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EURECA 2013 - Calculation of Aerodynamic Drag of Human Being in Various Positions Calculation of Aerodynamic Drag of Human Being in Various Positions Mun Hon Koo*, Abdulkareem Sh. Mahdi Al-Obaidi Department of Mechanical Engineering, school of Engineering, Taylor’s University, Malaysia, *Corresponding author: [email protected] Abstract— This paper studies the aerodynamic drag of human being in different positions through numerical simulation using CFD with different turbulence models. The investigation considers 4 positions namely (standing, sitting, supine and squatting) which affect aerodynamic drag. Standing has the highest drag value while supine has the lowest value. The numerical simulation was carried out using ANSYS FLUENT and compared with published experimental results. Aerodynamic drag studies can be applied into sports field related applications like cycling and running where positions optimising are carried out to reduce drag and hence to perform better during the competition. 2. Methods 2.1. Theoretical Analysis According to Hoerner [4], the total aerodynamic drag of human body can be classified into 2 components which are: πΆπΆ π·π·π‘π‘πππ‘π‘ππππ =πΆπΆ π·π·πππππππππ‘π‘ππππππ +πΆπΆ π·π·πππππππ π π π π’π’ππππ Where πΆπΆ π·π·πππππππ π π π π’π’ππππ is pressure drag coefficient and πΆπΆ π·π·πππππππππ‘π‘ππππππ is friction drag coefficient. Pressure drag is formed from the distribution of forces normal .to the human body surface [5]. The effects of viscosity of the moving fluid (air) may contribute to the rising value of pressure drag. Drag that is directly due to wall shear stress can be knows as friction drag as it is formed due to the frictional effect. The friction drag is the component of the wall shear force in the direction toward the flow, and it depends on the body surface area and the magnitude of the wall shear stress. Keywords— Turbulence Models, Drag Coefficient, Human Being, Different Positions, CFD 1. Introduction 2.2. Numerical Simulation The performance of human being in various positions is strongly affected by the resistance they experience in which the resistance consists of aerodynamic drag. Aerodynamic drag, a resistance force that acts upon a body moving through fluid like air and that is opposite to the direction of motion of the body [1, 2]. In sports field that competing with speed, aerodynamic drag is one of the important factors. The lower the value of drag coefficient, the lower the aerodynamic drag of the positions. The main idea of this paper is to investigate the effects of aerodynamic drag of human in different position theoretically and numerically. The total drag is majorly formed by pressure drag and friction drag due to the skin friction of the human body. In speed performing sports like cycling, cyclist often optimize the positions to reduce their drag by conducting experiment using wind tunnel. The measurement of human body flow can be said is time consuming and field tests are very difficult to set up. Even the experts in the related field can only optimize the positions to reduce drag by trial and error Therefore, computational fluid dynamic (CFD) is one of the alternative techniques to be carried out to investigate the aerodynamic drag. However, since the accuracy of numerical simulation method needs to be verified, published experimental data are used to compare and to justify the CFD results. Usually the maximum allowable errors in predicting the drag can range between10-12%. The wind tunnel experiment investigation of various positions of human was published by Schmitt [3]. The results can be used to validate the values obtain from CFD simulation. Fig. 1 shows the 4 body positions to be studies during the research. The numerical simulation in the present work is carried out using ANSYS FLUENT 14.0. According to Chowdhurry [6], the human body parts can be simplified due to the configuration and size of human body is way too complex. Therefore, a simplified model of human body with the average height and frontal area was drawn using SolidWorks as shown in Fig. 2 (a). The model is then imported into the virtual wind tunnel for computational studies as shown in Fig. 2 (b) 2 (a) 2 (b) Fig. 2 (a) Simplified Human Body (b) computational domain and boundary condition. Steady 3D Reynolds-averaged Navier-Stokes (RANS) is used in combination with turbulence models such as k-ε and k-ω with the lowReynolds number modeling (LRNM) together is used. The four turbulence model to be carried out in the study is standard k- ε model, realizable k-ω model, standard k-ω model and lastly shear stress transport (SST) k-ω model. There is no universally proved that which turbulence model is the most accurate flow for every study case [7]. Only steady flow is performed as the studies of flow of transient can be very complicated and hard to predict. In this study case, the velocity inlet will be set at 10 m/s which is almost equivalent to 38 km/h as this input boundary condition is the normal speed for a casual cycling speed. Fig.1 (a) Standing (b) Sitting (c) Squatting (d) Supine. [4] 99 EURECA 2013 - Calculation of Aerodynamic Drag of Human Being in Various Positions 3. Results 3.1 Comparison of Introductory Test limitations when solving the same condition study; therefore, it may produce the different final values. The value of drag coefficient is dimensionless. Fig. 3 shows the values of drag coefficient of various positions using various turbulence models. Table 1. Percentage Error between Published Experimental Data and Numerical Simulation Data Percentage of Error (%) realizable standard k-Ο΅ k-ω Positions standard k-Ο΅ SST k-ω Standing 3.3 0.008 11.1 3.3 Sitting 5.1 2.5 3.8 0.05 Supine 44.4 40 66.7 40 Squatting 48.9 40 35.6 37.8 5. Conclusion and Recommendations Fig. 3 Drag coefficient of Various Positions with Turbulence Models. 4. Discussion Figure 3 shows that the supine position gives the lowest drag coefficient while the standing position gives the highest. The drag coefficient of various positions from lowest to highest value in ascending: supine, squatting, sitting and standing. It also shows that the lesser projected frontal area can greatly reduce the drag coefficient. Since the input boundaries of velocity inlet, air viscosity, air density and flow direction are the same for 4 positions; the only factor to affect the changes in drag coefficient is the frontal area. In others words, a less frontal area means the body expose lesser external flow to the wind. As the standing position has the highest projected frontal area, it exerts more forces due to the pressure and skin friction drag on the frontal area on the human opposite the wind direction. While the supine position has the lowest projected frontal area because the exposed area in this position is less and thus the skin friction drag is less as well. In other hand, the research also carries out using different turbulence models. For squatting and supine positions, the values between the four turbulence models are very close. However, it has huge differences between the experimental data and numerical simulation data. This may due to the simplified human body model as the projected frontal area is not the same as the real human sample carry out by Schmitt [3]. The geometry of the upper body of the simplified human body is too wide and therefore it has higher drag coefficient. The difference of percentage errors for supine and squatting is relatively high, from 35% to 67%. For standing and sitting positions, the values between the experimental data and numerical simulation data are very close, the maximum error differences is only approximately 11%, which is still in the accepted range. The difference values of each turbulence model may occur due to the input method of the computational domain and boundary condition at the set-up. Each turbulence model has their own 100 In conclusion, the standing position gives the highest value of drag coefficient while supine gives the lowest drag coefficient. To improve the accuracy, the modeling of the human body cannot be too simplified as the sometimes the real body do not generate the same projected frontal area as the simplified model. The input method and meshing may affect the accuracy too. Further study on these options need to be carried out more critically. However, although the accuracy of the wind tunnel and CFD are not totally the same, CFD still can be used to study in high speed application sports like running, swimming, etc. to optimize the body configuration as it gives detailed flow for every single study. In the other hand, the results presented in the current study are part of the ongoing study. Investigation on apply velocities are considered such as average of walking speed, running, skiing and etc. Angle of attack is also one of the parametric to be considered in the overall studies. Acknowledgment Appreciation to Taylor’s University School of Engineering for the funding and equipment support of this project. 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