## Formula SAE teams aim for the podium with CFD

Budding F1 car designers & engineers here in Australia may be getting excited in the build-up to the first race of the F1 season with the Australian F1 Grand Prix being held in Melbourne next week (March 14-17), but many of them might also have another important car race in the back of their minds: the 2013 Formula SAE (FSAE) competition, which is the world’s largest student engineering design competition. Starting in 1979 but really gaining in popularity here during the past 15 years, Formula SAE invites highly-motivated engineering students from leading universities around the world to design, manufacture, test and race their own single-seat racecar. Each car is judged for dynamic performance including acceleration, autocross, endurance, fuel economy as well as other important engineering and business-related metrics such as cost, marketing and design philosophy. We've previously covered the impact of CFD technology on Formula 1 racecar design, but it is clear that CFD technology provides just as much benefit to the leading Formula SAE teams. In Australia, LEAP is proud to be closely associated with many of the local Formula SAE teams, including Monash Motorsport and Team Swinburne FSAE. In particular we'd like to recognise the passion and success of the Monash Motorsport team, who together with Team Leader Scott Wordley have won the past 4 Australasian FSAE titles, and are now ranked 2nd globally (out of 510 graded university teams)! CFD has formed a pivotal contribution to the design and testing of the aerodynamic package designed for the most recent Monash FSAE car (shown left, image courtesy Monash Motorsport & Mitchell Stafford), which incorporates imposing front and rear wings and a clever floor diffuser. In any racecar design, competing design goals set the scene for a constant battle to provide maximum downforce for superior braking and cornering performance, without sacrificing raw speed due to increased aerodynamic drag. Inspired by Formula 1 and refined using CFD, one of the 2013 car's secret weapons is a drag-reduction system (DRS) that automatically changes the angle of attack of the main wings at a certain speed to reduce drag when the car approaches top speed down the straight. Despite its competitive nature, our observation at LEAP is that Formula SAE is also a remarkably close-knit community as evidenced when Monash Motorsport have generously hosted other teams in their workshop and also given other teams access to their world-leading wind tunnel facilities. In conjunction with Monash Motorsport, LEAP Australia is preparing to host a special workshop in April covering the use of ANSYS CFD and FEA software for Formula SAE car design. The 3-day workshop "DESIGN TO WIN" will be held April 2nd-4th at Monash Clayton campus and students from all Formula SAE teams...

## (Part 2) 10 Useful Tips on selecting the most appropriate multiphase flow CFD models

As we discussed in our previous post, the first step when tackling a multiphase CFD problem is to identify the key characteristics of your physical system. Once you've done this (using our checklist if you are still new to multiphase CFD), you can begin to make informed decisions on what multiphase modelling approaches to use. We've compiled the following guidelines based on the decades of experience that LEAP has developed while helping customers in Australia and New Zealand to solve multiphase CFD problems, particularly companies and researchers in the minerals, process and energy industries: [1] If your problem involves a distinct free surface between two fluids (typically liquids), then the "Free surface" model in CFX or "Volume of Fluid / VOF" model in Fluent should be selected. Both of these methods allow an interface to be solved in steady-state (if it achieves an equilibrium state) or tracked over time in a transient simulation. [2] If your system involves a dilute system of droplets or particles (maximum volume fractions less that ~5%) and you need to track typical trajectories to follow physical processes (such as drying, evaporation, combustion etc.), then you need to use a Lagrangian approach: this is termed the Discrete Particle Model (DPM) in Fluent & the Particle Transport model in CFX. Both codes have an extensive range of in-built models related to the particle physics, so we encourage you to review these options in the manual before you start and contact LEAP if you have specific questions. [3] If your Stokes number is small, then the particles will quickly reach equilibrium with the fluid flow and travel at their terminal velocity. In this case, the Mixture model in Fluent or the Algebraic Slip Model (ASM) in CFX are good choices for a balance of accuracy and speed. The reason that these models greatly reduce computational time is that they only solve a single momentum equation and the other velocities are obtained by calculating the particle slip velocity. [4] If your Stokes number is larger, then an Eulerian model will be needed. An Eulerian multiphase model will solve a separate velocity field for each phase, which is the most general approach and allows complete freedom as to the behaviour of each phase within your domain. [5] If you have solid particles present, then you will need to understand the maximum packing density for your system (incorporating particle shape and size distribution), and then decide how you are going to enforce it. If the packing limit of your particles is not likely to be reached (or is unimportant to your simulation), then the Eulerian Granular models can be used which are based on solids pressure models and kinetic...

