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In vehicle aerodynamics analysis considering actual traveling conditions, it is known that the flow around the rotating wheel interferes with the wake of the wheel arch, the flow ejected from the engine room, and the flow under the floor. This significantly affects the aerodynamic drag value. In particular, the fuel consumption measurement method by the World Wide Harmonized Light Vehicle Test Procedure has been carried out since FY 2018 in Japan. A calculation condition on computational fluid dynamics is required to be accompanied with wheel rotation. However, it is not easy to carry out simulations with wheel rotation for a full-vehicle model using a general-purpose method with an unstructured grid or voxel lattice. This method has difficulty in performing calculations faster and easier.

In open-grille vehicle aerodynamics simulation using computational fluid dynamics, in addition to basic flow characteristics, such as turbulent flow with a Reynolds number of several million on the bluff body, it is important to accurately estimate the cooling air flow introduced from the front opening. It is therefore necessary to reproduce the detailed geometry of the entire vehicle including the engine bay as precisely as possible. However, there is a problem of generating a good-quality calculation grid with a small workload. It usually takes several days to a week for the pretreatment process to make the geometry data ‘clean’ or ‘watertight’. The authors proposed a computational method for complex geometries with a hierarchical Cartesian grid and a topology-independent immersed boundary method with dummy cells that discretize the geometry on a cell-by-cell basis and can set an imaginary point arbitrarily.

The applicability of high-performance computing (HPC) to vehicle aerodynamics is presented using a Cartesian grid approach of computational fluid dynamics. Methodology that allows the user to avoid a large amount of manual work in preparing geometry is indispensable in HPC simulation whereas conventional methodologies require much manual work. The new frame work allowing a solver to treat ‘dirty’ computer-aided-design data directly was developed with a modified immersed boundary method. The efficiency of the calculation of the vehicle aerodynamics using HPC is discussed. The validation case of flow with a high Reynolds number around a sphere is presented. The preparation time for the calculation is approximately 10 minutes. The calculation time for flow computation is approximately one-tenth of that of conventional unstructured code.

A simulation framework for vehicle aerodynamics using up to 10 billion fully unstructured cells has been developed on a world-fastest class supercomputer, called the K computer, in Kobe, Japan. The simulation software FrontFlow/red-Aero was fully optimized on the K computer to utilize up to 10,000 processors with tens of thousands of cores. A hybrid parallelization method using MPI among processors and OpenMP among cores inside each processor was adopted. The code was specially tuned for unsteady aerodynamic simulation including large-eddy simulation, and low Mach number approximation was adopted to avoid excessive iterations usually required for the fully incompressible algorithm. The automated mesh refining system was developed to generate unstructured meshes of up to 10 billion cells. In the system, users only generate unstructured meshes in the order of tens of millions of cells directly using commercial preprocessing software.

One of the world's largest unsteady turbulence simulations of flow around a formula car was conducted using Large Eddy Simulation (LES) on the Earth Simulator in Japan. The main objective of our study is to investigate the validity of LES for the assessment of vehicle aerodynamics, as an alternative to a conventional wind tunnel measurement or the Reynolds Averaged Navier-Stokes (RANS) simulation. The aerodynamic forces estimated by LES show good agreement with the wind tunnel data (within several percent!) and various unsteady flow features around the car is visualized, which clearly indicate the effectiveness of large-scale LES in the very near future for the computation of flow around vehicles with complex configurations.