CODE 98218 ACADEMIC YEAR 2024/2025 CREDITS 4 cfu anno 1 ENGINEERING TECHNOLOGY FOR STRATEGY (AND SECURITY) 10728 (LM/DS) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR ING-IND/09 LANGUAGE English TEACHING LOCATION GENOVA SEMESTER 1° Semester TEACHING MATERIALS AULAWEB OVERVIEW The course provides the fundamentals to robust design of systems and related procedures for moitoring and diagnostics, encompassing deterministic and stocastic approaches. Practical examples are derived from the mechanical engineering and energy system fields. AIMS AND CONTENT LEARNING OUTCOMES The module aims to illustrate how the design under uncertainty can help in modelling and design of the energy systems. The first part of the course will cover the necessary fundamentals of statistics. Then different uncertainty quantification methods will be presented, starting from sampling method like Monte Carlo and continuing with different approximated methods an overview of robust design will presented, focusing on the application of uncertainty quantification method in optimization problems. In the second part of the course, advanced techniques for Data Driven monitoring will be presented. Both methods will be applied to different case studies. AIMS AND LEARNING OUTCOMES At the end of the course the student will be able to: - approach and quantify the uncertainties impacting on a specific design problem - use mathematical methods for estimating the uncertainty in the design outputs - conduct a systematic design of experiment and treat design problems under uncertainty - apply robust design techniques to engineering problems - recognize the most common monitoring and diagnostic strategies - design a monitoring system selecting the most relevant parameters - select the most promising modelling approach on the basis of the available system information PREREQUISITES basic analytical skills, equation handling, basic computer coding skills TEACHING METHODS Lectures and exercises on PC SYLLABUS/CONTENT Part A – Robust Design of Systems Lecture 1 – Introduction and motivation Course introduction, practical needs and applications of uncertainty quantification and robust design techniques. Introduction to statistics: statistical analysis and main parameters (average, variance, probability distribution functions, etc.). Lecture 2 – Uncertainty definition and treatment Source and types of uncertainties (aleatory, epistemic). Uncertainty in measurements, recommended approach in scientific publications, ASME standards. Lecture 3 – Uncertainty estimation and Montecarlo simulation Overview of sampling and approximate methods for uncertainty quantification - UQ (Monte Carlo, response sensitivity analysis, polynomial chaos, design of experiment, response surface methodology), backward and forward UQ analysis. The reference sampling method: MonteCarlo simulation. The concept of Mean Square Pure Error (MSPE). Lecture 4 – Approximate methods Fundamentals and features of approximated methods for uncertainty quantification and propagation from inputs to outputs: the Response Sensitivity Analysis - RSA, the Polynomial Chaos - PC. Lecture 5 – Design of Experiment and Response Surface methods Fundamentals and features of approximated methods for uncertainty quantification and propagation from inputs to outputs: Response Surface Method – RSM, Design of Experiment – DOE. Lecture 6 – Robust design approach Robust design: fundamentals, optimization algorithm, combination with uncertainty quantification methods. Application examples: analytical function, cantilever beam, microgas turbine, gas-gas heat exchanger, advanced fuel cell energy system. Lecture 7 – Exercise on algebraic application MonteCarlo simulation and RSA method in Matlab environment: the Rosenbrock function. Lecture 8 – Exercise on engineering application Robust Design of a gas-gas heat exchanger: optimisation algorithm and uncertainty quantification. Part B – Monitoring and Diagnostics Lecture 1: Overview of the monitoring and diagnostics: objective, hurdles and methods General definitions, Maintenance Strategies, Monitoring application, Interaction between Monitoring and Control systems, Process Monitoring Design: Design of Experiment vs Monitoring Lecture 2: Industrial approach to monitoring and diagnostics Definitions by ISO 13372 2012: Condition monitoring and diagnostics of machines; Condition Monitoring; Diagnosis and Prognosis; Expert Systems for Monitoring and Diagnosis of Rotating Machinery Lecture 3: Dealing with measurement error: Data Reconciliation & Gross Error Detection Introduction to measurement errors; Comparison of data validation methods; DR and GED theory; problem formulation; Novel technique vs. traditional