail (See Figures 2 and 3). Figure 3: J70 Jib with Camera Attached to Deck Cameras connected to the sensors allow for comparisons between data points at a given time stamp, and the jib and main photos associated with that moment in time. Curves can then be fit, by hand, to each of the stripes along the sail (see Figure 4). All the parameters for these curves (stripe length, camber, front depth, back depth, and stripe coordinates) are then loaded into a data file for processing using the Rhino 3D CAD software. Figure 4: Computer Generated Curves from Sail Stripes 4.2 CAD Automation Figure 2: View of J70 Mainsail from Boom Mounted Camera with Stripes for Image Processing Using a Rhino 3D python script, the parameters can be adapted to create a series of curves/splines that can be lofted for a complete sail. Specifically, the known length of the stripe and assumption that each stripe lies in a horizontal plane allows for conversion between a 2- dimensional image and 3-dimensional splines. This process converts the X,Y, and Z coordinates from the image pixels into global X,Y, and Z coordinates using a process similar to the V-SPARS approach outlined by Le Pelley [5]. The stripes are then aligned, with the main sail stripe s leading edges defined along an unbent mast and the jib sail stripe s leading edges defined along its halyard. Lastly, the stripes are offset by a mast bend angle and a luff sag factor, with luffsagfactor = 1 is equal to the design luff curve. At this point, all the stripes can be lofted together to form one continuous sail surface. 62

This entire process s automation allows sails to be quickly digitized and transformed into 3D models in just minutes. If the process of creating sail parameters could be automated, the total time required would be a matter of seconds. 4.3 Numerical Testing Using the method outlined, we were able to model over 30 different rig configurations based on live data from on the water testing. The differences between a slight change in rig settings were easily picked up by the camera vision system, as shown in the following figures. Figure 5: Base and Minus 2 Rig Settings Although the wind speeds were not precisely uniform during each of these photos, they were close enough to visually represent the differences in rig loading for each case. Because we acquired pictures at 0.1Hz and data at 1Hz, we had plenty of data to use for each AWS and AWA desired. 5. UPWIND COMPUTATION 5.1 Defining a Mesh Achieving grid convergence when defining a mesh for a sail case is important for trusting CFD results. One should be able to increase the number of surface refinements, doubling the cell density on both the sails and mast with no significant difference in driving force, heeling force, or any other values. For this study, we used grid settings based on similar upwind studies from Doyle CFD. In this way, we did not have to perform an entire grid convergence study, but simply adjusted the mesh settings to better suit the geometry and conditions of the J70 sails. We varied the number of refinements and used the driving force term at each level of refinement to see how that parameter changed. A change greater than 5% is considered significant. All meshing was done through OpenFOAM's snappyhexmesh utility, which takes surface geometries in steriolithography (STL) format, and iteratively generates and adapts 3 dimensional meshes to the shape of the surface. After testing 8 different mesh settings with identical geometries and initial conditions, we decided to use a 8 surface refinements and 9 edge refinements on all input geometries (jib, main, and mast). The boundary box for this mesh had a base cell size of 3.4m x 4m x 2.7m, with each level of refinement dividing a unit cell into 8 identical cells. Additionally, we added a refinement box around the sail set, with a length of 8m (the height of the sail) towards the wake (see Figure 7). This entire region contained 4 refinements (cell size 21.3cm x 25.0cm x 16.9cm), and gave better resolution for the turbulent flow of the wake region. This mesh produced roughly 3.5 million cells, which, using 32 cores of a Intel Xeon E5-2680 v2 processor, took less than one hour to complete a case. This allowed for simultaneous computation and a quick turnaround for simulation results. Increasing the mesh to 9 surface refinements greatly increased computation time, but changed driving force by 3%, which was not greater than the 5% that was considered significant. Figure 7: Sails with Refinement Box Figure 6: Tight to Loose Sheeting 63

