An anisotropic numerical model for thermal hydraulic analyses: application to liquid metal flow in fuel assemblies

A CFD analysis has been carried out to study the thermal–hydraulic behavior of liquid metal coolant in a fuel assembly of triangular lattice. In order to obtain fast and accurate results, the isotropic two-equation RANS approach is often used in nuclear engineering applications. A different approach is provided by Non-Linear Eddy Viscosity Models (NLEVM), which try to take into account anisotropic effects by a nonlinear formulation of the Reynolds stress tensor. This approach is very promising, as it results in a very good numerical behavior and in a potentially better fluid flow description than classical isotropic models. An Anisotropic Shear Stress Transport (ASST) model, implemented into a commercial software, has been applied in previous studies, showing very trustful results for a large variety of flows and applications. In the paper, the ASST model has been used to perform an analysis of the fluid flow inside the fuel assembly of the ALFRED lead cooled fast reactor. Then, a comparison between the results of wall-resolved conjugated heat transfer computations and the results of a decoupled analysis using a suitable thermal wall-function previously implemented into the solver has been performed and presented

A Data-Driven Fuzzy Approach for Predicting the Remaining Useful Life in Dynamic Failure Scenarios of a Nuclear Power Plant

This paper presents a similarity-based approach for prognostics of the Remaining Useful Life (RUL) of a system, i.e. the lifetime remaining between the present and the instance when the system can no longer perform its function. Data from failure dynamic scenarios of the system are used to create a library of reference trajectory patterns to failure. Given a failure scenario developing in the system, the remaining time before failure is predicted by comparing by fuzzy similarity analysis its evolution data to the reference trajectory patterns and aggregating their times to failure in a weighted sum which accounts for their similarity to the developing pattern. The prediction on the failure time is dynamically updated as time goes by and measurements of signals representative of the system state are collected. The approach allows for the on-line estimation of the RUL. For illustration, a case study is considered regarding the estimation of RUL in failure scenarios of the Lead Bismuth Eutectic eXperimental Accelerator Driven System (LBE-XADS

Early warning system for the prevention and control of unauthorized accesses to air navigation services infrastructures

Early warning systems are fundamental instruments for the management of critical situations since they are able to signal in advance any anomaly with respect to ordinary situations. The purpose of this paper is to present an early warning system, based on artificial neural networks, for the prevention and control of unauthorized accesses to the air navigation services infrastructure in Italy

A sensitivity analysis for the adequacy assessment of a multi-state physics modeling approach for reliability analysis

In this work, a moment-independent Sensitivity Analysis (SA) based on Hellinger distance and Kullback-Leibler divergence is proposed to identify the component of a system most affecting its reliability (Diaconis et al., 1982; Gibbs et al., 2002; Di Maio et al., 2014). This result is used to adequately allocate modeling efforts on the most important component that, therefore, deserves a component-level Multi-State Physics Modeling (MSPM) to be integrated into a system-level model, to estimate the system failure probability

Towards a common object model and API for accelerator controls

An Object-Oriented Application Programming Interface (OO API) can provide applications with an abstract model of the components of an accelerator. The main question is how to encapsulate different control systems into one single abstract model. The abstract model of an 00 API can be described in a formal way via object models in order to clarify the semantic issues, to describe the important concepts (device, attributes, ...), and to decompose the objects up to the granularity where the model of some objects can be shared between labs. A C++ API (as well as C API) can be derived from the object-model. This paper presents a common object model which is derived from the object-model. This paper presents a common object model which is derived from both the current CERN-PS model and the current ERSF model. We describe the technical difficulties we encountered in migrating existing control systems into a shared but usable model. We also aim to increase the universality of the model by taking into account the CDEV library, as well as CORBA. A high-level description of the model will be presented with examples of the derived API

Robust multi-objective optimization of safety barriers performance parameters for NaTech scenarios risk assessment and management

Safety barriers are to be designed to bring the largest benefit in terms of accidental scenarios consequences mitigation at the most reasonable cost. In this paper, we formulate the problem of the identification of the optimal performance parameters of the barriers that can at the same time allow for the consequences mitigation of Natural Technological (NaTech) accidental scenarios at reasonable cost as a Multi-Objective Optimization (MOO) problem. The MOO is solved for a case study of literature, consisting in a chemical facility composed by three tanks filled with flammable substances and equipped with six safety barriers (active, passive and procedural), exposed to NaTech scenarios triggered by either severe floods or earthquakes. The performance of the barriers is evaluated by a phenomenological dynamic model that mimics the realistic response of the system. The uncertainty of the relevant parameters of the model (i.e., the response time of active and procedural barriers and the effectiveness of the barriers) is accounted for in the optimization, to provide robust solutions. Results for this case study suggest that the NaTech risk is optimally managed by improving the performances of four-out-of-six barriers (three active and one passive). Practical guidelines are provided to retrofit the safety barriers design

