Stefano Riva

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Verified

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• Master of Science
• PhD Student at Politecnico di Milano

Magnetohydrodynamics (MHD) studies the dynamics of electrically conducting fluids under the influence of a magnetic field and it is relevant in several nuclear applications. However, the high computational cost of multi-physics MHD simulations poses a challenge. Reduced Order Modelling (ROM) offers a promising alternative, enabling lower-dimensiona...

In recent years, algorithms aiming at learning models from available data have become quite popular due to two factors: 1) the significant developments in Artificial Intelligence techniques and 2) the availability of large amounts of data. Nevertheless, this topic has already been addressed by methodologies belonging to the Reduced Order Modelling...

The recent developments in data-driven methods have paved the way to new methodologies to provide accurate state reconstruction of engineering systems; nuclear reactors represent particularly challenging applications for this task due to the complexity of the strongly coupled physics involved and the extremely harsh and hostile environments, especi...

The Shallow Recurrent Decoder networks are a novel paradigm recently introduced for state estimation, combining sparse observations with high-dimensional model data. This architecture features important advantages compared to standard data-driven methods including: the ability to use only three sensors (even randomly selected) for reconstructing th...

Magnetohydrodynamics (MHD) investigates the intricate relationship between electromagnetism and fluid dynamics, offering a complete insight into the behavior of conducting fluids under the influence of magnetic fields. This theory plays a pivotal role in the framework of magnetic confinement fusion, where it can be applied to describe both thermonu...

Over the years, the development of Data-Driven Reduced Order Modelling (DDROM) techniques has paved the way for novel approaches to combine the physical knowledge built in high-fidelity simulations with the physical observations from experimental measurements. On the one hand, these approaches allow updating and correcting the background informatio...

Generally speaking, the latest advancements in Machine Learning and Artificial Intelligence have been made possible thanks to the availability of large amounts of data, as they provide sufficient information for the training phase. However, the nuclear reactor field is still far from this reality, for multiple reasons: the computational cost of the...

Innovative reactor technologies in the framework of Generation IV are usually characterised by harsher and more hostile environments compared to standard nuclear systems, for instance , due to the use of liquid metals as coolant or the adoption of liquid fuel (uranium or thorium-based molten salts). Such designs pose more challenges in the monitori...

Learning models from data has become quite popular recently due to the significant developments in artificial intelligence and the availability of large amounts of data. Nevertheless, this topic has already been addressed in the past by methodologies belonging to the Reduced Order Modelling framework: in particular, one of the most famous equation-...

The description of fluid motion is typically carried out either considering continuum matter or collision models (Lattice Boltzmann methods), with the former being the most common: in this framework, differential balance equations are derived from conservation principles and are solved to obtain density, velocity, pressure and temperature fields. I...

Reliable, real-time state estimation in nuclear reactors is of critical importance for monitoring, control and safety. It further empowers the development of digital twins that are sufficiently accurate for real-world deployment. As nuclear engineering systems are typically characterised by extreme environments, their in-core sensing is a challengi...

Mathematical modeling plays a crucial role in designing and obtaining licenses for nuclear reactor cores, particularly when many physics are involved e.g. neutronics, thermo-mechanics, thermal hydraulics etc. This becomes even more significant for innovative reactors like KRUSTY, which uses unique heat-pipe cooling and Uranium-Molybdenum monolith b...

Reactor safety and monitoring have historically been important and challenging aspects within nuclear power plants, and attention to these issues will grow even more in future years due to both the increase in the energy demand, including from fission plants, and the unique features of designs expected for Gen-IV reactors. Neutron flux measurement...

The state of an operating nuclear reactor depends on several physical phenomena that coexist and are interdependent: they can be taken into account simultaneously by adopting a multi-physics approach, allowing a higher level of detail of the system's properties. Neutron physics and thermal hydraulics are of great importance in this framework, their...

Due to the multiple physics involved and their mutual and complex interactions, nuclear engineers and researchers are constantly working on developing highly accurate Multi-Physics models, focusing in particular on the core coupling of Neutronics and Thermal-Hydraulics. Nevertheless, the development of accurate and stable models remains a challengi...

In 1927, Erwin Madelung developed an analogy between hydrodynamics and the quantum mechanics world by linking wave functions and the velocity of inviscid fluids by a suitable coordinate transformation; in particular, a wave function satisfying a Schrödinger equation results in an associated velocity field compliant with the Euler equations for invi...

For Generation-IV nuclear reactors, the problem of optimal sensor positioning and real-time estimation of the quantities of interest is an open problem. In particular, the harsh environment of fast reactors, both due to the high radioactive levels and the presence of non-conventional coolants such as liquid metals or molten salts, is such that in-c...

