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Information about UNSTEADY AERODYNAMIC SIMULATION OF A RENAULT TWINGO VEHICLE Bordei S,...

In the present work an unsteady aerodynamic simulation on a Renault TWINGO vehicle is presented. This numerical experiment was carried out to study the external aerodynamic forces on one hand, and to predict the mean flow rates on the heat exchangers on the other hand. All this information is used to further predict other important quantities for the final vehicle design as for example: fuel consumption, dynamic stability and performance of the heat exchangers and vehicle homologation. The full aerodynamic simulation has been done using a well known CFD tool in the automotive industry namely PowerFLOW

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Section 3 is devoted to numerical results for a Renault TWINGO vehicle. Finally, concluding remarks are given in Section 4. 2. SIMULATION PROCESS Lattice Boltzmann method is playing a dominant role in the computational fluid dynamics community [5]. These discrete-velocity models are based on a special discretization of macroscopic kinetic equations, i.e., by constructing simple kinetic models, incorporating the essential physics of microscopic processes and applying novel numerical discretizations on these kinetic formulations. Clearly, the discrete-velocity models are based on the Boltzmann equation and kinetic theory rather than Navier-Stokes equation and continuum theory. In addition to theoretical generality, kinetic methods may have computational and numerical advantages, because the Boltzmann equation is a first-order linear partial differential equation (PDE) as opposed to Navier-Stokes equation, a second-order nonlinear PDE. It is beyond this paper to present the strategy adopted within PowerFLOW. For the mathematical modeling, numerical schemes and other internal features we refer the reader to [4], [6], [8], [9]. CATIA CAD Model Ansa Surface Grid Power Case Case Setup PowerFLOW Discretizer Volume Grid The PowerFLOW Decomposer Domain decomposition for a parallel computation The PowerFLOW Solver Numerical Solution of the flow Equation PowerViz Visualisation/ Results Fig. 1. Simulation Process

Next, we present the simulation process. As is shown in Figure 1, the simulation process is divided in the following steps: the retrieval of the TWINGO CAD data from the Renault data base, geometry clean up, surface mesh generation, setup of the initial and boundary conditions, volume discretization (done automatically by PowerFLOW), numerical simulation (automatic parallel memory and CPU decomposition) and at the end interpretation of the results for specific needs. The preprocessing step is made according to internal Renault specifications and demands. Having a cleaned geometry the surface grid is created using high quality triangles, an essential step to producing accurate results and speed up the whole convergence of the simulation. As an example in figure 2 is presented the MAP algorithm [1], [2] used as much as possible in the generation of the surface mesh. Fig 2. MAP Algorithm Now, having the surface mesh, we can proceed to the set up of the initial and boundary conditions together with other important parameters of the automatic volume mesh generation. A view of different resolution zones around the TWINGO vehicle can be seen in Figure 3. Once the surface mesh is obtained and also the whole set up of the case is made, PowerFLOW will generate automatically the volume mesh and proceeds to the numerical solution. The visualization of the results is done with another EXA tool called PowerViz [2]. Fig 3. Simulation Domain

3. NUMERICAL RESULTS In the following we give numerical results for one TWINGO vehicle design. As previously said, these results are used to study the aerodynamical forces and flow velocities on the heat exchangers thus to assess the end design convergence. As initial conditions we use standard air properties at sea level. The inlet flow boundary conditions correspond to 165 km/h and for the outflow boundary we impose the static pressure. Fig 4. Streamlines on the longitudinal plane at the center of the vehicle Table 1. Numerical results Cx = 0.326 Cz = -0.01 SCx = 0.663 m2 SCz = -0.019 m2 SCx_wake = 0.071 m2 CzAR = 0.075 In Table 1 are summarized the main aerodynamical coefficients for this test, namely the drag coefficient Cx, the lift coefficient Cz, the energy loss due to eddies in the wake SCx_wake, and finally, the rear lift coefficient CzAR, respectively. Furthermore, S represents the projected frontal area of the vehicle. Fig. 5. Streamlines in the horizontal plane at mirror height

Figure 4 shows the streamlines on the longitudinal plane at the vehicle center, and in Figure 5 it is presented the streamlines on the horizontal plane cut at the mirrors high. The instability of the wake is at its limits, being very closed to the stable wake. To find out information about the drag in the fluid domain we use the following equation [3]: 2 v 2 w 2 u SCx = ∫ (1 − Cpi ) dA − ∫ 1 − dA + ∫ + dA ; V V A A A V (1) where: A represents the aria of a plane perpendicular to the flow direction, u, v, w are the velocity components, Cpi is the total pressure coefficient defined as: Cpi = pi − p∞ ρ∞ ⋅V 2 . 2 (2) In the Cpi formula represents the upstream velocity, ρ the upstream density, p∞ the ∞ total upstream pressure, and pi the total pressure on the surface, respectively. The first term in equation 1 represents the total pressure loss, the second one is the longitudinal velocity loss, and the third one is the loss of energy due to eddies, respectively. To have a look of the flow inside the engine compartment, we plot in Figure 6 the velocity vectors at a vertical plane cut at the middle of the vehicle. V Fig. 6. Velocity vectors in the engine compartment on a vertical plane at the middle of the vehicle

