The International Vehicle Aerodynamics Conference PDF
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International Vehicle Aerodynamics Conference 2014 Organising Committee Adrian Gaylard Jaguar Land Rover Jeff Howell Tata Motors European Technical Centre Martin Jones MIRA Geoff Le Good G L Aerodynamics Robert Lewis TotalSim Ltd Martin Passmore Loughborough University David Sims-Williams Durham University International Vehicle Aerodynamics Conference 2014 HOLYWELL PARK, LOUGHBOROUGH, UK 14–15 OCTOBER 2014 AMSTERDAM (cid:2)BOSTON (cid:2)CAMBRIDGE (cid:2)HEIDELBERG (cid:2)LONDON NEW YORK (cid:2)OXFORD (cid:2)PARIS (cid:2)SAN DIEGO SAN FRANCISCO (cid:2)SINGAPORE (cid:2)SYDNEY (cid:2)TOKYO Woodhead Publishing is an imprint of Elsevier Woodhead Publishing is an imprint of Elsevier Ltd 80 High Street, Sawston, Cambridge CB22 3HJ, UK 225 Wyman Street, Waltham, MA 02451, USA Langford Lane, Kidlington, OX5 1GB, UK First published 2014, Woodhead Publishing © The author(s) and/or their employer(s) unless otherwise stated, 2014 The authors have asserted their moral rights. 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Alternatively you can submit your request online by visiting the Elsevier web site at http://elsevier.com/locate/permissions, and selecting Obtaining permission to use Elsevier material. Trademark notice: Product or corporate names may be trademarks or registered trade- marks, and are used only for identification and explanation, without intent to infringe. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library. Library of Congress Control Number: 2014951744 ISBN 978-0-08-100199-8 (print) ISBN 978-0-08-100245-2 (online) For information on all Woodhead Publishing publications visit our website at http://store.elsevier.com Produced from electronic copy supplied by authors. Printed in the UK and USA. Printed in the UK by 4edge Ltd, Hockley, Essex. Real world drag coefficient – is it wind averaged drag? S Windsor Aerodynamics, Jaguar Land Rover, UK ABSTRACT Drag reduction of road vehicles continues to be the holy grail of aerodynamicists with renewed importance of aerodynamics as OEMs strive to reduce CO and 2 improve fuel economy. New legislation such as Worldwide harmonized Light vehicles Test Procedure (WLTP) is increasing the emphasis on real world boundary conditions (with moving ground wind tunnel testing) and vehicle configurations, in an effort to obtain more realistic fuel consumption figures. However, WLTP in common with all other drive cycles used in the automotive sector is based on zero- yaw aerodynamic measurements. This implicitly assumes conditions vehicles rarely see: the onset wind vector is at zero yaw, the atmosphere is still and undisturbed by other road users. This paper will show that many modern cars (particularly low drag saloon cars) have noticeable drag coefficient (C ) minima at zero yaw, with some having up to D 10% increase in C for relatively small yaw angle changes around zero. The D concept of wind averaged drag ((cid:1829)(cid:3005)(cid:3024)) will be discussed as a means of assessing real world aerodynamic performance. Three methods of calculating C are investigated DW and the MIRA method is preferred for application to passenger cars. This demonstrates that including yaw angle effects via (cid:1829) provides a significantly (cid:3005)(cid:3024) different perspective on the aerodynamic contribution to fuel economy. 1. INTRODUCTION Modern wind tunnels, with their moving ground and quasi turbulence generators, are being designed and built to achieve an experimental set up that is getting closer and closer to the real world. But the wind tunnel still only partially simulates on road conditions and it is very difficult to quantify each of the known errors that exist between the wind tunnel and the real world as in Figure 1. Generally, an automotive wind tunnel provides an environment with low levels of turbulence, a floor boundary layer approaching 0 mm and most often used at zero yaw. However, road vehicles spend very little time being driven in conditions where the ambient wind is either stationary and/or at 0° yaw to the vehicle’s direction and in low levels of turbulence, yet the de facto drag coefficient (C ) is measured (and D quoted) assuming the ambient wind is 0 kph and/or at 0° to the vehicle’s