PRISM: Platform for Research In Simulation Methods

Completed

Renewable Energy Sources(Wind Energy)
Not Energy Related

Basic and strategic applied research

ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering)

Not Cross-cutting

Professor SJ Sherwin
Aeronautics
Imperial College London

Standard

EPSRC

01 July 2018

31 December 2023

66 months

£1,612,965

Mathematical sciences

London

NC : Engineering

Professor SJ Sherwin, Aeronautics, Imperial College London

Dr C Cotter, Mathematics, Imperial College London
Dr PE Farrell, Mathematical Institute, University of Oxford
Dr J Gorman, Earth Science and Engineering, Imperial College London
Dr DA Ham, Mathematics, Imperial College London
Professor P Kelly, Computing, Imperial College London
Dr DC Moxey, Engineering Computer Science and Maths, University of Exeter
Professor CC Pain, Department of Earth Sciences, Imperial College London
Dr J Peiro, Aeronautics, Imperial College London
Dr MD Piggott, Earth Science and Engineering, Imperial College London
Dr P E Vincent, Aeronautics, Imperial College London

Project Contact, Rolls-Royce PLC
Project Contact, Tidal Lagoon Power Ltd
Project Contact, Met Office
Project Contact, McLaren Racing Ltd
Project Contact, Atlantis Resources Corporation
Project Contact, BP International Ltd
Project Contact, University of Sao Paulo
Project Contact, Amec Foster Wheeler UK

Computational science is a multidisciplinary research endeavour spanning applied mathematics, computer science and engineering together with input from application areas across science, technology and medicine. Advanced simulation methods have the potential to revolutionise not only scientific research but also to transform the industrial economy, offering companies a competitive advantage in their products, better productivity, and an environment for creative exploration and innovation.The huge range of topics that computational science encapsulates means that the field is vast and new methods are constantly being published. These methods relate not only to the core simulation techniques but also to problems which rely on simulation. These problems include quantifying uncertainty (i.e. asking for error bars), blending models with data to make better predictions, solving inverse problems (if the output is Y, what is the input X?), and optimising designs (e.g. finding a vehicle shape that is the most aerodynamic). Unfortunately, the process through which advanced new methods find their way into applications and industrial practice is very slow.One of the reasons for this is that applying mathematical algorithms to complex simulation models is very intrusive; mostly they cannot treat the simulation code as a "black box". They often require rewriting of the software, which is very time consuming and expensive. In our research we address this problem by using automating the generation of computer code for simulation. The key idea is that the simulation algorithm is described in some abstract way (which looks as much like the underlying mathematics as possible, after thinking carefully about what the key aspects are), and specialised software tools are used to automatically build the computer code. When some aspect of the implementation needs to change (for example a new type of computer is being used) then these tools can be used to rebuild the code from the abstract description. This flexibility dramatically accelerates the application of advanced algorithms to real-world problems.Consider the example of optimising the shape of a Formula 1 car to minimise its drag. The optimisation process is highly invasive: it must solve auxiliary problems to learn how to improve the design, and it be able to modify the shape used in the simulation at each iteration. Typically this invasiveness would require extensive modifications to the simulation software. But by storing a symbolic representation of the aerodynamic equations, all operations necessary for the optimisation can be generated in our system, without needing to rewrite or modify the aerodynamics code at all. The research goal of our platform is to investigate and promote this methodology, and to produce publicly available, sustainable open-source software that ensures its uptake. The platform will allow us to make advances in our software approach that enables us to continue to secure industrial and government funding in the broad range of application areas we work in, including aerospace and automotive sectors, renewable energy, medicine and surgery, the environment, and manufacturing

01/02/19