Monte Carlo methods, safety and risk analysis (Monte Carlo methods, safety and risk analysis A)

PART A – MONTE CARLO METHODS At the end of the course the student should know: ELO 1 - the fundaments of probability and statistics; ELO 2 - the theory and main concepts at the basis of the Monte Carlo method; ELO 3 - the correct approach to the generation of averages ans statistical error when dealing with sampling and experimental measurements; ELO 4 - the correct approach to the interpretation of measurements and computational results affected by statistical uncertainty; At the end of the course the students should be able to: ELO 5 - apply the Monte Carlo method for the solution of problems, by i) correctly identifying the statistical phenomena involved, ii) carry our the random sampling and iii) perform the corresponding statistical analysis to evaluate averages and uncertainties ELO 6 - apply the Monte Carlo approach to problems involving statistical phenomena in different fields of application of interest for the enegy and nuclear engineering field (e.g. risk analysis, safety assessments, neutral particle transport) ELO 7 - critically evaluate the results obtained by the application of the Monte Carlo method, understanding the role of the statistical uncertainity and its impact on the reliability of the results obtained

Monte Carlo methods, safety and risk analysis (Monte Carlo methods, safety and risk analysis B)

PART B – SAFETY AND RISK ANALYSIS The objective of this part of the course is to provide the students with competences on: (i) techniques for hazard identification; (ii) methodologies for safety (resp., risk) evaluation and analysis; (iii) methods for the control and reduction of the risks associated to the operation of energy (e.g., nuclear) and, in general, industrial systems and plants (preventive and mitigative actions). These multidisciplinary and transversal competences, typical of safety and reliability analysts and managers, are necessary in every field of modern engineering for the design and operation of safe and reliable systems. At the end of the course the students should know: ELO 1 – the (qualitative and quantitative) concepts and definitions of hazard, safety, risk, prevention and mitigation. ELO 2 – the main contents of the legislation about safety and risk of energy and industrial plants. ELO 3 – the systemic and systematic formulation of the safety and risk analysis process. ELO 4 – basic notions on the (qualitative and semi-quantitative) methods for hazard and scenario identification. ELO 5 – the quantitative approach to safety and risk analysis, and the related (analytical and stochastic simulation-based) evaluation techniques. ELO 6 – basic notions on the methodological tools to treat the uncertainty in the reliability and risk assessments and to build confidence in the corresponding results. At the end of the course the students should be able to: ELO 7 – develop and apply the (qualitative and quantitative, analytical, and stochastic simulation-based) methods for the safety and risk analysis of energy and industrial systems and plants. ELO 8 – critically evaluate the results obtained by the application of the methods learned, also in relation to the uncertainty and confidence, with which they can be used to take robust decisions about preventive, protective, mitigative and reactive measures. ELO 9 – communicate the results of their own activity in a technically sound way.

Monte Carlo methods, safety and risk analysis (Monte Carlo methods, safety and risk analysis A)

Fundamentals of mathematics, applied thermodynamics and physics; concepts on process plants, thermal-hydraulics and fluid dynamics, basics of radiation and particle transport. Basic knowledge of MATLAB.

Monte Carlo methods, safety and risk analysis (Monte Carlo methods, safety and risk analysis B)

PART B – SAFETY AND RISK ANALYSIS Fundamentals of mathematics and physics. Basic concepts on process (energy) plants. Basic knowledge of MATLAB.

Monte Carlo methods, safety and risk analysis (Monte Carlo methods, safety and risk analysis A)

PART A - MONTE CARLO METHODS -1- Probability and statistics a. Concept of probability and its properties b. Probability density functions, expected value and variance c. Simulation of random event - sample average d. Tchebycheff inequality and Central Limit Theorem e. Statistical laws of interest for applications in the energy and nuclear engineering field f. Properties of correlated statistical quantities -2- The Monte Carlo method a. Origin ad motivations b. Sampling methodologies c. Simulation of discrete and continuous random walks d. Applications of the Monte Carlo method to engineering problems: neutron propagation, radiative heat transfer, evaluation of energy plants performance, evaluation of integrals, uncertainty quantification ... e. Introduction to the Monte Carlo neutron transport code Serpent

Monte Carlo methods, safety and risk analysis (Monte Carlo methods, safety and risk analysis B)

