Giovanni Sansavini: Catalogue data in Spring Semester 2023 |
| Name | Prof. Dr. Giovanni Sansavini |
| Field | Reliability and Risk Engineering |
| Address | Reliability and Risk Engineering ETH Zürich, LEE L 201 Leonhardstrasse 21 8092 Zürich SWITZERLAND |
| Telephone | +41 44 632 50 38 |
| sansavig@ethz.ch | |
| URL | http://www.rre.ethz.ch/ |
| Department | Mechanical and Process Engineering |
| Relationship | Associate Professor |
| Number | Title | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 151-0280-00L | Advanced Techniques for the Risk Analysis of Technical Systems | 4 credits | 2V + 1U | G. Sansavini, B. Gjorgiev | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course provides advanced tools for the risk/vulnerability analysis and engineering of complex technical systems and critical infrastructures. It covers application of modeling techniques and design management concepts for strengthening the performance and robustness of such systems, with reference to energy, communication and transportation systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students will be able to model complex technical systems and critical infrastructures including their dependencies and interdependencies. They will learn how to select and apply appropriate numerical techniques to quantify the technical risk and vulnerability in different contexts (Monte Carlo simulation, Markov chains, complex network theory). Students will be able to evaluate which method for quantification and propagation of the uncertainty of the vulnerability is more appropriate for various complex technical systems. At the end of the course, they will be able to propose design improvements and protection/mitigation strategies to reduce risks and vulnerabilities of these systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Modern technical systems and critical infrastructures are complex, highly integrated and interdependent. Examples of these are highly integrated energy supply, energy supply with high penetrations of renewable energy sources, communication, transport, and other physically networked critical infrastructures that provide vital social services. As a result, standard risk-assessment tools are insufficient in evaluating the levels of vulnerability, reliability, and risk. This course offers suitable analytical models and computational methods to tackle this issue with scientific accuracy. Students will develop competencies which are typically requested for the formation of experts in reliability design, safety and protection of complex technical systems and critical infrastructures. Specific topics include: - Introduction to complex technical systems and critical infrastructures - Basics of the Markov approach to system modeling for reliability and availability analysis - Monte Carlo simulation for reliability and availability analysis - Markov Chain Monte Carlo for applications to reliability and availability analysis - Dependent, common cause and cascading failures - Complex network theory for the vulnerability analysis of complex technical systems and critical infrastructures - Basic concepts of uncertainty and sensitivity analysis in support to the analysis of the reliability and risk of complex systems under incomplete knowledge of their behavior Practical exercitations and computational problems will be carried out and solved both during classroom tutorials and as homework. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Slides and other materials will be available online | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | The class will be largely based on the books: - "Computational Methods For Reliability And Risk Analysis" by E. Zio, World Scientific Publishing Company - "Vulnerable Systems" by W. Kröger and E. Zio, Springer - additional recommendations for text books will be covered in the class | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Fundamentals of Probability | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-2020-00L | Reliability Engineering and Quantitative Risk Analysis | 4 credits | 2V + 1U | G. Sansavini, V. Dang, L. Podofillini | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course introduces the fundamentals of reliability and risk analysis, from basic probability concepts to advanced computational techniques, for a variety of industrial application fields. The overall aim is to provide students with a package of methods for systematic and comprehensive system analyses to inform reliability, safety, and resilience. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | At the end of the course, the students will be able to: • Develop risk analysis models for industrial systems (scenarios, undesired events, consequences) • Apply representative tools for quantitative risk analysis (Fault trees and event trees) • Use appropriate measures to understand the contributors (events, components) to system risk • Analyze risk contribution for human factors and incorporate in risk model • Characterize and propagate uncertainties in the risk model parameters - Apply methods to design reliability-, safety-, and resilience-enhancing solutions | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Risk definitions and risk/consequence diagrams; Fault trees and event trees; Boolean logic; risk importance measures; component models; reliability data; human reliability analysis; dependent failures and common causes of failure; Bayesian probability theory; uncertainty modeling and propagation; resilience analysis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 364-1058-00L | Risk Center Seminar Series | 0 credits | 2S | H. Schernberg, D. Basin, A. Bommier, D. N. Bresch, S. Brusoni, L.‑E. Cederman, P. Cheridito, F. Corman, H. Gersbach, C. Hölscher, K. Paterson, G. Sansavini, B. Stojadinovic, B. Sudret, J. Teichmann, R. Wattenhofer, U. A. Weidmann, S. Wiemer, R. Zenklusen | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | In this series of seminars, invited speakers discuss various topics in the area of risk modelling, governance of complex socio-economic systems, managing risks and crises, and building resilience. Students, PhD students, post-docs, faculty and individuals outside ETH are welcome. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Participants gain insights in a broad range of risk- and resilience-related topics. They expand their knowledge of the field and deepen their understanding of the complexity of our social, economic and engineered systems. For young researchers in particular, the seminars offer an opportunity to learn academic presentation skills and to network with an interdisciplinary scientific audience. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Academic presentations from ETH faculty as well as external researchers. Each seminar is followed by a Q&A session and (when permitted) a networking Apéro. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | The sessions are recorded whenever possible and posted on the ETH Risk Center webpage. If available, presentation slides are shared as well. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Each speaker will provide a literature review. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | In most cases, a quantitative background is required. Depending on the topic, field-specific knowledge may be required. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Competencies |
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