Dr Edoardo Patelli
ZZ (DO NOT USE) was Civil Engineering and Industrial Design
- Personal WebsiteCossan Software
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Research
Generalised probabilistic approaches and advanced stochastic computational methods
In engineering practice, the evaluation of risk is often performed adopting over-simplified models and subjective judgements of experts which reduce considerably the credibility of the predictions in quantitative terms. Research is therefore required to develop more accurate models for risk assessment that are able to include vague and imprecise information and to identify the features, events and processes that influence the system integrity. Such models require in turn the availability of very efficient uncertainty quantification tools required to estimate the probability of occurrence, or intervals of probability, of events and their consequences.
Current research projects are:
* Uncertainty and Reliability of Systems and Networks
* Robust Design Optimization of Structural Systems
* Efficient Numerical Methods for Uncertainty Quantification in Engineering
General purpose software for uncertainty quantification and risk analysis
Computer-aided modelling and simulation is now widely recognised as the third `leg` of scientific method, alongside theory and experimentation. Many phenomena can be studied only by using computational processes such as complex simulations or analysis of experimental data. One of the greatest challenges of virtual prototyping is to improve the fidelity of the computational analysis. This can only be achieved by explicitly including variability and uncertainties from different sources.
Stochastic methods offer a much more realistic approach for analysis and design, but they are generally computational expensive. Hence, scalable computational tools are necessary, i.e. by making use of the computational power of a cluster and grid computing.
A multi-disciplinary software suite for uncertainty management and risk analysis is under development. The computational tools satisfy the industry requirements regarding numerical efficiency, flexibility, scalability and analysis of detailed models that can be used to solve a wide range of engineering and scientific problems. The availability of such software is particularly important for the analysis and design of resilient structures and safety critical systems. COSSAN software
Current research projects are:
* Reliability/robustness-based approaches and computational tools for multidisciplinary systems under mixed aleatory and epistemic uncertainty
* Efficient and Energy-Aware Software for Stochastic Analysis on Large-Scale Systems
* Efficient Numerical Methods for Uncertainty Quantification in Engineering
Risk analysis, Nuclear Safety and Probabilistic Risk Assessment
Risk is the potential of experiencing a loss when a system does not operate as expected due to the occurrence of uncertain, and difficult-to-predict events. Risk assessment requires the quantification of not only the direct cost of system failure but also the accompanying multi-faceted failure consequences that cascade across the boundaries of every disciplines and sectors of society. This is illustrated by a well known disaster such as the Fukushima nuclear incident, where natural events caused an avalanche of inter-related effects on the safety systems of the plant and subsequent contamination of the environment.
Probabilistic safety assessment methods is a generic term for the integrated analysis of risks arising from plant and processes which are undertaken by applying structured and systematic analysis techniques. Although some issues have been addressed in recent years, there is still a need to continue improving the probabilistic safety assessment methods. Every disaster is unique, and the availability of robust and fast predictive models able to deal with scarce and limited data is of fundamental importance, with the aim of having more realistic analysis that can support safety related decision at nuclear installations and mitigation actions will result in fewer deaths and less damage in case of severe accident.
Current research projects are:
* Smart Online Monitoring of Nuclear Power Plants
* Uncertainty Quantification and Risk Assessment Methods for Natural Hazards Triggering Simultaneous Failures
* Risk mitigation: Human errors and preventive design and project management
* Optimal Risk and Benefit Sharing and Management in Large Energy Projects
Research Grants
Informed mining: risk reduction through enhanced public and institutional risk awareness (IM AWARE)
ECONOMIC AND SOCIAL RESEARCH COUNCIL
November 2019 - March 2023
A Resilience Modelling Framework for Improved Nuclear Safety - NuRes
ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCIL
January 2019 - September 2021
Uncertainty Quantification in Fusion Power Plant Design
CULHAM CENTRE FOR FUSION ENERGY (UK)
October 2018 - October 2022
Realistic model prediction for managing risk in nuclear decommissioning Generating realistic synthetic plant data for future scenario analysis CDT
NATIONAL NUCLEAR LABORATORY LTD (UK)
October 2017 - September 2021
Smart on-line monitoring for nuclear power plants (SMART)
ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCIL
December 2015 - May 2018
Climate Change and Extreme Weather Impact on Hydropower Installations.
DEPARTMENT FOR BUSINESS, INNOVATION AND SKILLS (UK)
April 2016 - March 2017
Large multipurpose platforms for exploiting renewable energy in open seas (PLENOSE).
EUROPEAN COMMISSION
May 2014 - April 2018
Research Collaborations
Jiamei Deng
External: Leeds Beckett University
Smart Online Monitoring of Nuclear Power Plants
Rong-Jiun Sheu
External: National Tsing Hua University
Radiological safety evaluation and uncertainty analysis related to spent fuel storage installation or nuclear decommissioning. Dual PhD programme in Nuclear Safety
Zhan KANG (亢战)
External: Dalian University of Technology
Structural optimization under non-probabilistic uncertainties.
Virtual Engineering Centre
Internal
Robust Design. Uncertainty quantification.
Developing and exploit a general purpose software for uncertainty quantification and risk analysis