
Nanostructured alloys for extreme applications
We explore the potential of atomistic simulations for radically new, bottom-up design of self-healing and radiation-resistant metallic materials.
Modern engineering relies heavily on the computational simulation of future products and on time-efficient and effective design decision making based on such simulations. The work of researchers at the University of Southampton spans this entire workflow, starting with large scale simulations - for example to better understand the atmospheric environment and the impact its changes might have on low Earth orbit spacecraft operations - through methods that link changes in product performance to changes in design variables. The latter class of technologies is increasingly making use of the latest advances in machine learning and automation, which conspire to speed up iterative design loops; this, in turn, allows the exploration of a wider range of designs, ultimately leading to more competitive products - for example, gas turbine engines with lower emissions.
We explore the potential of atomistic simulations for radically new, bottom-up design of self-healing and radiation-resistant metallic materials.
Our assessment of the influence of carbon dioxide emissions on the upper atmosphere using the IRIDIS super-computer has revealed how greenhouse gas is increasing the globe’s space debris problem.
Alan Turing conceptualised the use of evolution to develop intelligent machines, we utilise this idea to solve complex, real-world, engineering problems.
Our research enables engineers to simultaneously improve engine performance whilst reducing time to market through a combination of machine learning and CAD automation.
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