Publications related to Optimisation of used nuclear fuel canister loading using a neural network and genetic algorithm

Atomic level insight into irradiation effects in nuclear fuel materials

Microstructural evolution during in-pile irradiation, radiation damage effects and fission products behavior in UO2 nuclear fuel are key issues in understanding and for the modeling of the performance as well as safety characteristics of nuclear fuels in t ...

EPFL2024

Intra-pin Reaction Rate Measurements in the CROCUS Experimental Reactor

Andreas Pautz, Vincent Pierre Lamirand, Mathieu Hursin, Oskari Ville Pakari, Thomas Jean-François Ligonnet, Tom Mager

In response to the need for validating high-fidelity deterministic neutronics solvers capable of providing pin-resolved neutron flux distributions, intra-pin reaction rates measurements were performed in the CROCUS experimental reactor. A specially designe ...

2024

Experimental study and interpretative modelling of the Power Exhaust in Configurations with Multiple X-Points in TCV

Sophie Danielle Angelica Gorno

Nuclear fusion presents a promising clean energy source to mitigate future energy crises, with magnetic confinement fusion well-positioned to provide a baseload scenario to power future reactors. The unmitigated power exhaust of such reactors threatens its ...

EPFL2024

Innovative thermophysical characterization of an ATF composite cladding: the case of silicon carbide

Andrea Cavaliere

Nuclear power is a powerful technology that plays an important role in the fight against climate change, and research is continuously engaged in studies that could further improve its safety. After the Fukushima accident, Accident Tolerant Fuels research h ...

EPFL2024

Gamma noise to non-invasively monitor nuclear research reactors

Andreas Pautz, Vincent Pierre Lamirand, Oskari Ville Pakari, Pavel Frajtag, Tom Mager

Autonomous nuclear reactor monitoring is a key aspect of the International Atomic Energy Agency’s strategy to ensure nonproliferation treaty compliance. From the rise of small modular reactor technology, decentralized nuclear reactor fleets may strain the ...

Nature Research2024

Neutron-Gamma Noise Measurements in a Zero-Power Reactor Using Organic Scintillators

Andreas Pautz, Vincent Pierre Lamirand, Oskari Ville Pakari

Noise measurements in light water reactor systems aid in generating validation data for integral point kinetic parameter predictions and generating monitoring parameters for reactor safety and safeguards. The CROCUS zero-power reactor has been used to supp ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Gamma-ray Spectroscopy in Low-Power Nuclear Research Reactors

Andreas Pautz, Vincent Pierre Lamirand, Oskari Ville Pakari

Gamma-ray spectroscopy is an effective technique for radioactive material characterization, routine inventory verification, nuclear safeguards, health physics, and source search scenarios. Gamma-ray spectrometers typically cannot be operated in the immedia ...

MDPI2024

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Mattia Atzeni

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

EPFL2024

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

EPFL Infoscience2024

Safe Deep Neural Networks

Kyle Michael Matoba

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

EPFL2024