Uncertainty Quantification & ManagementSpecial Interest Group

Prepared by:Matt Butchers, Knowledge Transfer Network

Uncertainty Quantification & Management SIG

ContentsExecutive Summary ...................................................................................................1

Background..................................................................................................................1

Scope ........................................................................................................................1

Aims and Objectives .............................................................................................2

Deliverables .................................................................................................................2

Highlights and Case Studies ....................................................................................3

KPIs and Outcomes ....................................................................................................5

Learnings and Recommendations ..........................................................................5

Contacts .......................................................................................................................5

1Uncertainty Quantification & Management SIG

The Uncertainty Quantification & Management Special Interest Group was established in 2015 to promote the benefits of a more statistically-savvy engineering design and assessment community. With a steering group of leading industry representatives and researchers, the group explored topics such as visualisation, materials modelling, inverse problems, and specific industry challenges through highly successful UQ&M Study Groups with Industry.

Executive Summary

Scope

Whether bringing a new product from conception into production or operating complex plant and production processes, commercial success rests on careful management and control of risk in the face of many interacting uncertainties. Today’s fiercely competitive market and increasingly stringent regulatory environment are such that there is very little margin for error. A new era of virtual design and engineering, underpinned by modelling and simulation, opens the opportunity to deal with uncertainty in a systematic, formal way, by which sensitivities to various uncertainties can be quantified and understood, and designs and processes optimised so as to be robust against such uncertainties.

Leading companies in many fields of engineering are increasingly aware of these possibilities and uncertainty quantification is beginning to feature strongly in their strategic aspirations. The Uncertainty Quantification & Management Special Interest Group (UQ&M SIG) was established in 2015 to promote the benefits of a more statistically-savvy engineering design and assessment community. The Topic is very rich in challenges across mathematics, statistical modelling, multi-disciplinary analysis, and simulation. Fortunately, the UK is well-placed with world-class expertise in all of these areas, although this has yet to be inspired and drawn together to tackle the challenge of developing a practical and mature capability. Thus, in the spring of 2014, a meeting was held in London of key stakeholders under the auspices of the HVM (High Value Manufacturing) arm of Innovate UK to discuss these issues and establish the level of interest in a national UQ&M initiative. As a result of this meeting, it was agreed to launch a Special Interest Group (SIG) on UQ&M.

Background

2 Uncertainty Quantification & Management SIG

Over the three years of SIG delivery:

Online Capability Map and Use Case Platform; –

Newsletter and social media presence; –

5 workshops and reports; –

1 First Year Report; –

Support for RCUK (Research Councils UK) Networks; –

A number Study Groups resulting in collaborative groupings for future –R&D;

12 Case Studies which highlight the value in investing in UQ&M; –

4-part webinar series established by industrial needs, delivered by –leading UK researchers.

Aims and Objectives

The top-level aim of the SIG was to draw together a UQ&M community and provide a structured meeting space where all the players could share their aspirations, knowledge and expertise. It was to be expected that much of tangible value would be created, such as:

Collaborative groupings that identify real benefit in working together; –

The development and refinement of challenges and aspirations; –

The emergence of a clutch of industry pulled projects that make –significant advances against the above challenges within given industrial HVM sectors;

An increasingly statistics-savvy engineering design and assessment –community;

A highly visible joined up and holistic UK based UQ&M capability that –can respond positively to end-user aspirations and requirements.

Deliverables

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The primary mechanism we used in the SIG to drive B2R engagement was via the use cases generously supplied by our industry partners. These use cases were worked through in three study groups; two at the Institute for Risk and Uncertainty in Liverpool and one at the Warwick Centre for Predictive Modelling. Over these groups, eleven problems were tackled, encompassing aerospace, drug discovery, automotive, and many more. These groups have bought together over 43 distinct organisations and over 175 unique participants - when added together these groups amount to over 1.5-person years of collaboration between businesses and researchers. Many of the solutions discovered at these study groups have created new research plans within industry and have led to collaborations with universities. More UQ&M study groups are being planned to maintain this effort.

