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Corrosion protection is a major topic of research for automotive manufacturers and major suppliers. The most critical parts affected by corrosion are the pillars and cavities. Automotive manufacturers spend a lot of effort to improve protection layers as much as possible to increase the longevity of their products. The Electro-Coat Paint Operation (ELPO) or E-Coat process is key in achieving this goal. Unfortunately, often the electric current does not reach all the areas very well and resulting in undercoating. Also, the consequent baking process might see the ovens not achieving the desired temperatures into the cavities. As a fact it often happens that these areas are exposed by undercoating, and hence undermining the quality of corrosion protection. Some car manufacturers investigate one level more to assure a good quality of corrosion protection also within cavities, by applying wax.

There are many factors which influences the deposition behavior in the e-coating process. In this paper, the basic theory of the thickness simulation based on the Faraday’s law and the classic model for the distribution of the electric potential governing by the diffusion equation are described. The paint experiment needed to derive the parameters involved in the model is discussed. Moreover, a hydrodynamic model based on Nernst-Planck equation is proposed, which includes three mechanisms of electro-migration, diffusion, and convection. Such a model can cover the influence of paint velocity on the thickness simulation. The Lattice Boltzmann method (LBM) is considered as a numerical solver for the hydrodynamic model. The technique of detection of air bubbles is applied to improve the precision of the thickness simulation.

Last several decades of car design were a continuous and slow process. In recent years, due to the electrification of engines, the design approach requires fast adaptation/modification of old technologies to fit the upcoming requirements. Moreover, the new technologies need to be developed from scratch. One of the most important elements that has been radically changing in recent years is the drive transmission system. In order to help the fast development of novel powertrains, the ability to make fast and accurate Computational Fluid Dynamics (CFD) analysis is of high importance. A vast 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 additionally handle the interface or free-surface within computational cells. Promising alternatives to Eulerian methods are Lagrangian approaches which, roughly speaking discretizes the fluid instead of spatial domain.

Autonomous cars already exist, why should anybody these days spend manual time on mesh preparation? This is a task for a machine, not for a human being. This paper shows a one-click way to prepare the mesh for multi-bodies or complex topological objects for CAX application and how to obtain autonomously suitable data cleaning and output qualities of complex multi object geometries. The underlying software is already in use for data cleaning, surface mesh generation and 3D mesh generation: here it prepares a body in white (BIW) starting from a CAD geometry to surface mesh, including data cleaning fully automatically with 5-8 hours computational time on a desktop machine, while requiring less than 15 minutes of manual work. Firstly, during the import process the object gets subdivided and differentiates each part as sheet or solid. Each of these parts are moved to the global coordinate system. The user can review if the input data is the correct one.

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.

Autonomous cars already exist, why should anybody these days spend manual time on mesh preparation? This is a task for a machine, not for a human being. In this session, we will show a one-click way to prepare the mesh for multi-bodies or complex topological objects for 3D printing. The underlying software is already in use for paint shop applications: here it prepares a body in white starting from a CAD geometry fully automatically with 5-8 hours computational time on a desktop machine, while requiring less than 15 minutes of manual work. As an input, tessellated data can be imported from several sources including automatic interfaces allowing to extract the data of multi-bodies from CAD. However, these data are often defective and not manifold. In addition, the describing surface is not represented in an exact way. The only exact information one can rely on at this stage is the position of the vertices of the mesh: they are located directly on the surface.

Simulation tools are becoming more and more popular in the automotive industry since they can significantly reduce the costs required for development of new models. Currently there are many computational fluid dynamics (CFD) tools available on the market and becoming indispensable tools for R&D in many of the automotive applications. However there are some applications which require much effort by highly skilled engineers to prepare the model and impractical level of computation time even using a cluster computer using the conventional CFD tools due to the nature of physics and complexity of a geometry such like dip painting process. Therefore, corrosion protection engineers are striving to find an alternative solution. Another issue is that the main focus of those available CFD tools are problems occurring during the dip paint simulations and they omit problems occurring after the object dips out from the bath, such as retained water or bake drips.

The manual or semi-automatic preparation of triangular and tetrahedral meshes for simulations is a very time-consuming task. The mesh preprocessing software MERGE however masters this task fully automatically and in a considerably shorter timeframe. It takes CAD or STL data as input, repairs invalid meshes and connects them together. The small gaps between the input objects are automatically closed and even glancing intersections can be cut off. The output can be supplied in different variations, either as a mesh that minimizes the number of elements, as a mesh consisting of high-quality surface triangles, or as a conforming tetrahedral mesh, while all of these possibilities are suitable for CFD or CAE simulations. Connecting the meshes of an entire car body can be done in (as little as) a few hours and does not require any manual work.

The development of entire car bodies benefits from simulations, especially if they are performed at an early stage of development because they lower the costs for required car body modifications. This paper focuses on a dip paint simulation and describes the simulation process as an e-coat (electric coating) thickness simulation which considers gas bubbles, drainage and buoyancy forces. This paper points out the advantages of this technology by explaining the theory behind this. A new hydrodynamic method is used which performs about 1000 times faster as standard computational fluid dynamics (CFD) solvers. In addition, this method allows executing the computation on standard desktop machines, i.e. no high performance computer (HPC) is needed. In addition we introduce a simple method to calculate the static buoyancy forces of arbitrary homogeneous objects and a simple model movement of an engine hood induced by buoyancy and drag forces.

The development of entire car bodies benefits from simulations, especially if they are performed at an early stage of development. Simulations lower the costs for required car body modifications. This paper focuses on dip painting simulation and describes the simulation process by detecting badly painted areas and liquid carry overs. A new hydrodynamic method has been benchmarked to CFD and real-life results. Results will be shown together with case examples. The solver is similar to, and as accurate as, standard CFD solvers; it is faster in its computation speed by a factor of at least 1000. The electrophoretic deposition (ELPO) of an entire car body can be simulated overnight with ALSIM. How is this possible? This paper points out the reason for all of this.

Simulations need high quality mesh representations as input which are often manually prepared. The fully automatic mesh preprocessor software MERGE avoids this time consuming task. The software CAD or STL data as input, repairs invalid meshes and merges the input objects together. Small gaps between the input objects are automatically closed. The output surface mesh can be supplied in two different flavors, either as a mesh that minimizes the number of elements or as a mesh consisting of high quality triangles, ready for 3D meshing. A closed and connected surface mesh of an entire car body can be prepared overnight in a fully automatic manner.