The efficiency of the heat exchangers is realized by quantifying the mass flow rates at the frontal air inlets. 4. CONCLUSION In the present paper, we studied the air flow around a Renault TWINGO automobile in order to predict aerodynamic forces acting on it together with other important parameters used by different Renault entities. These types of studies are new for the RTR center and for the Romanian automotive industry. The whole CFD simulation is done with the PowerFLOW software package, a widely used CFD code in the automotive industry. Having many automatic features it allows to decrease the computational time and also the number of working hours in order to set up a complete model. The presented computational test demonstrates the capability of PowerFLOW to deliver high accuracy results for drag in a relatively short period of time. The lift coefficient calculation is still a challenge, because the turbulence model in PowerFLOW models the boundary layer as fully turbulent and it does not attempt to model the transition point [4]. To conclude, these sorts of simulations lead to a better fuel economy, higher top speeds, and last but not least, less CO2 emissions, making the decision to buy a TWINGO very attractive from a financial and also from an environmental point of view. REFERENCES [1] ANSA User’s Guide, Part I, Printed in Greece – January 2008. [2] PowerFLOW User’s Guide, Exa Corporation 2006. [3] Wolf-Heinrich Hucho et al.” Aerodynamics of Road Vehicles”, Butterworth, Feb 1987. [4] Dominik Hermann, “A Study of the suitability of PowerFLOW as an Educational Engineering Design Tool for the Undergraduates Students”, http://spot.colorado.edu/~maute/PowerFlow/Tutorials.pdf, January 2008. [5] S.Chen, G.D. Doolen, “Lattice Boltzmann methods for fluid flows”, Ann. Rev. Fluid Mech. 30, 329-364, 1998. [6] Hudong Chen, Chris Teixeira, and Kim Molving, “Digital Physics Approach to Computational Fluid Dynamics: Some Basic Theoretical Features”, International Journal of Modern Physics C, Vol. 8, No. 4, 675-684, 1997. [7] PowerViz User’s Guide, Exa Corporation, 2006. [8] Hudong Chen, “Volumetric formulation of the Lattice Boltzmann method for fluid dynamics: Basic concept“, Physical Review, Volume 58, No. 3, 3955-3963, 1998. [9] Mehtab M., Cristopher M. Teixeira, “Two equation turbulence modeling with the Lattice Boltzmann method”, ASME PVP Division Conference, Boston MA, august 1-5, 1999. [10] EXA webpage: www.exa.com.

The efficiency of the heat exchangers is realized by quantifying the mass flow rates at the frontal air inlets. 4. CONCLUSION In the present paper, we studied the air flow around a Renault TWINGO automobile in order to predict aerodynamic forces acting on it together with other important parameters used by different Renault entities. These types of studies are new for the RTR center and for the Romanian automotive industry. The whole CFD simulation is done with the PowerFLOW software package, a widely used CFD code in the automotive industry. Having many automatic features it allows to decrease the computational time and also the number of working hours in order to set up a complete model. The presented computational test demonstrates the capability of PowerFLOW to deliver high accuracy results for drag in a relatively short period of time. The lift coefficient calculation is still a challenge, because the turbulence model in PowerFLOW models the boundary layer as fully turbulent and it does not attempt to model the transition point [4]. To conclude, these sorts of simulations lead to a better fuel economy, higher top speeds, and last but not least, less CO2 emissions, making the decision to buy a TWINGO very attractive from a financial and also from an environmental point of view. REFERENCES [1] ANSA User’s Guide, Part I, Printed in Greece – January 2008. [2] PowerFLOW User’s Guide, Exa Corporation 2006. [3] Wolf-Heinrich Hucho et al.” Aerodynamics of Road Vehicles”, Butterworth, Feb 1987. [4] Dominik Hermann, “A Study of the suitability of PowerFLOW as an Educational Engineering Design Tool for the Undergraduates Students”, http://spot.colorado.edu/~maute/PowerFlow/Tutorials.pdf, January 2008. [5] S.Chen, G.D. Doolen, “Lattice Boltzmann methods for fluid flows”, Ann. Rev. Fluid Mech. 30, 329-364, 1998. [6] Hudong Chen, Chris Teixeira, and Kim Molving, “Digital Physics Approach to Computational Fluid Dynamics: Some Basic Theoretical Features”, International Journal of Modern Physics C, Vol. 8, No. 4, 675-684, 1997. [7] PowerViz User’s Guide, Exa Corporation, 2006. [8] Hudong Chen, “Volumetric formulation of the Lattice Boltzmann method for fluid dynamics: Basic concept“, Physical Review, Volume 58, No. 3, 3955-3963, 1998. [9] Mehtab M., Cristopher M. Teixeira, “Two equation turbulence modeling with the Lattice Boltzmann method”, ASME PVP Division Conference, Boston MA, august 1-5, 1999. [10] EXA webpage: www.exa.com.

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