The nuclear industry is characterised by tight safety criteria, making the development of accurate multi-physics models to describe the most important phenomena occurring within the reactor a necessity. Among the various multi-physics coupling, the most fundamental is the one between neutronics and thermal-hydraulics, including the dependence of th...

Sensor positioning and real-time estimation of non-observable fields is an open question in the nuclear sector, especially for advanced nuclear reactors. In Circulating Fuel Reactors (CFR), liquid fuel and coolant are homogeneously mixed, and thus these reactors will not have internal structures, making sensor positioning in the primary circuit, in...

pyforce is a Python package implementing some Data-Driven Reduced Order Modelling (DDROM) techniques for applications to multi-physics problems, mainly set in the Nuclear Engineering world. These techniques have been implemented upon the dolfinx package (currently v0.6.0), part of the FEniCSx project, to handle mesh generation, integral calculation...

As first proposed by Madelung in 1926, the analogy between quantum mechanics and hydrodynamics has been known for a long time; however, its potentialities and the possibility of using the characteristic equations of quantum mechanics to simulate the behavior of inviscid fluids have not been thoroughly investigated in the past. In this methodology,...

The state of an operating nuclear reactor depends on several interdependent physical phenomena, which can be considered simultaneously by modelling the system using a multiphysics (MP) approach. MP allows a higher level of detail of the system’s properties at the expense of code complexity and computational burden, whereas, in the past, single-phys...

Due to the very stringent safety requirements of nuclear facilities, there is a need of developing precise and accurate computational tools for the reactor's safety analysis both during the licensing process and standard operation. Achieving this goal requires verification by other state-of-the-art neutronic codes and validation through comparison...

Nowadays, the state-of-the-art approach in numerical modelling for nuclear reactors is represented by the multi-physics (MP) analysis. This framework enables the investigation of the inter-dependency between different physics characterising a reactor (e.g., neutronics and thermal hydraulics) for a deeper understanding of the phenomena occurring in...

ROM4FOAM is a library written for OpenFOAM-v6 with a collection of several solvers implementing some non-intrusive ROM techniques.

Hybrid Data Assimilation (HDA) methods aim at combining the advantages of mathematical models and experimental observations by integrating Model Order Reduction techniques into a Data Assimilation framework, thus reducing the solution time whilst keeping the accuracy of the models to the desired level. HDA methods provide tools able to estimate the...

Hybrid Data Assimilation (HDA) methods are a class of numerical methods that aim at integrating Model Order Reduction (MOR) techniques into a Data Assimilation (DA) framework, thus combining mathematical models and experimental data. The objective is to reduce the solution time using MOR algorithms whilst keeping the accuracy of the models at the d...

The problem of sensor positioning and real-time estimation of non-observable fields is an open question in the nuclear sector, especially for what concerns advanced nuclear reactors. Those are complex engineering systems subjected to harsh environmental working conditions. For example, the Molten Salt Fast Reactor in its current proposed configurat...

The problem of estimating in real-time the state of a system by combining experimental data and models has been extensively addressed in literature. In particular, there have been a lot of developments in reduced order modelling techniques integrated in a data assimilation framework. This coupling allows to reconstruct the variable of interest cons...

The Empirical Interpolation Method (EIM), and its generalized version (GEIM), are non-intrusive, reduced-basis model order reduction methods hereby adopted and modified to address the problem of optimal placement of sensors and real-time estimation in thermo-hydraulics systems. These techniques have been used to extract the characteristic spatial m...

Computational fluid dynamics is the standard approach to simulate the behaviour of fluids governed by the Navier-Stokes equations. This problem always involves a suitable treatment of the non-linearity of the advection term in the equations, which is the main bottleneck in performing fast simulations. Moreover, as the Reynolds number increases, its...

Theoretical modelling of complex systems using Partial Differential Equations (PDE) is one of the most used techniques for design and optimization. The main limitation of this approach is the associated computational cost of the numerical solution, which prevents quick repetitive solving, necessary, for instance, in control applications. The hypoth...

Hybrid Data Assimilation (HDA) methods are a class of numerical methods that aim at integrating Model Order Reduction (MOR) techniques into a Data Assimilation (DA) framework, thus combining mathematical models and experimental data. The objective is to reduce the solution time using MOR algorithms whilst keeping the accuracy of the models at the d...

Hybrid Data Assimilation (HDA) methods aim at combining the advantages of mathematical models and experimental observations by integrating Model Order Reduction techniques into a Data Assimilation framework, thus reducing the solution time whilst keeping the accuracy of the models to the desired level. HDA methods provide tools able to estimate the...