Rapid Predictions of Particle Deposition Using rCFD

Sammanfattning

Computational Fluid Dynamics (CFD) is widely used within the automotive industryto evaluate designs during early development stages. However, simulationsinvolving turbulent flows and particle deposition are computationally expensive. RecurrenceComputational Fluid Dynamics (rCFD) is a method that aims to reducesimulation time by utilizing recurring flow behaviour. The aim of this thesis is toimplement and investigate rCFD in Simcenter STAR-CCM+ within an industriallyrelevant workflow. The method is first applied to a cylinder and then tested on amore complex geometry; a Volvo EX40 mirror. A method for generating a recurrencepath and performing particle tracking is developed using STAR-CCM+, Javaand Python.Results show that rCFD can be successfully implemented in STAR-CCM+, reducingcomputational time for particle simulations, while maintaining acceptable accuracy.Furthermore, the accuracy of the method is influenced by Reynolds numbers. In conclusion,rCFD shows promising potential for reducing computational cost, enablingmore efficient contamination analysis in automotive applications.