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Details of Grant 

EPSRC Reference: EP/J501736/1
Title: Coupling of Real-World Data and Fast Response Algorithms to Improve Simulation Correlations and Optimise Construction
Principal Investigator: Kraft, Professor M
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Chemical Engineering and Biotechnology
Organisation: University of Cambridge
Scheme: Technology Programme
Starts: 01 March 2012 Ends: 31 January 2015 Value (£): 169,228
EPSRC Research Topic Classifications:
Information & Knowledge Mgmt Manufact. Enterprise Ops& Mgmt
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:  
Summary on Grant Application Form
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


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.
Key Findings
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Potential use in non-academic contexts
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Date Materialised
Sectors submitted by the Researcher
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Project URL:  
Further Information:  
Organisation Website: http://www.cam.ac.uk