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Electrostatic Deposition Solver Technology

The Numerical Approach:

Electro-potential diffusion equation is solved to predict the Electro-potential distribution in the paint materials. Electro-current is derived by the Electro-potential gap on the surface and by the Electro-resistance due to the paint film. The growth ratio of the film thickness is estimated by the correlations which are a function of electric currency on the surface. The temperature effects on the film growth ratio have been taken into account. This code is now available for the process in which body is first dipped into the pool of paint materials then electric potential is loaded.

The Analytical model:

Electric potential is represented by the diffusion equation. The time derivative terms is assumed to be zero because it is very small. The Electric wall boundary condition is treated as follows;

For anode: Electric potential value is specified as a function of time.

For cathode: Electric potential value is set to zero with resistance parameters of the paint film as a function of the paint materials and film thickness.

For other surfaces : adiabatic is assumed.

The Numerical Procedure:

Finite volume based unstructured mesh approach is used.

Mesh type includes Hexa, Tetra, Prism, Pyramid.

Interpolation between the nodes is based on a multidimensional polynomial in order to achieve better accuracy.

Numerical procedure are based on point Gauss-Seidel methodology.

Algebraic multi-grid approach is used for better convergence.

Verification Cases:

Case A: Simple Geometry:

A simple geometry shown in Figure 1 is dipped into paint.
Figure 2 shows the mesh used for the computations.

Figure 3 shows the boundary condition used for this case.

Figure 4 shows the paint deposition thickness on plate faces B and C.

Figure 5 represents the comparison of the results from ED with experimental data. The paint film thickness is very accurately predicted in the range between 7 micron and 15 micron.

Figure 1: Geometry of the simple shape experiment and Close up of cathode plates

Figure 2: Computational Domain and the mesh

Figure 3: Electro-potential Boundary Condition

Figure 4: Computation results on faces B and C

Figure 5: Comparison of results with experimental data

Case B: Auto Center Pillar Paint Application

Geometry shown in Figure 1 is used for this case. The mesh used for the surrounding box geometry is represented by the red box. Figure 2 show the results of the computation. The colors represents paint thickness.

Figure 1: Geometry and Computational Domain

Figure 2: Results

	Electrostatic Deposition Solver Technology
	The Numerical Approach: Electro-potential diffusion equation is solved to predict the Electro-potential distribution in the paint materials. Electro-current is derived by the Electro-potential gap on the surface and by the Electro-resistance due to the paint film. The growth ratio of the film thickness is estimated by the correlations which are a function of electric currency on the surface. The temperature effects on the film growth ratio have been taken into account. This code is now available for the process in which body is first dipped into the pool of paint materials then electric potential is loaded. The Analytical model: Electric potential is represented by the diffusion equation. The time derivative terms is assumed to be zero because it is very small. The Electric wall boundary condition is treated as follows; For anode: Electric potential value is specified as a function of time. For cathode: Electric potential value is set to zero with resistance parameters of the paint film as a function of the paint materials and film thickness. For other surfaces : adiabatic is assumed. The Numerical Procedure: Finite volume based unstructured mesh approach is used. Mesh type includes Hexa, Tetra, Prism, Pyramid. Interpolation between the nodes is based on a multidimensional polynomial in order to achieve better accuracy. Numerical procedure are based on point Gauss-Seidel methodology. Algebraic multi-grid approach is used for better convergence. Verification Cases:
	Case A: Simple Geometry:
	A simple geometry shown in Figure 1 is dipped into paint. Figure 2 shows the mesh used for the computations. Figure 3 shows the boundary condition used for this case. Figure 4 shows the paint deposition thickness on plate faces B and C. Figure 5 represents the comparison of the results from ED with experimental data. The paint film thickness is very accurately predicted in the range between 7 micron and 15 micron.	Figure 1: Geometry of the simple shape experiment and Close up of cathode plates
	Figure 2: Computational Domain and the mesh Figure 3: Electro-potential Boundary Condition	Figure 4: Computation results on faces B and C Figure 5: Comparison of results with experimental data


	Case B: Auto Center Pillar Paint Application

	Geometry shown in Figure 1 is used for this case. The mesh used for the surrounding box geometry is represented by the red box. Figure 2 show the results of the computation. The colors represents paint thickness.

	Figure 1: Geometry and Computational Domain	Figure 2: Results