Thesis title: 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.