## Tips & Tricks: Turbulence Part 2 - Wall Functions and Y+ requirements

Previously we have discussed the importance of an inflation layer mesh and how to implement one easily in ANSYS Meshing. We also touched upon the concept of mesh y+ values and how we can estimate them during the inflation meshing process. In other posts, we also discuss the different turbulence models and eddy simulation methods available to ANSYS CFD users. In today's post, we'll talk in more detail about y+ values apply to the most commonly used turbulence models. From our earlier discussions, we now understand that the placement of the first node in our near-wall inflation mesh is very important. The y+ value is a non-dimensional distance (based on local cell fluid velocity) from the wall to the first mesh node, as you can see in the image below. To use a wall function approach for a particular turbulence model with confidence, we need to ensure that our y+ values are within a certain range. Looking at the image above, we need to be careful to ensure that our y+ values are not so large that the first node falls outside the boundary layer region. If this happens, then the Wall Functions used by our turbulence model may incorrectly calculate the flow properties at this first calculation point which will introduce errors into our pressure drop and velocity results. The upper range of applicability will vary depending on the flow physics and the extent of the boundary layer profile. For instance, flows with very high Reynolds numbers (typically aircraft, ships, etc) will experience a logarithmic boundary layer that extends to several thousand y+ units, whereas low Reynolds number flows such as turbine blades may have an upper limit as little as 100 y+ units. In practice, this means that the use of wall functions for these class of flows should be avoided as their use will limit the overall number of mesh nodes that can be sensibly placed within the boundary layer. In general, it is recommended that you endeavour to place sufficient inflation layer cells within the boundary layer, rather than simply focusing on achieving any particular y+ value. This will be covered in detail in a future post In addition to the concern about having a mesh with y+ values that are too large, you need to be aware that if the y+ value is too low then the first calculation point will be placed in the viscous sublayer (logarithmic) flow region and the Wall Functions will also be outside their validity (below about y+ < 11). You can imagine that this would become an issue if a mesh intended to be used with wall functions is then refined near the wall. Fortunately, the use of...

## Tips & Tricks: Turbulence Part 1 - Introduction to Turbulence Modelling

We will now focus on Turbulence Modelling, which is a critical area for any engineer involved with industrial CFD. There are a number of different approaches so it is important that you have solid grounding in this area to enable you to choose the appropriate model for your simulation requirements. It is worth noting that in August 2012, LEAP will be hosting Dr. Florian Menter to run a series of Advanced Turbulence Training courses in Melbourne, Sydney and Perth. Dr. Menter is a world recognised expert in turbulence modelling, and more information on his visit to Australia can be found here. The ANSYS CFD Solvers solve the Navier Stokes and conservation equations, but as direct solutions are not possible to resolve for any flows of an industrial Reynolds number then we need to do some modelling, as opposed to resolving the values directly. The equations that we used are not closed and so we need to use Turbulence Modelling to close the equation set and then iterate towards a solution. We can use what is called a Reynolds Averaged Navier Stokes (RANS) approach, or we can use an Eddy Simulation technique which resolves the larger eddies in the flow and is only really required when you have separation or large recirculating regions. The most commonly used models are the RANS models due to their low cost in terms of compute power and run times. The Eddy Simulation methods can be quite mesh sensitive but will yield much better results for separated and recirculating flow, albeit over much longer run times. The RANS models apply a Reynolds decomposition technique to the Navier Stokes equations which breaks the velocity down into its mean and fluctuating components. This decomposition leaves us with one unknown value, which is termed the Reynolds Stress. We use Turbulence Models to resolve the Reynold’s Stress and close the equation set. There are two ways we can go about resolving this, the first (and most commonly used approach) is to use an isotropic value for the turbulent viscosity value which is called the an Eddy Viscosity Model, the other way is to solve using the Reynolds Stress Model (RSM) for the 6 separate Reynolds Stresses, which results in an anisotropic solution. EDDY VISCOSITY MODELS The limitation with Eddy Viscosity models is that they use an isotropic value which may not be appropriate and hence can increase the diffusion in your result. Obviously solving for the 6 Reynolds Stresses and dissipation will be more accurate, but you are then solving extra equations which will increase your run time considerably. There are further modifications to the 2 equation Eddy Viscosity...