Details of Grant 

EPSRC Reference:	GR/R76172/01
Title:	The Development of Efficient Methods for Global Sensitivity and Uncertainty Analysis within Reactive Flow Models, with Applications in Proc & Env Eng
Principal Investigator:	Tomlin, Professor AS
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department:	Fuel and Energy
Organisation:	University of Leeds
Scheme:	Advanced Fellowship (Pre-FEC)
Starts:	01 October 2002	Ends:	30 June 2008	Value (£):	253,554
EPSRC Research Topic Classifications:	Combustion Design of Process systems Waste Minimisation
EPSRC Industrial Sector Classifications:	No relevance to Underpinning Sectors
Related Grants:
Panel History:	Panel Date Panel Name Outcome 23 Nov 2001 Engineering Fellowships Panel (2002) Deferred
Summary on Grant Application Form
Computer models are often used within design and decision making processes to reduce the burden of expensive experimental programmes and to explore situations and parameter regions that cannot be reached by experiment. The use of models is therefore crucial to gaining economic competitiveness through efficient product design processes in the engineering sector. Models are also vital within environmental engineering to assess the environmental impact of a range of technologies on for example air and water quality. Such models are often used to inform policy formation (such as legislation on emissions) and environmental management. The quantification of model accuracy and the confidence that can be placed in model predictions is therefore crucial. Often this is achieved by comparison between model and experimental results, leading to costly programmes of research over limited conditions. It follows that the quantification of model uncertainties is vital if models are to be used to inform decisions. Sensitivity analysis can be used to ascertain how each model input factor influences the variation in the model output. Along with uncertainty propagation it can assess the impact of uncertainties in model input data on model output. The relative importance of the sources of uncertainty can then be assessed and vital information gained for use in model development. Previously used techniques of local sensitivity analysis are computationally efficient but present significant problems for situations where uncertainties in inputs are large and models are non-linear. Global sensitivities are more applicable but consume large amounts of computational effort. The aim of this project is therefore to develop efficient generic methods for global uncertainty analysis and to establish the limits of local techniques. The methods will be demonstrated for a range of applications from the chemical industry, combustion, and models describing the influence of traffic emissions on urban air quality. The research programme will be of benefit to UK industry since it could be employed to improve the design, efficiency and effectiveness of a wide range of industrial processes. It will contribute to the development of sustainable technology by providing better information for decision makers from environmental impact assessment models.
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Organisation Website:	http://www.leeds.ac.uk