Redesigning of motorcycle helmet for improved air ventilation using numerical simulations

Abstract

The flow prediction using computational fluid dynamics (CFD) in a given flow geometry becomes a complex issue when the flow is in the transition region. The motivation for this study is to find the best turbulence model for prediction of air flow in the air gap of helmet. In CFD if the flow for a given geometry is known to be turbulent then any standard turbulence model such as the k–ε model does a reasonable job of predicting the mean flow quantities. As a first step this study aims to find the optimum turbulence model for near transition flows for pipe flow problem which is a benchmark flow problem. Using simple flow problems such as pipe flow we show that even in this simple case if the flow is laminar and a turbulence model is used in the CFD simulations, the results with most turbulence models are erroneous. For a model to perform well under a laminar condition, it should predict a laminar flow and nearly zero eddy viscosity. Results of CFD simulations for pipe flows indicate that Spalart Allmaras (S-A) model shows these trends. Its relevance to helmet is because when we carry out CFD simulations for a helmet the air flow domain is large and almost all of it includes the region outside the helmet, where the flow is known to be turbulent. The boundary conditions are set on this outer domain. The gap between the helmet and the head of rider is very small and it is not a standard geometry. Since we do not know the velocities at the inlet of this thin air gap, we do not know a priori if the flow is in turbulent or laminar or in the transition region in the air gap. So it becomes imperative to work with a turbulence model that will perform well in laminar as well as turbulent flow conditions. The numerical experiment on simple pipe flow shows that S-A model performs better than the standard two equation models when the flow is in the laminar or transition regime and performs almost the same as the other two equation models in the turbulent regime. Having established this, we then try to match the results of the S-A model with experimental results of flow in 3-dimensional head-helmet arrangement and found that for 3-dimensional flows the S-A model does a better job than the k–ε models. S-A Model is then used to predict the best arrangement of vents for improving ventilation in helmet. It is found that ventilation in helmet with central and side vent is better than helmet with only central vent or side vent.