DR approach; VDI 2048 Exercise Lecture 4: Exercise on Data Reconciliation VDI 2048 Exercise solution: Linear solution with Lagrange Multiplier; Non-linear solution with Sequential Quadratic Programming. Lecture 5: Selection of monitoring and observation + detection strategy: First principles models and Data-Driven Models; Example of Fault detection and identification approaches; Fault simulation. Lecture 6: Dealing with high dimension Data Base: Principal Component Analysis PCA Theory; PCA for Dimensionality Reduction; Examples of PCA as regression model. Lecture 7: Selection of Regression Models Example of Regression Models; Model and Feature Selections, Technique for Data-Driven application; Evaluating the goodness of fit; Underfitting vs Overfitting; Exercise on Data sets Lecture 8: Real-Time Risk Management enabled by Industry 4.0 Industry 4.0; Industrial Internet of Things; Digital twin; Digital Factory; Risk Management in the Age of IIoT; State of the art of safety controls; Risk Assessment methods; Real-Time Risk Management. RECOMMENDED READING/BIBLIOGRAPHY Spiegel, M.R., Schiller, L.J.(1999), Statistics, McGraw Hill, NYC Montgomery, D.C. (2000), Design and Analysis of Experiments, John Wiley & Sons, New York Ralph C. Smith (2013), Uncertainty Quantification: Theory, Implementation, and Applications T.J. Sullivan (2015), Introduction to Uncertainty Quantification”, Springer Ghanem, Higdon, Owhadi, (2017), Handbook of Uncertainty Quantification, Springer Souza de Cursi, Sampaio, (2015), Uncertainty Quantification and Stochastic Modeling with Matlab, ISTE Press – Elsevier L. Eriksson, E. Johansson, N. Kettaneh-Wold, C. Wikström, and S. Wold (2001), Design of Experiments Marler, R. T., and Arora, J. S., 2004 “Survey of Multi-Objective Optimization Methods for Engineering” Structural and Multidisciplinary Optimization, 26(6), pp.269.295 Myers, R. H., and Montgomery, D. C., 2002, “Response Surface Methodology: Process and Product Optimization Using Designed Experiments” John Wiley & Sons Inc, USA. Law, A.l., Kelton, W. D., 1991, “Simulation Modeling and Analysis”, Mc Graw Hill Mäkelä, M., 2017, “Experimental Design and Response Surface Methodology in Energy Applications: A Tutorial Review” Energy Conversion and Management, 151(May), pp. 630–640. Kleijnen, J. P. C., 2005, “An Overview of the Design and Analysis of Simulation Experiments for Sensitivity Analysis” Eur. J. Oper. Res., 164(2), pp. 287–300. Tagushi, G., Yokoyama, Y., and Wu, Y., 1993, “Taguchi Methods: Design of Experiments” K. Kim, M.R. von Spakovsky, M. Wang, D.J. Nelson, 2012. “Dynamic optimization under uncertainty of the synthesis/design and operation/control of a proton exchange membrane fuel cell system, Journal of Power Sources, 205, pp. 252–263 Navarro, M., Witteveen, J., Blom, J., 2014. “Polynomial chaos expansion for general multivariate distributions with correlated variables” Seshadri, P., Narayan, A., Mahadevan, S., 2016. “Effectively Subsampled Quadratures For Least Squares Polynomial Approximations”. ArXiv e-prints Giunta, A.A., Eldred, M.S., Castro, J.P., 2004. “Uncertainty quantification using response surface approximations”, 9th ASCE Specialty Conference on Probabilistic Mechanics and Structural Reliability M. Cavazzuti, Optimization Methods: From Theory to Design, DOI: 10.1007/978-3-642-31187-1_3, Springer-Verlag Berlin Heidelberg, 2013. TEACHERS AND EXAM BOARD ALESSANDRO SORCE Ricevimento: contact only by mail at alessandro.sorce@unige.it LUCA MANTELLI Exam Board ALESSANDRO SORCE (President) LUCA MANTELLI (President Substitute) LESSONS LESSONS START https://corsi.unige.it/10728/p/studenti-orario Class schedule The timetable for this course is available here: Portale EasyAcademy EXAMS EXAM DESCRIPTION The first part on robust design requires a report containing: one chapter on theoretical topic of the course, one exercise on algebric equation, one exercise on engineering application. The second part on monitoring and diagnostics requires an oral exam. Students who have valid certification of physical or learning disabilities on file with the University and who wish to discuss possible accommodations or other circumstances regarding lectures, coursework and exams, should speak both with the instructor and with Professor Federico Scarpa (federico.scarpa@unige.it ), the School's disability liaison. ASSESSMENT METHODS Written and oral exam Exam schedule Data appello Orario Luogo Degree type Note 07/01/2025 09:30 SAVONA Orale 09/01/2025 11:00 GENOVA Orale 24/01/2025 14:30 GENOVA Orale 13/02/2025 11:00 GENOVA Orale 05/06/2025 11:00 GENOVA Orale 19/06/2025 11:00 GENOVA Orale 04/07/2025 11:00 GENOVA Orale 16/07/2025 11:00 GENOVA Orale 04/09/2025 11:00 GENOVA Orale