direction. Measuring a vehicle’s C with this assumption is both repeatable and quick to D compute in both the wind tunnel and CFD codes but the aerodynamicist knows that in the “real world” ambient conditions and vehicle set-up must, or at least should, be taken into account when calculating the vehicle’s true C . D _______________________________________ 3 © The author(s) and/or their employer(s), 2014 Figure 1 A vehicle in its real environment (based on Hucho [1]) Automotive wind tunnels were designed to be able to yaw the vehicle to simulate crosswinds but even now, nearly 60 years later, it is uncommon for any data of C D at yaw to be published or quoted. Yet it is the change in C with yaw angle that is D of particular interest in understanding its effect on real world fuel economy as well as stability etc. The concept of wind averaged drag takes a range of C at yaw and D calculates the vehicle’s drag coefficient in the real world environment; essentially it “characterizes” a vehicle’s aerodynamic drag behaviour. 2. NOTATION The relative velocity and wind direction of a vehicle travelling at velocity V with the V ambient wind velocity V is shown in Figure 2 below. The resultant velocity is W termed V at a yaw angle of Ψ. RES Figure 2 Relative Wind Velocity and Direction V = vehicle velocity (kph) V V = wind velocity (kph) W V = relative wind velocity (kph) RES Ѳ = wind direction relative to the vehicle Ψ = relative yaw angle C = drag coefficient at yaw angle Ψ DΨ C = wind averaged drag coefficient for wind velocity V DW W 4 3. WIND TUNNEL TEST RESULTS A range of SUV, saloon and sports cars (as shown in Table 1) were tested in the MIRA Full Scale Wind Tunnel (FSWT) to measure their aerodynamic characteristics. All the vehicles were tested at standard loading conditions, and measurements were taken over the yaw range ±5° in 1° increments and ±30° in 5° increments. The test results were left unedited and so positive and negative yaw data may not be symmetrical. Table 1 Vehicle Data Sets SUV and 2 Box Vehicles Audi Q3 SUV Saloons and Sports Cars Audi Q5 SUV Audi A3 5-Door Wagon Audi Q7 SUV Audi A5 Sportback Saloon BMW X1 SAV Audi A6 Saloon BMW X3 SUV Audi A8 Saloon BMW X5 SUV BMW 320d Eff Dyn Saloon BMW X6 SAV BMW 535 Saloon Ford Grand C-Max (for shape) MPV BMW 740 Saloon Ford S-Max MPV Ford Mondeo 4-Door Saloon Honda CR-V SAV Jaguar F-Type Coupe Sports Hyundai iX35 SUV Jaguar F-Type Convertible Sports Jeep Compass SUV Jaguar XF 10MY Saloon Kia Sorrento SUV Jaguar XF Sportbrake Wagon Land Rover Discovery 3 SUV Jaguar XJ 10MY Saloon Land Rover Freelander 2 SUV Jaguar XK Coupe 10MY Sports Lexus RX-450h SUV Jaguar XK Convertible 10MY Sports Mazda CX-5 SUV Lexus GS-450h Saloon Mercedes B-Class (for shape) 2-Box Mercedes C250 Saloon Mercedes ML SUV Mercedes E350 Saloon Nissan Qashqai SUV Mercedes S350 Saloon Porsche Cayenne GTS SUV Porsche Panamera Saloon Range Rover Evoque 5-Door SUV Vauxhall Insignia 08MY Saloon Range Rover 10MY SUV Vauxhall Insignia 12MY Saloon Range Rover 12MY SUV Range Rover LWB 14MY SUV Range Rover Sport 10MY SUV Range Rover Sport 14MY SUV Volvo XC60 SUV VW Touareg SUV The test results from these vehicles are anonymised in all of the subsequent Figures and Tables. It was noted that many vehicles, particularly low drag saloons, had a noticeable drag coefficient minima at 0° yaw (see Figure 3). This yaw response is not as common on SUVs but still evident on some vehicles. The percentage increase in C D with yaw from straight ahead for a selection of saloons and SUVs is shown in Figures 3 and 4 respectively. In many cases the vehicles which have low C at 0° yaw are far more likely to have D a greater increase in C with yaw. What is unclear though is whether this yaw D response is as a result of overall vehicle drag reduction or a particular body feature, for example front bumper planform. Front bumper planform can be used very 5 effectively to control flow separation at small yaw angles [1] but this can be disadvantageous at large yaw angles. Figure 3 shows that for Saloons A to F, drag coefficient increases between 5 and 11% over the range 0-5°. Saloon G shows that this trend is not typical for all vehicles in this sector, with an increase of less than 5% over the same yaw angle. Figure 3 Saloon Car Yaw Response Results in Figure 4 show that SUVs are