LEAP will be in Melbourne at the 2nd Int'l Symposium on Computational Particle Technology to showcase exciting new modelling work that has been completed recently using Rocky DEM and ANSYS CFD to predict liner wear in a semi-autonomous grinding (SAG) mill, using ANSYS CFD to model the effects of slurry flow within the mill on liner wear and particle breakage.
We are Horizon, Australia's lead F1 in Schools team from Brighton Secondary School in South Australia. After years of hard work and dedication, as well as the generous support of our sponsors such as LEAP Australia, we are very proud to have been crowned the 2018 F1 In Schools World Champions. Here is a recollection of our team’s journey using ANSYS CFD to refine our car design for the World Finals in Singapore in September 2018. Each year the F1 In Schools World Finals is the world's largest STEM competition and is the culmination of years of work from the top 50 teams from over 30 countries around the world, representing over 20 million students globally. In F1 In Schools, teams of 3-6 students create and manage a miniature F1 team, in which they are tasked to engineer a miniature F1 car that complies with a complex set of regulations that are raced down a 20 meter track in just 1 second. As well as this, teams create a team identity, brand and market their team to companies around the world to raise the money to compete in sponsorship. Teams have large 3 meter by 2 meter trade displays, portfolios, social media campaigns, industry interviews, verbal presentations and more designed to mimic the dynamic of a Formula One team. The competition is marked out of 1000 points, of which the engineering and racing of the car attributes to 600 of the overall points. Teams compete at a regional, state and national level prior to being able to represent their country at the World Finals. Each year, the world finals takes place in conjunction with a Formula One Grand Prix, this year being the Singapore Grand Prix. The winners of the World Finals are awarded with engineering scholarships to University College London. Since the engineering of the car is worth 350 points, it was very important to us that we had a thorough engineering process and spent as much time and effort into developing the best product possible. When we started the design process of the car, we wanted to make sure we were focusing on developing the most important areas. We created a mathematical model of the car travelling down the track which accounted for the forces being applied to the car. Through this we were able to determine that properties like the cars lift and drag had a more significant effect on the track time than other components like the wheel and axle system. On reflection, it was instantly visible that many of the teams at the competition were focusing and highlighting developments of their car in design...
From tunnel ventilation to thermal comfort on your daily commute: CFD applications in the Rail industry
As you become more immersed in the world of CFD/simulations, you also begin looking around you and identifying more aspects of your everyday life impacted by engineering simulation. Learn how work by engineers in the Rail industry now means your daily commute contains many good examples of how CFD has improved both your comfort and safety.
When dealing with a significant number of variables in our simulations, design engineers often find it challenging to work out which variables are the most important, and how to best tune these variables to improve performance. Learn how ANSYS optiSLang now offers a compelling proposition for answering these questions while giving engineers even more tools for exploring the possible performance envelope within key design parameters.
Engineers are continually under pressure to improve the performance of their products and often look to gain an edge using optimisation techniques - trying to reduce drag, increase lift (or downforce), or reduce pressure drop. Rather than relying on intuition to make geometry changes that are often constrained (using a parametric CAD approach), you can now use the new Adjoint solver to compute localised sensitivity data (related to your objectives) and optimize your design semi-automatically.