A Resilience Modelling Framework for Improved Nuclear Safety (NuRes)
Lead Research Organisation:
University of Strathclyde
Department Name: Civil and Environmental Engineering
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Organisations
Publications
Altieri D
(2020)
Machine Learning Approaches for Performance Assessment of Nuclear Fuel Assemblies Subject to Seismic-Induced Impacts
in ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Altieri D
(2020)
An efficient approach for computing analytical non-parametric fragility curves
in Structural Safety
Estrada-Lugo H
(2021)
Dynamic Credal Networks for Resilience Assessment of Complex Engineering Systems
Estrada-Lugo H
(2020)
Resilience Assessment of Safety-Critical Systems with Credal Networks
Estrada-Lugo H.D.
(2020)
Resilience assessment of safety-critical systems with credal networks
in 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020
George-Williams H
(2022)
Uncertainty in Engineering - Introduction to Methods and Applications
Lye A
(2021)
Sampling methods for solving Bayesian model updating problems: A tutorial
in Mechanical Systems and Signal Processing
Morais C
(2020)
Analysis and Estimation of Human Errors From Major Accident Investigation Reports
in ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Morais C
(2022)
Robust data-driven human reliability analysis using credal networks
in Reliability Engineering & System Safety
Morais C
(2022)
Identification of human errors and influencing factors: A machine learning approach
in Safety Science
| Description | Resilience engineering is considered to offer significant benefits when considering the effectiveness of safety-critical systems on potentially hazardous plants. This approach looks at designing systems that are capable of experiencing threats and have several approaches (known as dimensions) which enable the system to avoid, withstand, adapt to or recover from their effects. This project examines the benefits that resilience engineering could offer in the context of nuclear safety systems. It indicates the models and data required to predict the resilience of a nuclear power generation plant. Such models will be formulated and applied to a demonstrator system. Through this predictive tool, modern nuclear systems can be designed and operated to achieve high levels of safety demanded. Special attention in the framework will be given to deliberated, intended cyber-attacks and also the role in which humans can play in the recovery of the system following a threat. |
| Exploitation Route | Yes, the outcomes of the project and the associated code and algorithm will be available at the end of the project |
| Sectors | Construction Digital/Communication/Information Technologies (including Software) Energy Environment |
| Description | The proposed approach has been employed to a safety-related system of a nuclear reactor to assess the resilience subjecting to various threat scenarios. The resilience metrics for all the possible threat sequences have been quantified which are helpful in logical decision making. |
| First Year Of Impact | 2020 |
| Sector | Aerospace, Defence and Marine,Construction,Digital/Communication/Information Technologies (including Software),Energy |
| Description | Enhanced Methodologies for Advanced Nuclear System Safety (eMEANSS) |
| Amount | £854,922 (GBP) |
| Funding ID | EP/T016329/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 01/2022 |
| End | 11/2025 |
| Description | FIS360 Innovation Consultant |
| Amount | £25,000 (GBP) |
| Funding ID | NNL/GC_551 |
| Organisation | National Physical Laboratory |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 12/2021 |
| End | 03/2022 |
| Description | NDA Bursary scholarship |
| Amount | £65,000 (GBP) |
| Organisation | National Nuclear Laboratory |
| Sector | Public |
| Country | United Kingdom |
| Start | 09/2020 |
| End | 09/2024 |
| Title | Toolboox for OpenCossan |
| Description | OpenCOSSAN is a tool for uncertainty quantification and management. It represents the core of COSSAN software under continuous development at the Institute for Risk and Uncertainty,University of Liverpool, UK. All the algorithms and methods have been coded in a Matlab toolbox allowing numerical analysis, reliability analysis, simulation, sensitivity, optimization, robust design. OpenCossan is coded exploiting the object-oriented Matlab programming environment, where it is possible to define specialized solution sequences, which include reliability methods, sensitivity analysis, optimization strategies, surrogate models and parallel computing strategies. The computational framework is organized in packages. A package is a namespace for organizing classes and interfaces in a logical manner, which makes large software project OpenCossan easier to manage. A class describes a set of objects with common characteristics such as data structures and methods. Objects, that are instances of classes can be aggregated forming more complex objects and proving solutions for practical problem in a compact, organized and manageable format. The structure of the software allows for extensive modularity and efficient code re-utilization. Objects (instances of a class) can be aggregated forming more complex objects with methods providing solutions for practical problem in a compact, organized and manageable format. |
| Type Of Technology | Software |
| Year Produced | 2017 |
| Open Source License? | Yes |
| Impact | Bayesian Belief Networks, more commonly known as Bayesian Networks, are a probabilistic graphical model based on the use of directed acyclic graphs, integrating graph theory with the robustness of Bayesian statistics. The graphical framework of such models consists of nodes, representing the variables of the problem of interest, connected to each other by edges, generally arrows, that depict the dependency link existing between two nodes. The main aim of the Bayesian Network approach is to factorize the probability of a complex event exploiting the knowledge regarding the dependencies existing among its sub-parts. In order to overcome the limitations associated with traditional Bayesian Networks, the integration of such approach with the imprecise probability theory has attracted increasing attention in the scientific community leading to the formulation and study of Credal Networks. Further efforts and research are strongly required in order to enhance the attractivness of Credal Networks outside the academic world and to ensure the reliability and efficiency of their performance in real-world applications. These aims represent the core of the Credal Networks toolbox developed within the OpenCossan framework: well known and novel methodologies are integrated in the software in order to provide the implementation, manipulation and analysis of Credal Networks. |
| URL | http://www.cossan.co.uk/software/open-cossan-engine.php |
| Description | HUMAN RELIABILITY AND INTELLIGENT AND AUTONOMOUS SYSTEMS Workshop |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | The main objective of this workshop is to foster research and collaborations on methods, applications, related to novel domains for human reliability, intelligent and autonomous systems, the interaction with cyber threats and the consequences on resilience of systems. The workshop will provide researchers a forum to present the technical talks and exchange the knowledge for successful implementation of the collaborative research activities. |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://sites.google.com/view/workshop-human-reliability/ |