Tommaso Giovanni Baffetti
DC15: Digital twin for the design exploration and optimisation of a H2 combustion chamber
In this project, I will develop a data-driven digital twin for the analysis and optimisation of hydrogen combustion systems. I will focus on building reduced-order models capable of capturing the complex dynamics of reactive flows while remaining computationally efficient. Using numerical simulations and experimental data, I will train a hybrid framework combining autoencoders and transformer architectures to learn spatial and temporal correlations between features. This approach will enable fast prediction of system dynamics and support the exploration of the combustion chamber design space, to identify operating conditions that ensure stable hydrogen combustion while minimising pollutant emissions.
Supervisor Prof. Alessandro Parente hosted by
1st secondment: industry hosted by
About me
I obtained my Master’s degree in Aerospace Engineering, with a specialisation in Space Engineering, from Politecnico di Torino in 2025. In the academic year 2023–2024, I spent a year at RWTH Aachen University in Germany through the Erasmus+ programme, where I conducted my Master’s thesis at the Institut für Technische Verbrennung (ITV). The research focused on the use of convolutional neural networks for modelling intrinsic flame instabilities in lean hydrogen flames, combining my interests in fluid dynamics and machine learning. Alongside my academic studies, I gained practical experience as a Simulation Engineer and Mission Analysis Engineer within aerospace student teams in Italy and Germany. In addition, I completed a four-month internship as a Propulsion Engineer at a space company in Turin, where I worked on feed-system simulations. I am now excited to pursue my PhD within the DT-HATS project, where I aim to develop data-driven reduced-order models for hydrogen combustion systems using machine learning approaches such as transformer architectures. I look forward to deepening my expertise in combustion modelling and contributing to the development of digital twin technologies for next-generation sustainable transport systems.
My biggest motivation is my desire to become a true expert in the field, capable of answering scientific questions with expertise and creativity.