5.2 CFD Case Background Three jib designs were tested holding the main design constant. The section shapes of the jib designs varied in maximum camber, maximum camber location, back camber and twist profile. The jib designs represent a progression of designs driven by a trial and error testing process. Knowledge gained in the trial and error testing can be compared with the differences seen in the flow fields and surface loading from the RANS simulations yielding physical insight into the on the water observations about their relative performance. The sail sets were tested at a variety of apparent wind speeds and rig tunes. The J70 has a deck stepped rig and swept back spreaders which means the mast bend and head stay sag are directly related to rig tension. In order to simplify keeping track of rig tune J70 sailors often define their rig tension relative to a base setting. The base setting is usually chosen to be the desired rig tune in 10-12 knots of true wind speed. Rig setting is then defined is steps of turnbuckle turns either tighter or looser from this base setting. The difference between having your rig setup well and not well in a given condition is often only one to two settings. For a given apparent wind speed some of the jibs were tested across a range of 3 rig settings to identify the flying shapes changes. These rig settings are denoted by base, minus2 (for a two-turns looser rig), and plus3 (for a three-turns tighter rig). 6. RESULTS 6.1 Rig Settings The target result of this project was to test the sensitivity of the system to small changes is sail design, rig setup and sheeting. Ideally, there should be noticeable differences both in the flying geometries and driving forces under various settings. As shown in Section 4, the camera system was able to pick up on these geometry changes. The hope was that CFD cases set up under the exact same conditions as the boat would give insight into which settings outperform others. setting outperformed the driving force of the base setting by about 5%-10%. It is worth mentioning that heeling force also plays a large part at 7 knots, so an increased driving force does not necessarily correlate to an increased boat speed. Future improvements of this system will involve using a velocity prediction program for the J70 to more accurately relate the forces on the water and the simulated forces. This will yield more data on the advantages of rig settings in different sailing states. 6.2 Sheeting Settings Figure 9: Sheeting Comparison In Figure 9, various sheetings are compared at a range of apparent wind angles, with AWS = 7knts (See Figure 6 for geometric comparison). According to our CFD results, the medium sheeting setting optimizes the driving force under this low-wind condition. Once again, we see that our data acquisition system yields geometric differences from a change in sheeting. 6.3 Jib Comparison Lastly, this data acquisition system can be used to detect changes in jib designs when rig and sheeting settings are held constant. Over a variety of apparent wind speeds, certain jibs can outperform others, which can help sailors make the best sail choises on race day. In Figure 10 (below), the J3 jib achieves 3%-5% better driving force under low wind conditions, with a 28 degree AWA providing a much better VMG. Since the minus2 setting outperformed the base setting at these conditions, all sails were compared at that rig setting, with medium sheeting. Figure 8: Rig Settings Comparison In Figure 8, the drive force allowed by two different sail geometries are shown, each comparing two different rig settings, shown in Figure 5. For both sails, the minus2 rig Figure 10: Jib Comparison at 7 Knots AWS 64

These same 3 jibs are compared at AWS = 15 knots (Figure 11). Once again, the 28 degree AWA is closer to the ideal point of sail. Also, while the J1+ jib was comparable to the others at 7 knots, it lost its competitivity at 15 knots. Neglecting heeling moments, the J5 slightly outperforms the J3 at this condition, however the difference is small enough to where sailing skill would make a larger difference than sail choice in a race. Aero/Hydronautics of Sailing, Redondo Beach, CA. 1971. 3. Graf, Kai, and Olaf Müller. "Photogrammetric Investigation of the Flying Shape of Spinnakers in a Twisted Flow Wind Tunnel." Proceedings 19th Chesapeake Sailing Yacht Symposium, Chesapeake VA, March 2009. 4. Larsson, Lars, Rolf Eliasson, and Michal Orych. Principles of yacht design. A&C Black, 2014. 5. Le Pelley, D. J., and O. Modral. "V-Spars: A Combined Sail and Rig Shape Recognition System Using Imaging Techniques." Proc. 3rd High Performance Yacht Design Conference Auckland, New Zealand, 2-4 December 2008. Figure 11: Jib Comparison at 15 Knots AWS 7. CONCLUSIONS In one design racing, a data system such as the one proposed could change the way sailors compete. The low cost of the system will allow for average sailor to have access to the system not just grand prix race programs. Future research and sailing with this system could help identify important sailing states for the J70 or any other one design vessel and help to optimize rig tune and sail design at those states. With more aerodynamic research, the ideal configurations for a variety of states could be explored and studied in greater detail. As the process from images on the water to CFD results is streamlined, sailors will be able to get quick feedback on how their setup affects their sails flying shape and aerodynamic performance. With automated sail shape digitizing flying shapes can be displayed in real time assisting in rig setup. Using this system in combination with on the water testing can lead to valuable physical insight into why one sail or one rig tune out performs another. 6. Y. Tahara, Y. Masuyama, T. Fukasawa and M. Katori (2012). Sail Performance Analysis of Sailing Yachts by Numerical Calculations and Experiments, Fluid Dynamics, Computational Modeling and Applications, Dr. L. Hector Juarez (Ed.), ISBN: 978-953-51-0052-2, InTech 7. Zhang, Zhengyou. "A flexible new technique for camera calibration." Pattern Analysis and Machine Intelligence, IEEE Transactions on 22.11 (2000): 1330-1334 Acknowledgements Special thanks to Bradford Knight for his support and mentorship throughout this project. References 1. Collie, Stephen. Application of computational fluid dynamics to two-dimensional downwind sail flows. PhD thesis, University of Auckland, 2006. 2. Gentry, Arvel E. "The aerodynamics of sail interaction." Proc. 3rd AIAA Symposium on the 65