Engineering Complexity Resilience Network Plus
Engineering Complexity Resilience Network Plus
批准号:
EP/N010019/1
负责人:
Martin Mayfield
金额:
$64.01万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2016
资助国家:
英国
项目状态:
已结题
起止时间:
2016 至 --
中文摘要
点击翻译按钮获取中文摘要
英文摘要
Our society is increasingly reliant upon engineered systems of unprecedented and growing complexity. As our manufacturing and service industries, and the products that they deliver, continue to complexify and interact, and we continue to extend and integrate our physical and digital infrastructure, we are becoming increasingly vulnerable to the cascading and escalating effects of failure in highly complex and evolving systems of systems. Consequently, it is becoming increasingly critical that we are able to understand and manage the risk and uncertainty in Complex Engineering Systems (CES) to provide reliant and optimal design and control solutions.Research on natural complex systems is helping us to understand the implications of inter-dependencies within and between complex adaptive systems. However, unlike natural ecosystems, which may become more robust through diversifying, man-made complex systems tend to become more fragile as their complexity increases. If we are to deal with the challenge presented by complex engineered systems, we will need to exploit and synthesise our current understanding of natural and engineered systems, our current theories of complexity more generally.The ENgineering COmplexity REsilience Network Plus (hereafter called ENCORE) addresses the Grand Challenge area of Risk and Resilience in CES. Our vision is to identify, develop and disseminate new methods to improve the resilience and sustainable long-term performance of complex engineered systems, initially including Cities and National Infrastructure, ICT and Energy Infrastructure, Complex Products: Aerospace (both Jet Engines and Space Launch and Recovery Systems) and later to explore the inclusion of Nuclear Submarines, Power Stations and Battlefield Systems. We have chosen these particular CES domains as they strike a balance between the challenges and opportunities that the UK faces for which complexity science can have a significant impact for our citizens and businesses whilst spanning sufficiently diverse fields to present cross-domain learning opportunities.Our approach is to create shared learning from [1] the manner in which naturally complex systems cope with risk and uncertainty to deliver resilience (ecosystems, climate, finance, physiology, etc.) and how such strategies can be adapted for engineering systems; [2] how the tools and concepts of complexity science can contribute towards developing a greater understanding of risk, uncertainty and resilience, and [3] distilling world-class activity within individual CES domains to provide new insights for the design and management of other engineering systems.Examples of the potential for the application of this field and which will be considered for inclusion in the feasibility studies include:- Predicting equipment failures and their consequences in critical infrastructure systems;- Developing a management heuristic that plays the same role as a "risk register", but addresses systemic resilience;- Optimising the deployment of instrumentation required to manage cities and other CES effectively;- Increasing the resilience of interdependent digital systems;- Advancing models of cascading failure on networks such that they take account of node heterogeneity and in particular the different failure/recovery modes of different types of node. - Improving the number of contexts in which CES can be deployed with replicable performance;- Decreasing the likelihood of human behavioural errors in operating CES.- Identifying the critical elements that constrain/define system performance most strongly;- Extending system lifetimes and functionality;- Mapping the relationship between complex system complexity and fragility;- Characterising uncertainty and defining the inference process to transition from one phase to the other in the control of CES and in complex decision making processes.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
登录
查看更多内容
Manifold Cities: Social variables of urban areas in the UK
多元化城市:英国城市地区的社会变量
DOI:
10.48550/arxiv.1809.03376
发表时间:
2018
期刊:
影响因子:
--
作者:
[Barter E]
通讯作者:
Barter E
Robust Distributed Decision-Making in Robot Swarms: Exploiting a Third Truth State
机器人群中的鲁棒分布式决策:利用第三个真相状态
DOI:
--
发表时间:
2017
期刊:
影响因子:
--
作者:
[CrossCombe M]
通讯作者:
CrossCombe M
On the development logic of city-regions: inter- versus intra-city mobility in England and Wales
论城市区域的发展逻辑:英格兰和威尔士的城市间流动性与城市内流动性
DOI:
10.1080/17421772.2019.1569762
发表时间:
2019
期刊:
Spatial Economic Analysis
影响因子:
2.3
作者:
[Arbabi H]
通讯作者:
Arbabi H
Urban performance at different boundaries in England and Wales through the settlement scaling theory
DOI:
10.1080/00343404.2018.1490501
发表时间:
2019-06-03
期刊:
REGIONAL STUDIES
影响因子:
4.6
作者:
[Arbabi, Hadi, Mayfield, Martin, Dabinett, Gordon]
通讯作者:
Dabinett, Gordon
Productivity, Infrastructure and Urban Density-An Allometric Comparison of Three European City Regions Across Scales
生产力、基础设施和城市密度——欧洲三个城市区域不同尺度的异速生长比较
DOI:
10.1111/rssa.12490
发表时间:
2020
期刊:
Statistics in Society
影响因子:
--
作者:
[Arbabi H]
通讯作者:
Arbabi H
共 7 条
海外基金