Uncertainty propagation for the design study of the PETALE experimental programme in the CROCUS reactor

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

As a follow-up to the CEA-EPFL PETALE experimental program on stainless steel nuclear data, the EPFL initiated an open-loop pile-oscillation experimental program in the CROCUS reactor: BLOOM. A reproduction of the critical experiments of PETALE, the progra ...

Whereas the ability of deep networks to produce useful predictions on many kinds of data has been amply demonstrated, estimating the reliability of these predictions remains challenging. Sampling approaches such as MC-Dropout and Deep Ensembles have emerge ...

For the dimensioning and assessment of structures, it is common practice to compare action effects with sectional resistances. Extensive studies have been performed to quantify the model uncertainty on the resistance side. However, for statically indetermi ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

Most codes of practice adopt a semi probabilistic design approach for the dimensioning and assessment of structures. Accordingly, structural safety is ensured by performing limit state verifications using design values determined with adequately calibrated ...

Recently, there has been an arms race of pose forecasting methods aimed at solving the spatio-temporal task of predicting a sequence of future 3D poses of a person given a sequence of past observed ones. However, the lack of unified benchmarks and limited ...

High-level waste, stemming from nuclear electricity generation poses significant environmental and safety concerns. Currently, high-level wastes are stored in interim facilities needing constant monitoring and waiting for a definitive solution. Deep geolog ...

Image information about the state of a building after an earthquake, which can be collected without endangering the post-earthquake reconnaissance activities, can be used to reduce uncertainties in response predictions for future seismic events. This paper ...

Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning ma ...

The measurement of Z boson production is presented as a method to determine the integrated luminosity of CMS data sets. The analysis uses proton-proton collision data, recorded by the CMS experiment at the CERN LHC in 2017 at a center-of-mass energy of 13 ...

We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the ...

As large, data-driven artificial intelligence models become ubiquitous, guaranteeing high data quality is imperative for constructing models. Crowdsourcing, community sensing, and data filtering have long been the standard approaches to guaranteeing or imp ...

In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

This article outlines the advancements made in broadening the application scope of the OpenMC neutron transport code to include thermohydraulic coupling and nuclear data uncertainty propagation. These developments primarily involve the incorporation of the ...

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 ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

Uncertainty of spillover effects – including property devaluation - from proposed land-use change elicits opposition to local development. This hinders cities’ ability to implement land-use policy aimed at housing affordability and environmental sustainabi ...

Higher-order asymptotics provide accurate approximations for use in parametric statistical modelling. In this thesis, we investigate using higher-order approximations in two-specific settings, with a particular emphasis on the tangent exponential model. ...

Increasing adoption of smart meters and phasor measurement units (PMUs) in power distribution networks are enabling the adoption of data-driven/model-less control schemes to mitigate grid issues such as over/under voltages and power-flow congestions. Howev ...

A novel approach for robust controller synthesis, which models uncertainty as an elliptical set, is proposed in the paper. Given a set of frequency response functions of linear time-invariant (LTI) multiple-input multiple-output (MIMO) systems, the approac ...

Uncertainty propagation for the design study of the PETALE experimental programme in the CROCUS reactor

Chat with Graph Search

Design of an Open-Loop Pile-Oscillation Program in the CROCUS Reactor

ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference

Model uncertainties in action effects and load bearing capacity calculation in statically indeterminate reinforced concrete structures

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Influence of model uncertainty and long term deformations in action effects calculation in reinforced concrete structures

Toward Reliable Human Pose Forecasting With Uncertainty

A FEM modelling workflow to simulate THM effects in the rock around nuclear waste packages

Reducing uncertainties in response predictions of earthquake-damaged masonry buildings using data from image-based inspection

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Luminosity determination using Z boson production at the CMS experiment

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Extensions of Peer Prediction Incentive Mechanisms

Gibbs sampling the posterior of neural networks

Tool Developments in the OpenMC Code: Correlated Sampling and Transient Fission Matrix Approach Coupled to OpenFOAM

Intra-pin Reaction Rate Measurements in the CROCUS Experimental Reactor

Spectral Estimators for High-Dimensional Matrix Inference

Using automated design appraisal to model building-specific devaluation risk due to land-use change

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Experimental Validation of Model-less Robust Voltage Control using Measurement-based Estimated Voltage Sensitivity Coefficients

Data-driven IQC-Based Robust Control Design for Hybrid Micro-disturbance Isolation Platform