PART B – SAFETY AND RISK ANALYSIS 1. Introduction and description of the contents of the course: general overview of the safety and risk analysis topics offered and importance of the related transversal and multidisciplinary competences within the Master Degree in Energy and Nuclear Engineering (1.5h). 2. Concepts of hazard, safety, risk (and risk tolerability), prevention and mitigation: qualitative and quantitative definitions. (3h) 3. Introduction (basics) on the Italian and European legislation on safety and risk in the design and operation of energy plants [safety of workers, safety of components, machines and systems, safety of plants subject to major accidents (e.g., nuclear plants, refineries, chemical plants, oil&gas installations)]. (3h) 4. Quantitative Risk Assessment (QRA) framework: (11h) a) Comparison between deterministic and probabilistic approaches: respective strengths and weaknesses. b) Basic notions on the qualitative (and/or semi-quantitative) methods for the identification of hazards in the safety (resp., risk) analysis of energy (e.g., nuclear) and, in general, industrial systems and plants: (i) Failure Mode Effect and Criticality Analysis (FMECA) method (for the identification of system failure modes and the corresponding criticality and maintainability analyses); (ii) HAZard and OPerability analysis (HAZOP) method (for the identification of process anomalies). c) Details on the quantitative techniques adopted in the safety (resp., risk) analysis of energy (e.g., nuclear) and, in general, industrial systems and plants: (i) Event Trees, for the identification of the possible accidental sequences (scenarios); (ii) Fault Trees and Reliability Block Diagrams, for the quantification of the probabilities/frequencies of the accidental sequences (scenarios); (iii) Common Cause Failure analysis, for the treatment of dependent failures between components. d) Risk matrices and curves, for a qualitative and quantitative ranking of the criticality of the hazards and scenarios identified. 5. Quantitative (both analytical and stochastic, simulation-based) methodologies for the (time-dependent) reliability, availability and maintainability analyses of equipment and components employed in energy (e.g., nuclear) and, in general, industrial systems and plants: (25.5h) a) Definitions of (time-dependent) system reliability, availability and maintainability. b) Statistical methods for the estimation of reliability, availability and maintainability parameters from field data (and the related confidence): method of moments, method of the Maximum Likelihood Estimation. c) Analysis and mathematical modeling of realistic procedures like inspection, maintenance, repair. d) Importance measures for the assessment of the criticality of industrial (energy) equipment and components. e) Basic examples of Monte Carlo Simulation strategies (see also Part A of the Course) applied to the estimation of (time-dependent) system reliability, availability and maintainability indicators. 6. The problem of uncertainty and its analysis: basic notions on the methodological tools to treat uncertainty in the reliability and risk assessments of industrial (energy) systems and to build confidence in the corresponding results. (3h) 7. Basic notions and examples of application of more advanced and innovative computational techniques for the safety and risk analyses of complex, safety-critical (nuclear) systems. In particular: i) adaptive simulation methods for the dynamic Integrated Deterministic and Probabilistic Safety Assessment (IDPSA); ii) statistical techniques (e.g., supervised and unsupervised clustering) and artificial intelligence methods (e.g., Artificial Neural Networks, AutoEncoders) for the prompt (on-line) detection and diagnosis of faults; iii) soft-computing algorithms, regression techniques and Reduced Order Models (ROMs) for emulating long-running (thermal-hydraulic) codes at a reduced computational cost. (3h) 8. Case studies: lectures will be complemented by examples concerning the safety and risk analyses of realistic energy components, systems and plants exposed to hazards, as well as by quantitative exercise sessions developed by the teachers and/or by the students themselves (on paper or using laptops). (the number of hours is included in the previous bullet items)

Monte Carlo methods, safety and risk analysis (Monte Carlo methods, safety and risk analysis A)

Monte Carlo methods, safety and risk analysis (Monte Carlo methods, safety and risk analysis B)

Monte Carlo methods, safety and risk analysis (Monte Carlo methods, safety and risk analysis A)

PART A – MONTE CARLO METHODS All the concepts explained during lectures are applied directly by the professor during the lectures. Dedicated exercise sessions in class complement the understanding and allow the students to be confronted with more exercises. The students are also given suggestions for further individual exercises to be carried out at home. Exercise sessions at the computer are also envisaged, with the use of the programming language MATLAB. An introduction to the use of the Serpent code for the simulation of neutron transport for applications of interest in nuclear engineering is provided.

Monte Carlo methods, safety and risk analysis (Monte Carlo methods, safety and risk analysis B)

PART B – SAFETY AND RISK ANALYSIS Lectures by the teachers will be complemented by examples concerning the safety and risk analyses of realistic energy and industrial components, systems and plants exposed to hazards, as well as by quantitative exercise sessions carried out by the teachers and/or by the students themselves (on paper or using laptops).

Monte Carlo methods, safety and risk analysis (Monte Carlo methods, safety and risk analysis A)

PART A – MONTE CARLO METHODS - G. Vicario and R. Levi, Statistica e probabilità per ingegneri, Progetto Leonardo, Bologna, 2001 - S. M. Ross, Introduction to probability and statistics for engineers and scientists, Wiley, New York, 1987 - L. Lux and L. Koblinger, Monte Carlo particle transport methods : neutron and photon calculations, CRC, Boca Raton, 1991. - J. Spanier and E. M. Gelbard, Monte Carlo Principles and Neutron Transport Problems, Dover, 2008 - Lecture notes provided by the professor

Monte Carlo methods, safety and risk analysis (Monte Carlo methods, safety and risk analysis B)

