F1 Aerodynamics, Pressure Based vs Density Based Simulation, ANSYS Fluent
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In this project, aerodynamic coefficients of a Formula One (F1) car by two different solvers of pressure based and density based, has been studied.
This product includes a Mesh file and a comprehensive Training Movie.
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Description
Project Description
In this project, aerodynamic coefficients of a Formula One (F1) car by two different solvers of pressure based and density based, has been studied, at a speed of 108 meters per second at a lateral angle of zero degrees (actually a straight path). This velocity at the ground level is equivalent to Mach number approximately 0.32. We know this area from Mach number is the transition zone from incompressible to compressible flow, so on this geometry, the drag coefficient is investigated using two pressure based and density based solvers is discussed.
Introduction
In a dynamic simulation, quasi-experimental aerodynamic coefficients are used. These coefficients are obtained by quasi-experimental software. The coefficients obtained in these software have many errors. To achieve more accurate coefficients, numerical simulation by computational fluid dynamics is used, and the coefficients obtained by semi-experimental simulation software are calibrated using CFD coefficients. In this research, aerodynamic coefficients have been done using numerical simulations and Fluent commercial software.
Geometry & Mesh
In this section, the geometry of the F1 car is examined.
Since the flow characteristics also affects the upstream in the subsonic stream, the computational domain is selected as follows. Downstream, due to the vortices falling behind the geometry to achieve the appropriate residues in solving the larger computational domain, is considered.
In numerical CFD simulations, one of the most significant and time-consuming steps in solving is mesh generation. The better the mesh quality, the other solving steps will proceed with more confidence.
Due to the dimensions of the problem and the desired geometry, an structured mesh has been used for the boundary layer, and an unstructured mesh has been used for other parts of the solution domain. To generate a mesh in ICEM software, you must first determine the element size in different parts according to the dimensions of the problem and the type of flow and geometry of the object under study.
The elements number is 3429165. The following figure shows the grid on the middle page.
Solver Setting
The issue has been investigated numerically using Fluent commercial software. This problem is solved in steady mode using pressure-based and density-based methods.
Fluent software has been used to solve the governing equations numerically. This chapter considers flow conditions, type of selected boundaries, type of solvent, and flow discretization methods.
Material Properties
In this research, the air has been used as a fluid. The following table shows the air properties extracted from the Fluent software database.
Amount (units) | Fluid properties (air) |
1006.43 (J/kg.k) | Specific heat |
0.0242(w/m.k) | Thermal conductivity |
Boundary Condition
One of the most influential variables in the numerical solution process is boundary conditions. For this purpose, we use different boundary conditions in the computational domain. below we introduce these conditions.
– Wall
– Velocity inlet
– pressure-far-field
models (F1) | ||
K&W | viscous model | |
sst | K&W model | |
boundary conditions (F1) | ||
Pressure-far-field | Pressure-far-field | |
0.32 | Mach number | |
0 Pascal | gauge pressure | |
wall | wall of body | |
stationary wall | wall motion | |
solution methods (F1) | ||
couple | pressure velocity coupling | |
second order | pressure | spatial discretization |
second order upwind | momentum | |
first order upwind | turbulent kinetic energy | |
first order upwind | turbulent dissipation rate | |
initialization (car) | ||
standard | initialization method | |
0 (Pa) | gauge pressure | |
108 (m/s) | z-velocity | |
0 (m/s) | y-velocity , x-velocity |
Solution Convergence
In computational fluid dynamics, we use iterative solutions. These iterative solutions start with the initial values and continue solving until they reach the convergence criteria or the number of steps specified by the user. We define different criteria for the convergence of the problem. One of the most widely used criteria for determining the convergence of a problem is residual values. Residuals are the sum of the differences in the values calculated on all cells in the current and previous time steps. They are calculated in each iteration, and the solution continues until its value is less than the criterion specified by the user. We usually recommend to reduce the residual values by 3 or 4 times.
As shown in the figure below, all residuals, including velocities and parameters of the turbulence model, are smaller than the standard criterion.
Examining the status of the results during the iterative solution (monitoring) and the residuals reaching the convergence criterion can help decide whether the solution converges. According to the problem, the convergence process of the drag force has been studied to ensure the convergence of the problem. The non-noticeable change in the desired quantity indicates the convergence of the numerical solution.
Results
In this chapter, we present and analyze the results related to simulation in two parts, qualitative and quantitative. The quality of the flow around the F1 body has been studied using boundary diagrams. We also investigate the current problem using drag forces.
The following tables show the drag force and the number of iterations of the solution for convergence for the two modes. The forces are in Newtons.
Drag Force (N) | Number of iterations | |
pressure-based | 673 | 300 |
density-based | 685 | 2500 |
As shown in the table above, the drag force is almost the same for both density based and pressure based solvers, i.e., for this Mach and this geometry, the compressible flow CFD simulation applying Density- Based solver has almost no effect on the solution results. The difference is that convergence in the pressure based solver requires fewer iterations of the solution, so it is more cost-effective in terms of time . So, the lower computational cost and accurate results in comparison with Density Based results of the Pressure Based, make this solver more appropriate for this CFD simulation.
The following are the contours related to this research for both solvers.
There are a Mesh file and a comprehensive Training Movie that presents how to solve the problem and extract all desired results.
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