Model development for large scale intake manifold optimization using CFD

Abstract: Continued improvement of combustion engines to operate with lower fuel usage and lower harmful emissions leads to more and more complex engine designs. Computational fluid dynamics (CFD) is a method which helps predicting engine performance, reducing the reliance on engine tests. CFD can be used to optimise a geometry using a design of experiments (DOE). This study focuses on developing a simulation method to use for such large scale optimisation of air intake manifolds. The main focus in this study was the difference in flow quantities such as swirl number and pressure drop between the different cylinders for a given manifold. Four different simulation approaches were tested: one steady-state, two transient and one transient with a moving mesh. These simulation methods were tested on five different geometries based on a six cylinder, 13L spark ignited (Otto) combustion engine and a six cylinder, 13L compression ignited (Diesel) combustion engine. The five geometries were compared using the different simulation methods, with the main goal of determining if the different simulation methods provide the same optimal geometry. The most complex simulation model, the transient simulation with moving mesh, was chosen as main reference case in absence of experimental results. Results of this mock design of experiments show that the different simulation approaches do not perform consistently enough to recommend using any of the tested methods in further optimisation studies. While the various methods showed significantly different results when comparing the differences in flow parameters between cylinders, using the steady-state or transient methods to predict the flow parameters in a single cylinder is a viable approach. Multiple possible causes for the inconsistent results are discussed, chief among which is the chosen grid generation approach and selected convergence criteria. A recommendation is made to improve the reference, moving mesh, case using scale resolving models.