A Resilience Modelling Framework for Improved Nuclear Safety (NuRes)

Lead Research Organisation: University of Strathclyde
Department Name: Civil and Environmental Engineering

Abstract

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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 11/2021 
End 11/2024
 
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 10/2020 
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/