The technical reports, which were written as summaries for the businesses, form a fascinating collective literature of UQ&M priciples applied to some of the most exciting and pressing challenges in the UK industry. Funding secured as a result of these groups currently stands at around £150k. The use cases tackled during the SIG duration are listed below:

Airbus, Climb-Cruise Engine Matching; –

Airbus, Wing Design Load Margin Setting; –

Airbus, Climb-Cruise Engine Matching – An Optimisation Approach; –

AstraZeneca, Improved Drug Discovery Through Better Machine –Learning Models;

Atomic Weapons Establishment, developing a Surrogate Model for –Predicting Metallic Corrosion Events;

High Speed Two, Visualising, Communicating, and Managing Risk in –Large Infrastructure Projects;

Jaguar Land Rover, Ride Optimisation Considering Vehicle Mass –Property Variation;

The MTC, PKM Dynamics Model Validation for Control Design; –

Rolls-Royce, Thermo-mechanical Analysis of a Turbine Disc; –

Zenotech, Increasing Certainty in Offshore Energy; –

Zenotech, Machine Learning for Wind Energy Modelling. –

AWE - A Computational Framework to Address Materials Ageing –Behaviour

GKN Aerospace - Understanding Metal Spray Deposition Variation –

Highlights and Case Studies

4 Uncertainty Quantification & Management SIG

Rolls-Royce - Probabilistic Frameworks for Nuclear Integrity –Assessments

BAE Systems - Uncertainty Management Framework for Evolving –Electrical Power Generation & Distribution Test Environments.

Furthermore, we have been delighted to see an overspill of our UQ communities into other KTN and Innovate UK activities, bringing UQ expertise to fields as diverse as agriculture, batteries, and energy generation, for the benefit of organisations such as Williams Advanced Engineering, Mondelez International, National Grid and many more. We will continue to engage the UQ community to further underpin exciting opportunities in UK’s Industrial Strategy – with upcoming opportunities including health and construction.

Workshops over the past few years have tackled some of the more expansive topics, where a broader subsection of the research community is needed to consider and share the state of the art. In Edinburgh, we explored The Role of Inverse Problems & Optimisation in UQ. In London, we tackled Visualising to Communicate Uncertainty in Industrial Design, and in Cambridge, over four-days, we welcomed an international audience to discuss Predictive Multiscale Materials Modelling.

Prof. Dr. Nicholas Zabaras said of the latter meeting “The workshop provided an ideal environment, not possible, elsewhere, for bringing togetherbringing together leading national and international academics and industrial researchers, to address and debate interdisciplinary solutions to unifying mathematical themes fundamental to predictive materials modelling. The different perspectives presented and the opportunity to discuss mathematical and statistical challenges arising in predictive materials modelling in industrial applications have contributed to a long-lasting impact.”

In late 2015, we welcomed Luis Crespo from NASA to the UK to take part in a seminar series held at Imperial College in Liverpool, culminating in a meeting at the Pullman Hotel in central London. Luis was pleased to ‘benefit from the exchanges resulting from the seminar[s] and from knowing the UQ needs driving [UK] UQ challenge[s]’.

5Uncertainty Quantification & Management SIG

11 case studies; –

12 business to research collaborations; –

300 SIG members; –

5 new studentships; –

£40k for external money to SIG activities; –

£258k of new projects initiated by the SIG. –

KPIs and Outcomes

UQ&M is new and rapidly evolving field, in which the UK is particularly well placed due to its strengths in the mathematical sciences. Statistical research is addressing:

high-dimensional model responses, –

the combination of big data and UQ, –

probabilistic/meta modelling/data analytics, –

optimisation with uncertainties. –Intrusive approaches are becoming more popular if exascale computing develops.

In most businesses, there is currently very limited application of UQ and therefore a lack of ‘best practice’. In those businesses who do apply UQ techniques, this is mostly understood as sensitivity analysis. There is evidence of some R&D activities in aerospace.

The SIG has been seen as a vital cross-business platform, which exchanges data, capability and aspirations.

Learnings and Recommendations

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Chairs in bold

Ron Bates (Rolls-Royce PLC.), Veronica Bowman (Dstl), Francisco Alejandro Diaz De la O (University of Liverpool), Steve Graham (National Nuclear Laboratory), Tony Hutton (Airbus), Robert Leese (Smith Institute), Cameron Mackay (Unilever PLC), Alejandra Matamoros (The MTC), Lynne McGregor (Innovate UK), Francesco Montomoli, Jacqui Morison (Jaguar Land Rover), Tony O’Hagan (Emeritus Professor of Statistics), Andrew Pike (GE Power), Catherine Powell (University of Manchester), Andy Richardson (Jaguar Land Rover), Sanjiv Sharma (Airbus), David Standingford (Zenotech), Rhia Visavadia (EPSRC), Paul Westoby (Dstl).

Contacts

7Uncertainty Quantification & Management SIG

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