Flying has always been a passion of mine. Growing up I wanted to be a pilot and fly aeroplanes. But hiring in this profession is cyclic, and when I finished school in Germany they were no longer recruiting pilots. So I chose to study the next closest thing: aerospace engineering.
Visiting air shows and exhibitions captured my imagination. You can see an engine up close and you can walk around it. There are books and television programs about building jet engines. We take them for granted and hope they work. If you know what’s actually going on inside, it’s a miracle they stay in the air. As a kid you don’t really think about how an engine works, but you soon realise how amazing these machines really are.
The future of aircraft is very hard to predict. Everyone is talking about renewable energy, but even for the large airframe companies such as Boeing or Airbus a switch of energy sources is a risky investment. These companies could go bankrupt if they invested in the wrong model. This is linked to the nature of the aircraft industry, where safety is a primary concern. They cannot afford for anything to go wrong, as many lives would be at risk. There’s no room for chance or guesswork, and it’s no wonder the industry has such a conservative approach.
While we can’t run experiments on physical aeroplanes, we can use computer programs to test the physics behind virtual aircraft engines. We write code, then apply our program to engine models on supercomputers. From the data it produces, we can tell a lot about turbulence in an aircraft engine and pinpoint how we can improve them. But exploiting the new computing technologies means spending a lot of time reworking code. As computer hardware advances, we need to keep adapting our code so we can produce even better data.
With our living code and access to supercomputers, we can develop faster and greener air travel in one year, rather than 3000. My research project has been allocated 100 million core hours on the world’s fastest supercomputers.
If we ran our code on desktop computers, it would take 25,000,000 hours or nearly 3000 years to finish our simulations. But with our code and supercomputers we can do it one year.
Our code aims to improve air travel using a two-pronged approach. First, we want to improve our understanding of the physics of aircraft engines. With this knowledge we can help the designers in industry enhance and tweak the engines we already have. Second, we aim to develop accurate and reliable tools to help us design the next generation of engines. The problem with existing tools is they are too closely tuned to current engine design, so they have a limited ability in developing the next generation of aircraft engines.
People ask why we spend all this effort to achieve a 2 per cent energy saving. But when you consider gas turbines, we’re not just talking about one engine that consumes this energy. It’s the combined usage of thousands of airplanes and gas turbines for power generation all around the world that we have in mind, burning over $200 billion worth of fuel every year. Even if we can improve energy efficiency by just 1 per cent, we can make a significant difference.
The Fluids group at the University of Melbourne is well-known around the world for their work on turbulence. But instead of running virtual experiments, they typically run experiments using physical tunnels. While both approaches to mechanical engineering have their advantages, it is important to have a mix of both. We’re both producing data, and we both need to extract and analyse physics from the information we collect. We expect to develop much synergy in our work over the next few years, to create cleaner and greener air flight.
Currently, we can only run experiments on one small part of the engine. We take one component, such as the low-pressure turbine, and look at a single blade of that component. Our long term ambition is to run a simulation on a full engine. Computing power is increasing all the time, and in 12-15 years’ time we could probably do 1000 times larger simulations than we can now.
When I started University, an experimental researcher told me that computer simulations would never be good enough to help design a new engine – 20 years on I feel that we are getting pretty close to doing exactly that.”
- As told to Kristen Goodgame
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