Completed Engineering Materials & Manufacturing

Strategic Partnership in Computational Science for Advanced Simulation and Modelling of Engineering Systems - ASiMoV

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AI plain-English summary

Rolls-Royce and its partners are building the first complete digital simulation of a gas-turbine engine in operation, including heat, stress, airflow, and electromagnetics all at once. Today’s engines, like the Trent XWB, were designed using simulation, but next-generation engines require a leap beyond what current software can handle. The aerospace industry wants “virtual certification”—replacing some physical engine tests with trusted computer models—but no existing solver can run a full engine simulation at the resolution needed. The problem is scale: each simulation will demand a billion core hours on future Exascale supercomputers, and current algorithms cannot deliver. If ASiMoV succeeds, Rolls-Royce could save millions of pounds per engine programme by reducing physical testing. More broadly, the project will push forward fundamental computational science—new numerical solvers, algorithms, and software infrastructure—that will also benefit other industries reliant on high-fidelity simulation, such as energy and transport. The work is applied, but it requires breakthroughs in fundamental mathematics and computer science to make Exascale simulation practical.

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The strategic vision of this Prosperity Partnership for Advanced Simulation and Modelling of Virtual Systems (ASiMoV) is to enable the research and development of the next generation of engineering simulation and modelling techniques. Our aim is to achieve the world's first high fidelity simulation of a complete gas-turbine engine during operation, simultaneously including the effects of thermo-mechanics, electromagnetics, and CFD. This level of simulation will require breakthroughs at all levels, including physical models, numerical solvers, algorithms, software infrastructure, and Exascale HPC hardware. Our partnership uniquely combines fundamental engineering and computational science research with two high tech SMEs and Rolls-Royce plc to address a challenge that is well beyond the capabilities of today's numerical solvers. Simulation and modelling, enabled by high performance computing, have transformed the way products are designed and engineered. The technology developed for the Trent XWB, the world's most efficient aero engine, could only have been achieved through simulation and modelling. However, next generation products will place demands on simulation that cannot be met by incremental changes to current techniques. The ACARE Flightpath 2050 goals demand fundamental changes to engine architectures and the 2015 Aerospace Technology Institute Propulsion Strategy identified "virtual certification" as a key technology needed in the 2025-30 timeframe. The journey to virtual certification is an incremental one requiring a thorough evidential database to convince the certification authorities that the analysis can be trusted. It will move forward on a number of fronts. One of those is the whole engine tests to certify operational performance and thrust. Our driving ambition is to realise new simulation technology for the ultra-high resolution and extreme scale needed for meaningful virtual certification models. For Rolls-Royce, virtual certification will bring a major business transformation requiring unprecedented trust in simulation and fundamental changes to design processes and skills. Estimated cost savings for virtual certification are measured in the many £millions per engine programme; but, we also estimate that each simulation will require a billion core hours. At this scale, savings from computational cost and performance optimisation will be £millions per design study. Hence the need for ASiMoV to push forward the boundaries of numerical modelling and simulation on the next generation of Exascale supercomputers.

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Researchers

Alexander Korsunsky (Co-Investigator)Antoine Jerusalem (Co-Investigator)Christopher Richardson (Co-Investigator)David Hills (Co-Investigator)Garth Wells (Co-Investigator)Gihan Mudalige (Co-Investigator)Jose Nunez-Yanez (Co-Investigator)Mark Parsons (Principal Investigator)Nikica Petrinic (Co-Investigator)Simon McIntosh-Smith (Co-Investigator)Stephen Jarvis (Co-Investigator)

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Original classification

Research Grant

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