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Methods of emulation, model calibration and uncertainty analysis developed by Professor Tony O'Hagan and his team at The University of Nottingham (UoN) have formed the basis of Pratt & Whitney's Design for Variation (DFV) initiative which was established in 2008. The global aerospace manufacturers describe the initiative as a `paradigm shift' that aims to account for all sources of uncertainty and variation across their entire design process.
Pratt & Whitney considers their implementation of the methods to provide competitive advantage, and published savings from Pratt & Whitney adopting the DFV approach for a fleet of military aircraft are estimated to be approximately US$1billion.
This study demonstrates how Bayes linear methodologies developed at Durham University have impacted on industrial practice. Two examples are given. The approach has been applied by London Underground Ltd. to the management of bridges, stations and other civil engineering assets, enabling a whole-life strategic approach to maintenance and renewal to reduce costs and increase safety. The approach has won a major award for innovation in engineering and technology. The methodology has also been applied by Unilever and Fera to improve methods of assessing product safety and in particular the risk of chemical ingredients in products causing allergic skin reactions.
Pratt & Whitney (one of the world's largest makers of aircraft engines) has developed a process, "Design for Variation" (DFV), that uses Bayesian methods developed at Sheffield for analysing uncertainty in computer model predictions within the design, manufacture and service of aircraft engines. The DFV process significantly improves cost efficiency by increasing the time an engine stays operational on the wing of an aircraft, so reducing the time that the aircraft is unavailable due to engine maintenance. DFV also saves costs by identifying design and process features that have little impact on engine performance, but are expensive to maintain. Pratt & Whitney estimate the DFV process to generate savings, for a large fleet of military aircraft, of [text removed for publication].
The UK Food and Environment Research Agency (Fera) has used these methods in their risk analyses, for example in assessing risks of exposure to pesticides.
ENABLE is a history matching and uncertainty assessment software system for the oil industry, whose inference engine was produced by the Durham Statistics group, based on their research on uncertainty quantification for complex physical systems modelled by computer simulators. The system optimizes asset management plans by careful uncertainty quantification and reduces development costs by accelerating the history matching process for oil reservoirs, resulting in more informed technical and economic decision-making. ENABLE was acquired by Roxar ASA in 2006 and current users include the multinational oil company Statoil. From January 2008 to September 2012 (the most recent set of figures) the turnover attributed to ENABLE was [text removed for publication].
The Climate Change Act, 2008, constructed a legally-binding long-term framework for the UK to cut greenhouse gas emissions and a framework for building the UK's ability to adapt to a changing climate. The Act requires a UK-wide climate change risk assessment (CCRA) that must take place every five years and a national adaptation programme (NAP), setting out the Government's objectives, proposals and policies for responding to the risks identified in the CCRA. The CCRA, and thus the NAP, drew heavily on the uncertainty analysis for future climate outcomes, published in 2009 by the Met Office as the UK Climate Projections UKCP09, which in turn drew heavily on research into the Bayesian analysis of uncertainty for physical systems modelled by computer simulators carried out at Durham University. A wide range of industries and public sector organisations likely to be affected by climate change have consulted with the Met Office on UKCP09 to inform decisions on policy and investment, involving billions of pounds, in sectors as diverse as flood defence, transport, energy supply and tourism.
The chemical contamination of food or soil poses a significant risk to human health; regulatory decisions on the level of this risk are based upon measurements of contamination. To improve these risk assessments, Ramsey devised the `duplicate method' to estimate the level of uncertainty in measurements of contamination. The application of this method is now included in statutory guidance provided by the soil, food and water sectors to improve reliability in the classification of materials as contaminants and thereby reduce the worldwide risk of contamination to humans.
Methodologies for shape analysis developed by the Shape and Object Data Analysis group at The University of Nottingham (UoN) have underpinned important applications resulting in a range of benefits for companies and organisations, including in human movement capture and fingerprint modelling.
Firstly, the economic benefits of the methodologies developed at Nottingham to capture human movement data without a calibration trial have been used by a commercial software company, Charnwood Dynamics Ltd, and have saved time for its users and increased portability. Secondly, by incorporating research methods into practice, practitioners have improved standard processes, which have resulted in efficiency savings. Organisations which have benefitted from the research methods include the German Federal Police, where the methodology has been used in modelling growth in adolescent fingerprints, resulting in lower error rates and a reduction in false matches.
Aston University researchers developed and maintain the Uncertainty Markup Language (UncertML) for quantitative specification and interoperable communication of uncertainty measures in the Web. It is the only complete mechanism for representation of uncertainty in a web context. UncertML has been:
- Used in policy and decision making by UK (Food and Environment Research Agency) and international (European Commission) government agencies, and many research / industrial institutes;
- Presented at industrial /technical workshops, leading to ongoing international collaborations with bodies such as national space agencies (ESA and NASA) and government data providers;
- Accepted as a discussion paper for formal standardisation by the Open Geospatial Consortium;
- Chosen by independent data providers for efficient sharing of complex information and rigorous risk analysis across scientific domains such as pharmacy, global soil mapping and air quality.