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UK funding (£231,100): Coupling of Real-World Data and Fast Response Algorithms to Improve Simulation Correlations and Optimise Construction Ukri1 Mar 2012 UK Research and Innovation, United Kingdom
Overview
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Coupling of Real-World Data and Fast Response Algorithms to Improve Simulation Correlations and Optimise Construction
| Abstract | The project will develop and deploy MODSUITE within the Caterpillar UK Engines Company Ltd. (CAT). MODSUITE is a novel data analysis and optimisation tool and will be applied to analyse engine and machine test data against predictive physics-based models of the processes occurring within the engine. The tool will apply novel optimisation and fastresponse algorithms to systematically refine and quantify the uncertainty within the models. This will enable CAT to use the make more effective use of the data and use the models to help optimise engine performance, including gaseous and particulate (soot) emissions. The project is split between four main partners. The Computational Modelling (CoMo) Group at the University of Cambridge will perform the fundamental research and development required to apply MODSUITE to the applications presented by CAT. Cambridge Computational Modelling Ltd. (CMCL), an engineering software and services company, will focus on developing a user interface and testing the application of the software. CAT and BorgWarner Ltd. (see www.borgwarner.com) will provide experimental data and models for real applications to support the software testing. Federal Mogul Ltd. (see www.federalmogul.com) will act as a subcontractor to CAT for some of the model development. The software will be developed and tested using three demonstration applications, at increasing levels of system complexity. The initial phase of the project will be performed at CMCL facilities in Cambridge. Following successful completion of the initial testing, the software will be deployed at CAT via an internet based user interface. This will enable the software to be run at CMCL whilst it is still in development, whereas the application models will be run at CAT using distributed computing technology, allowing CAT to harness the large computing resource at their disposal and maintain control over the models and data. The web-based interface and distributed computing design offer a simple, but powerful solution. The CoMo Group will initially contribute to the project by investigating optimisation methods that are not currently implemented within MODSUITE. The ones that are most relevant to the applications presented by CAT will be identified and added. The group will extend MODSUITE so that it can automatically read and process the large quantities of experimental data made available by CAT, facilitating the creation of data driven models. A wider range of different response surface methods and an automated response generation and selection method will be investigated and implemented. This will increase the versatility of the tool such that suitable response surfaces can be generated for each specific test application. These improvements will facilitate the ability to generate suitable data driven models as well as fast surrogate models. Advanced optimisation methods will be developed with a focus on self-calibration and robustness to provide consistent and reliable results without the need for expert knowledge of the specific algorithms. These advanced algorithms will be combined with uncertainty propagation and analysis tools to quantify the uncertainties in the model. The MODSUITE code will be adapted to allow it to run over a distributed computing system. |
| Category | Research Grant |
| Reference | EP/J501736/1 |
| Status | Closed |
| Funded period start | 01/03/2012 |
| Funded period end | 31/01/2015 |
| Funded value | £231,100.00 |
| Source | https://gtr.ukri.org/projects?ref=EP%2FJ501736%2F1 |
Participating Organisations
| University of Cambridge |
The filing refers to a past date, and does not necessarily reflect the current state. The current state is available on the following page: University of Cambridge, Cambridge.
