We build new methodologies Monte Carlo analysis and Perturbation Theory
We combine Computational Fluid Dynamics and particle transport in reactor analysis
Adopting ensemble weather forecast we deliver the most reliable energy predictions
We use Bayesian Machine Learning for robust Uncertainty Quantification
We develop innovative approaches for stochastic adjoint particle transport
Reduced Order Models drive us towards accuracy and computational efficiency
On 15th January 2019, MMP has been selected in the Call for Growth by Next Energy (Cariplo Factory) to access the Engage Phase and start a collaborative service development and…
MMP works on reactivity evolution of fuel salt during its draining.
We study the behavior of the ThorCon MSR during salt draining through the freeze valves.
We work on the heat transfer between the drain tanks and the surrounding structure for the ThorCon MSR reactor.
Lead-cooled Fast Reactors (LFRs) are studied in the frame of the OCAPIE project, coordinated by the Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Genova. The purpose of this work…
We developed a Machine Learning system capable of estimating statistical inflows in the hydroelectric power plants of the whole European Union.
We have been selected by ENTSO-E to develop an electric load forecasting tool both mathematically advanced and easy to use: we tackled this task by developing TRAPUNTA.
