Stanford University researchers are hoping to improve the design of hypersonic aircraft engines by running simulations on thousands of computers at once.
The $20m (£12.5m) Stanford Predictive Science Academic Alliance Program (PSAAP) funded by the US Department of Energy aims to model how fuel and air flow through a scramjet engine so that engineers can make their designs safer and more reliable.
‘Some of our latest calculations run on 163,000 processors simultaneously,’ said the programme’s faculty director Prof Parviz Moin in a statement. ‘I think they’re some of the largest calculations ever undertaken.’
A scramjet engine enables aircraft to fly at many times the speed of sound by burning fuel in an airflow that is already moving at supersonic speed.
However, the extreme mechanical forces acting on these engines make them very vulnerable to failure if conditions are not exactly right.
Last year, a test flight of the hypersonic Boeing X-51 Waverider ended prematurely after a problem known as an unstart occurred. It’s this issue that the Stanford researchers hope to address.
‘If you put too much fuel in the engine when you try to start it, you get a phenomenon called “thermal choking”, where shock waves propagate back through the engine,’ said Moin.
‘Essentially, the engine doesn’t get enough oxygen and it dies. It’s like trying to light a match in a hurricane.’
PSAAP’s aim is work out exactly what the uncertainties are in modelling a scramjet engine so that engineers can build enough tolerance into their designs to allow the technology to function in different circumstances.
‘When you base decisions on computations that are in some way imperfect, you make errors,’ said Juan Alonso, associate professor of aeronautics and astronautics.
‘Not only that, but these hypersonic vehicles are themselves subject to uncertainties in how they behave in the air.’
Understanding these uncertainties could also help improve other complex simulations, he added.
‘These same technologies can be used to quantify flow of air around wind farms, for example, or for complex global climate models.’