PART B – SAFETY AND RISK ANALYSIS MANDATORY MATERIAL - Booklets and slides provided by the teachers. OPTIONAL MATERIAL Safety and Risk analysis (general): - E. Zio, “Introduction to the basics of reliability and risk analysis”, Editore: World scientific, Anno edizione: 2007. - E. Zio, “Computational methods for reliability and risk analysis”, Editore: World scientific, Anno edizione: 2009. - P. Baraldi, F. Cadini, E. Zio, “Introduction to reliability and risk analysis: worked out problems”, Editore: World Scientific, Anno edizione: 2011. - W. Kroger, E. Zio, “Vulnerable Systems”, Editore: World Scientific, Anno edizione: 2011. - T. Aven, P. Baraldi, R. Flage and E. Zio, “Uncertainty in risk assessment”, Editore: Wiley, Anno edizione: 2014. - A.K. Jardine, H.C. Tsang, A. Tsang, “Maintenance, Replacement, and Reliability: Theory and Applications”, Editore: CRC Press, Anno edizione: 2005. - European Safety and Reliability Association, “Maintenance Modeling and Applications”, Editore: ESRA, Anno edizione: 2010. Safety and Risk analysis (“nuclear”-oriented): - M. Modarres, I. S. Kim, “Deterministic and Probabilistic Safety Analysis - Handbook of Nuclear Engineering”, Editore: Springer, Anno edizione: 2010. - IAEA-TECDOC-1200, “Applications of probabilistic safety assessment (PSA) for nuclear power plants”, Anno edizione: 2001. - ASME, “Operation and Maintenance of Nuclear Power Plants”, Editore: ASME book, Anno edizione: 2009.

Monte Carlo methods, safety and risk analysis (Monte Carlo methods, safety and risk analysis A)

Lecture slides; Exercises; Exercise with solutions ; Video lectures (previous years);

Monte Carlo methods, safety and risk analysis (Monte Carlo methods, safety and risk analysis B)

Lecture slides; Lecture notes; Exercises; Exercise with solutions ; Video lectures (previous years);

Monte Carlo methods, safety and risk analysis (Monte Carlo methods, safety and risk analysis A)

Exam: Computer lab-based test; Written test;

Monte Carlo methods, safety and risk analysis (Monte Carlo methods, safety and risk analysis B)

Exam: Written test; Compulsory oral exam;

Monte Carlo methods, safety and risk analysis (Monte Carlo methods, safety and risk analysis A)

For Part A + B: The exam is carried out separately for the two parts of the course (A+B) and a grade >=18 is to be obtained on both parts. PART A – MONTE CARLO METHODS The exam aims at verifying the competence of the students on both basics statistics and on the application of the Monte Carlo Method. Therefore, the students are requested to solve some exercises, ranging from basic statistics to applications of the Monte Carlo method to simplified problems by developing small MATLAB programs (exam carried out at the PoliTO LAIB). The correct solution of all exercises proposed allows to obtain a maximum grade of 30/30 cum laude. The grade obtained by the exercise part can be modified (max +3 points, min -2 points, not compulsory) by answering to a theoretical question provided together with the exercises. The final grade saturates at 30/30 cum laude. The total duration of the test is 2.5 hours. Students are not allowed to use any material (books, notes, ...) during the final exam. FINAL GRADE The final grade of the exam is evaluated as the average of the grades obtained in the two parts of the exam, i.e. Monte Carlo Methods (part A) and Safety and Risk Analysis (part B), rounded to the upper integer.

Monte Carlo methods, safety and risk analysis (Monte Carlo methods, safety and risk analysis B)

For Part A + B: The exam is carried out separately for the two parts of the course (A+B) and a grade >=18 is to be obtained on both parts. For Part B, the evaluation consists in a written and oral exam. The written test includes both numerical exercises and very brief theoretical questions on all the macro-topics treated (items 2-8 of the Contents section). The exercises are mainly aimed at evaluating the student’s capability of applying the qualitative and quantitative methods for the safety and risk assessment of energy equipment and plants (ELO 7). The theoretical questions will allow verifying the student’s comprehension of the concepts of safety, risk, prevention and mitigation, his/her knowledge of the main legislation related to safety, of the qualitative methods of hazard and scenario identification and characterization, of the fundamentals of the systemic and quantitative approaches to the evaluation of safety and risk of energy plants (ELO 1-6). This part will allow verifying acquired (conceptual, theoretical, and possibly “foundational”) knowledge that cannot be evaluated by numerical exercises. The theoretical questions can be of different nature: (very brief) open questions (e.g., definitions of relevant concepts), or true/false or multiple-choice. The duration of the written exam is 2h-2h15' (max). The use of any learning resource (books, handouts, etc.) is not allowed. This part of the exam will contribute 65% of the final score. The oral exam (duration around 30 minutes) will mainly assess the student’s capability of autonomously and critically formulating, in a clear and convincing way, judgments about the confidence and uncertainty in the results of a safety and risk analysis (ELO 8, 9). This part of the exam will contribute 35% of the final score. The minimum score that allows passing the exam is 18/30. Maximum score is 30/30 cum laude. The final grade of the exam is evaluated as the average of the grades obtained in the two parts of the exam, i.e. Monte Carlo Methods (part A) and Safety and Risk Analysis (part B), rounded to the upper integer.