less likely to show the exaggerated minima of the saloon vehicles, but where this response is evident the magnitude is much less. In Figure 4, SUVs A and B show marked minima at 0° but only have a 6-7% rise in C at 5° yaw with the other vehicles having more parabolic yaw responses. D In the case of SUVs A and B, they are not vehicles with class leading low C at 0° D yaw. 6 Figure 4 SUV Yaw Response 4. WIND AVERAGED DRAG METHODS Having identified that some vehicles have large increases in C at small yaw angles D it would be useful to be able to quantify the effect of the wind on C for a given D road speed and to compare vehicle to vehicle. Wind averaged drag (C ) is one DW approach that allows this comparison to be made. There are 3 commonly known and documented methods for calculating ‘wind averaged’ drag coefficient: MIRA [2], SAE J1252 [3] and TRRL Report 392 [4] and these documents should be referred to for full derivation of their methods. Each of these methods is very similar in their general calculation but they differ in how a weighting is applied to the results as outlined below. The MIRA method (see Appendix 1) assumes a fixed vehicle velocity and then uses 7 different ambient wind velocities from 2kph - 26kph (1.2mph – 16.2mph) with a weighting factor applied to each velocity. The wind averaged drag coefficient is then calculated using 133 data points and averaged over the range ±180°. A worked example of the MIRA method can be found in Appendix 4, and in this case study the vehicle measured C = 0.272 in the wind tunnel and C = 0.289 by D DW calculation. The SAE J1252 method (see Appendix 2) assumes a fixed vehicle velocity and a fixed ambient wind velocity of 11.3kph (7mph). In this method it is assumed that the wind approaches the vehicle with equal probability from any direction. C is DW then calculated using 12 data points, 6 in both yaw directions. 7 The TRRL method (see Appendix 3) is similar to the SAE method in that it too assumes a fixed vehicle velocity and a fixed ambient wind velocity of 11.3kph (7mph). However the TRRL method applies a weighting factor to the wind direction over the range ±180°. C is then calculated using 12 data points, 6 in both yaw DW directions. All of the methods use the following simplified derivation in their calculation: The relative wind velocity is given by equation (1): V V 2V 22VV Cos (1) RES V W V W And the yaw angle by equation (2): V sin tan1 W (2) VV VWcos The wind averaged drag coefficient C can now be defined, assuming longitudinal DW vehicle symmetry, by equation (3) where C is determined using wind tunnel data. DΨ 2 1 V C C RES d (3) Dw 0 DV V The integral in equation (3) cannot obviously, or simply, be evaluated by finding an indefinite integral so numerical techniques were employed in its calculation, namely the mid-point and trapezium rules. A synopsis of the calculations can be found in the Appendix. 5. WIND AVERAGED DRAG RESULTS The wind averaged drag coefficient (C ) for all of the vehicles was calculated using DW the three methods described above and compared to the C as measured at the D MIRA FSWT, and the results are shown in Table 2. A fixed vehicle speed of 70 mph, maximum speed for cars on UK motorways, was used on the calculations. It should be noted that many of the vehicles were not “eco” or low drag variants; many were fitted with high powered engines and sports kit with wide tyres. The SAE method [3] for calculating C is the simplest of the three methods, in that DW a notional average wind speed is assumed to be equally probable from all directions relative to the vehicle. The MIRA method [2] gives a weighting to the wind velocity and the TRRL method [4] gives a weighting to the wind direction distribution for the UK. It is of no surprise that the SAE and TRRL methods give essentially the same result as they use a similar approach. The SAE method averages 12 data points to calculate the C , and the TRRL method uses the same 12 data points but then DW uses a weighted average of 6 averages and thereby avoiding Simpson’s Paradox. The MIRA method is the more comprehensive of the three methods as it uses a much larger data set of 19 yaw angles and 7 wind speeds, and therefore is the preferred method in this paper to calculate C . DW 8
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