Computational Analysis of Spray Pre-treatment in Automotive Applications

2020-01-0479

04/14/2020

Event
WCX SAE World Congress Experience
Authors Abstract
Content
The automotive coating industry consists of several processes targeting the reliability and longevity of the manufactured Body-In-White (BIW) with process optimization playing a key role. Pre-treatment of BIW is one of the important aspects and this involves processes in the paint shop and body-in-white shop. The relevance of cleaning every part of the BIW is well known in the industry, and we will focus on the spray wash processes. While the industry currently relies on experiences from previous designs and experimental observations from model studies, this drastically slows down process optimization for new car models. Recent developments in Computer Aided Engineering (CAE) industry has shown capability to perform reliable studies using computer models that speed up processes. The current study focuses on the Computational Fluid Dynamic (CFD) evaluation of spray washing of a BIW using a meshless method known as Smoothed Particle Hydrodynamics (SPH).
The study specifically discusses simulation of a washing process, where a car part is moving through pre-treatment line. Using the Lagrangian based fully meshless SPH method, the fluid dynamic aspects of the problem is simulated. The solver is based on a Predictive-Corrective Incompressible (PCISPH) formulation of SPH, which obtains instantaneous physical properties of the fluids and their impact on the solids. The mass-based domain discretization ensures only lesser computational cost in domain partially filled with fluids. Additionally, algorithms implemented on Graphics Processing Unit (GPU) makes the simulations faster and increases the scope for scalability.
Meta TagsDetails
DOI
https://doi.org/10.4271/2020-01-0479
Pages
8
Citation
Menon, M., Baig, S., and Verma, K., "Computational Analysis of Spray Pre-treatment in Automotive Applications," SAE Technical Paper 2020-01-0479, 2020, https://doi.org/10.4271/2020-01-0479.
Additional Details
Publisher
Published
Apr 14, 2020
Product Code
2020-01-0479
Content Type
Technical Paper
Language
English