FRANCESCO ALDO TUCCI

Dottore di ricerca

ciclo: XXXV


supervisore: Prof. Alessandro Corsini

Titolo della tesi: Artificial Intelligence for Digital Twins in Energy Systems and Turbomachinery: development of machine learning frameworks for design, optimization and maintenance

The expression Industry4.0 identifies a new industrial paradigm that includes the development of Cyber-Physical Systems (CPS) and Digital Twins promoting the use of Big-Data, Internet of Things (IoT) and Artificial Intelligence (AI) tools. Digital Twins aims to build a dynamic environment in which, with the help of vertical, horizontal and end-to-end integration among industrial processes, smart technologies can communicate and exchange data to analyze and solve production problems, increase productivity and provide cost, time and energy savings. Specifically in the energy systems field, the introduction of AI technologies can lead to significant improvements in both machine design and optimization and maintenance procedures. Over the past decade, data from engineering processes have grown in scale. In fact, the use of more technologically sophisticated sensors and the increase in available computing power have enabled both experimental measurements and highresolution numerical simulations, making available an enormous amount of data on the performance of energy systems. Therefore, to build a Digital Twin model capable of exploring these unorganized data pools collected from massive and heterogeneous resources, new Artificial Intelligence and Machine Learning strategies need to be developed. In light of the exponential growth in the use of smart technologies in manufacturing processes, this thesis aims at enhancing traditional approaches to the design, analysis, and optimization phases of turbomachinery and energy systems, which today are still predominantly based on empirical procedures or computationally intensive CFD-based optimizations. This improvement is made possible by the implementation of Digital Twins models, which, being based primarily on the use of Machine Learning that exploits performance Big-Data collected from energy systems, are acknowledged as crucial technologies to remain competitive in the dynamic energy production landscape. The introduction of Digital Twin models changes the overall structure of design and maintenance approaches and results in modern support tools that facilitate real-time informed decision making. In addition, the introduction of supervised learning algorithms facilitates the exploration of the design space by providing easy-to-run analytical models, which can also be used as cost functions in multi-objective optimization problems, avoiding the need for time-consuming numerical simulations or experimental campaings. Unsupervised learning methods can be applied, for example, to extract new insights from turbomachinery performance data and improve designers’ understanding of blade-flow interaction. Alternatively, Artificial Intelligence frameworks can be developed for Condition-Based Maintenance, allowing the transition from preventive to predictive maintenance. This thesis can be conceptually divided into two parts. The first reviews the state of the art of Cyber-Physical Systems and Digital Twins, highlighting the crucial role of Artificial Intelligence in supporting informed decision making during the design, optimization, and maintenance phases of energy systems. The second part covers the development of Machine Learning strategies to improve the classical approach to turbomachinery design and maintenance strategies for energy systems.

Produzione scientifica

11573/1615012 - 2021 - Cascade With Sinusoidal Leading Edges: Identification And Quantification of Deflection With Unsupervised Machine Learning.
Corsini, Alessandro; Delibra, Giovanni; Tieghi, Lorenzo; Tucci, Francesco Aldo - 04b Atto di convegno in volume
congresso: ASME Turbo Expo 2021 (Virtual, On Line)
libro: Proceedings of the ASME Turbo Expo 2021: Turbomachinery Technical Conference and Exposition. Volume 1: - (978-079188489-8)

11573/1490640 - 2021 - A Machine-Learnt Wall Function for Rotating Diffusers
Tieghi, Lorenzo; Corsini, Alessandro; Delibra, Giovanni; Tucci, Francesco Aldo - 04b Atto di convegno in volume
congresso: ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition (Virtual, Online)
libro: Volume 1: Aircraft Engine; Fans and Blowers - (978-0-7918-8405-8)

11573/1545718 - 2021 - A Machine-Learnt Wall Function for Rotating Diffusers
Tieghi, Lorenzo; Delibra, Giovanni; Corsini, Alessandro; Tucci, Francesco Aldo - 01a Articolo in rivista
rivista: JOURNAL OF TURBOMACHINERY (American Society of Mechanical Engineers:22 Law Drive:Fairfield, NJ 07007:(800)843-2763, (973)882-1167, EMAIL: infocentral@asme.org, INTERNET: http://www.asme.org, Fax: (973)882-1717) pp. 1-9 - issn: 0889-504X - wos: WOS:000675351400003 (6) - scopus: 2-s2.0-85107536696 (7)

11573/1457255 - 2020 - Development of a data-driven model for turbulent heat transfer in turbomachinery
Tucci, Francesco Aldo; Delibra, Giovanni; Corsini, Alessandro - 04c Atto di convegno in rivista
rivista: E3S WEB OF CONFERENCES (Les Ulis : EDP Sciences, 2013-) pp. 1-11 - issn: 2267-1242 - wos: (0) - scopus: 2-s2.0-85097150983 (1)
congresso: 75th National ATI Congress – #7 Clean Energy for all (ATI 2020) (Rome, Italy)

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