The advent of X-ray free-electron lasers (XFELs) has revolutionized fundamental science, from atomic to condensed matter physics, from chemistry to biology, giving researchers access to X-rays with unprecedented brightness, coherence and pulse duration. All XFEL facilities built until recently provided X-ray pulses at a relatively low repetition rate, with limited data statistics. Here, results from the first megahertz-repetition-rate X-ray scattering experiments at the Spectroscopy and Coherent Scattering (SCS) instrument of the European XFEL are presented. The experimental capabilities that the SCS instrument offers, resulting from the operation at megahertz repetition rates and the availability of the novel DSSC 2D imaging detector, are illustrated. Time-resolved magnetic X-ray scattering and holographic imaging experiments in solid state samples were chosen as representative, providing an ideal test-bed for operation at megahertz rates. Our results are relevant and applicable to any other non-destructive XFEL experiments in the soft X-ray range.

The purpose of this manuscript is to propose a novel methodology for conducting uncertainty analysis for event-driven indicators of system’s availability. In this approach the system’s availability depends on the frequency of components’ failures and their duration. For all the components the parameters that determine their behavior are obtained from statistical analysis, which means they are given with certain uncertainty, hence the system’s availability is also uncertain. In this paper we present a numerically effective algorithm that assesses uncertainty about system’s availability. A modular approach has been adopted, in which the uncertainty about the complete system’s availability is estimated by combining the information obtained for separate subsystems using the knowledge about the functional relationships between them. Furthermore, to assure numerical efficiency, a novel approach has been adopted, which combines screening approach with quasi-random Sobol sampling approach. The functionality of the proposed method is visualized on an illustrative example.

The purpose of this manuscript is to introduce a novel conceptual design of a method that allows integration of Bayesian Uncertainty Analysis (BUA) with Bayesian Sensitivity Analysis (BSA) for event-driven indicators of system's availability. Such Bayesian Integrated Global Uncertainty and Sensitivity Analysis (BIGUSA) procedure integrates the previously independently developed global UA and SA methods. The discussed approach is based on global exploration of parameters' space, which allows to capture higher-order interactions between correlated variables, thus avoiding common pitfalls of methods based on local state space explorations. The method is developed for a generic industrial facility characterized by the following input-output signals: (i) inputs - the stochastic events causing failures of individual assets; (ii) outputs - the availability, which is measured with suitable quantitative indicators of the facility. The objectives of the proposed BIGUSA are twofold: (i) description of the uncertainty profile of the availability of the analyzed facility (system) given the uncertainty profiles of the building blocks of the system (assets); (ii) assessment of how sensitive the availability of the facility is to individual failure events that might occur at the assets level. This is done by adopting the Bayesian approach, in which, at the higher level, the failure rates (FRs) are sampled from Bayesian priors which are fed to the stochastic simulation engine at the lower level of the algorithm. Stochastic realizations produced by such simulation engine are used to describe uncertainty profiles of the outputs (UA stage) and their sensitivity to variations in the input variables (SA stage). The former is performed by nonparametric density estimation, while the latter is assessed with Sobol indices, which measure how much of the output variance is due to variance in inputs.

The Dual Fluid Reactor (DFR) is a Generation IV concept that relies on a phased development pathway using a low-temperature microdemonstrator ($\mu$DEMO) and a high-temperature minidemonstrator (mDEMO). A rigorous methodology is required to scale experimental data between these facilities to ensure the reliable design of the final reactor. This paper establishes such a methodology grounded in Similarity Theory. The Cathare-2 system code was used to perform a parametric study on a simplified model of the demonstrators, which use lead–bismuth eutectic and pure liquid lead, respectively. This study focused on identifying the specific operating conditions required to match key “defining” dimensionless numbers—the Reynolds number (Re) for dynamic similarity and the Peclet number (Pe_h) for thermal similarity. The analysis successfully identified and presented the distinct operating ranges of fluid velocity and mass flow required to achieve either state. Results show that matching the Reynolds number allows for the dimensionless pressure drop to be scaled with a deviation below 0.2%, while matching the Peclet number allows for the dimensionless temperature profile to be scaled with a deviation under 2.5%. The central finding is that dynamic and thermal similarity cannot be achieved simultaneously due to the different working fluids and temperatures of the demonstrators. This forces a strategic choice in experimental design, where an experiment must be tailored to investigate either fluid dynamics or heat transfer. This work provides the foundational “rulebook” for designing these crucial experiments, ensuring that data from the DFR demonstrator program is both reliable and scalable.