ALSIM Sense

Physics

Multi-Physics

Consider multiple simultaneous physical phenomena

Heat Transfer

Convection, radiation and conductivity in solids and fluids

Moving Boundaries

Rotating and moving objects

Non-Newtonian Fluids

Viscoelastic, thixotropic so as non-Newtonian viscosity materials

Free Surface

Retain volumetric stabilities

Turbulence

Use methods to predict high Reynolds numbers accurately

GPU-Accelerated Meshless CFD Methods for Solving Engineering Problems in the Automotive Industry

Efficient modelling of complex multi-phase fluid-flows is one of the most common engineering challenges nowadays. The majority of the commonly used CFD solvers are based on Eulerian approaches (grid-based). These methods are, in general, efficient with some drawbacks, e.g. it is necessary to handle additionally the location of the interface or free-surface within computational cells. Very promising alternatives to the Eulerian methods are Lagrangian approaches which, roughly speaking, discretize fluid instead of the domain. One of the most common methods of this kind is the Smoothed Particle Hydrodynamics (SPH) method, a fully Lagrangian, particle-based approach for fluid-flow simulations. One of its main advantages, over the Eulerian techniques, is no need for a numerical grid. Consequently, there is no necessity to handle the interface shape because it is directly obtained from the set of computational particles. Due to this, there is no additional numerical diffusion related to the interface handling. Thus, the SPH method is increasingly used for hydro-engineering and geophysical applications involving free-surfaces and multi-phase flows. One disadvantage of the SPH method over the grid-based approaches is the numerical efficiency. However, in most of the cases involving complex geometries, the human time needed to create computational grids can be so long, that it becomes more time- and cost-efficient to perform calculations using SPH. Furthermore, in recent years new techniques allowing numerical simulations to be performed using Graphics Processing Units (GPU) have been developed. Since the SPH method is easy to write in a parallel manner, we decided to create our SPH simulation framework using Nvidia CUDA technology – a parallel computing platform and programming model developed to use GPU devices for general purpose processing. In the present work, we discuss the potential advantages and disadvantages of using the SPH method for solving typical problems arising in the automotive industry.

 

Download the paper, which reveals the techniques enabling the above-described functionalities here.