Adding PD-1/PD-L1 inhibitors to chemotherapy for the first-line treatment of extensive stage small cell lung cancer (Sclc): A meta-analysis of randomized trials

Survival outcomes in extensive-stage small cell lung cancer (ES SCLC) are dismal, with median overall survival (OS) less than 12 months. The combination of PD-1/PD-L1 immune checkpoint inhibitors (ICIs) with first-line platinum-etoposide chemotherapy has been recently evaluated in randomized clinical trials. We performed a systematic literature review through PubMed and conference proceedings. Randomized trials evaluating chemotherapy +/− PD-1/PD-L1 ICIs were included in the meta-analysis. Efficacy (OS), activity [progression-free survival (PFS) and objective response rate (ORR)] outcomes and toxicities were analyzed. For selected endpoints, we focused on patients’ subgroups (OS) and on landmark analyses (OS, PFS). Four randomized trials were identified; globally, 1553 patients were randomized to receive chemotherapy +/− PD-1/PD-L1 ICIs. Adding a PD-1/PD-L1 ICI to chemotherapy led to a significant benefit in OS [hazard ratio (HR) 0.76, 95% confidence interval (CI) 0.68–0.85, p < 0.00001), PFS [HR 0.75, 95% CI 0.68–0.84, p < 0.00001] and ORR [odds ratio 1.28, 95% CI 1.04–1.57, p = 0.02]. No unexpected toxicity emerged. At 12, 18, 24 months for OS, and at 12, 18 months for PFS, experimental arms retained significant improvement in event-free rates, with absolute gain of approximately 10% compared with standard treatment. Albeit the magnitude of the benefit is less impacting compared to other settings of immunotherapy, the addition of PD-1/PD-L1 ICIs to chemotherapy in ES SCLC provided significant improvements in survival outcomes with the known toxicity profile. Biomarkers predicting which patients are suitable to derive long-term benefits are eagerly awaited

A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply

The Operation & Maintenance (O&M) of Cyber-Physical Energy Systems (CPESs) is driven by reliable and safe production and supply, that need to account for flexibility to respond to the uncertainty in energy demand and also supply due to the stochasticity of Renewable Energy Sources (RESs); at the same time, accidents of severe consequences must be avoided for safety reasons. In this paper, we consider O&M strategies for CPES reliable and safe production and supply, and develop a Deep Reinforcement Learning (DRL) approach to search for the best strategy, considering the system components health conditions, their Remaining Useful Life (RUL), and possible accident scenarios. The approach integrates Proximal Policy Optimization (PPO) and Imitation Learning (IL) for training RL agent, with a CPES model that embeds the components RUL estimator and their failure process model. The novelty of the work lies in i) taking production plan into O&M decisions to implement maintenance and operate flexibly; ii) embedding the reliability model into CPES model to recognize safety related components and set proper maintenance RUL thresholds. An application, the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED), is provided. The optimal solution found by DRL is shown to outperform those provided by state-of-the-art O&M policies

Flexible operation and maintenance optimization of aging cyber-physical energy systems by deep reinforcement learning

Cyber-Physical Energy Systems (CPESs) integrate cyber and hardware components to ensure a reliable and safe physical power production and supply. Renewable Energy Sources (RESs) add uncertainty to energy demand that can be dealt with flexible operation (e.g., load-following) of CPES; at the same time, scenarios that could result in severe consequences due to both component stochastic failures and aging of the cyber system of CPES (commonly overlooked) must be accounted for Operation & Maintenance (O&M) planning. In this paper, we make use of Deep Reinforcement Learning (DRL) to search for the optimal O&M strategy that, not only considers the actual system hardware components health conditions and their Remaining Useful Life (RUL), but also the possible accident scenarios caused by the failures and the aging of the hardware and the cyber components, respectively. The novelty of the work lies in embedding the cyber aging model into the CPES model of production planning and failure process; this model is used to help the RL agent, trained with Proximal Policy Optimization (PPO) and Imitation Learning (IL), finding the proper rejuvenation timing for the cyber system accounting for the uncertainty of the cyber system aging process. An application is provided, with regards to the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED)

Seismic resilience assessment of Small Modular Reactors by a Three-loop Monte Carlo Simulation

We develop a three-loop Monte Carlo Simulation (MCS) framework for the seismic resilience assessment of Small Modular Reactors (SMRs), embedding Probabilistic Seismic Hazard Analysis (PSHA), seismic fragility evaluation and multiple SMR units accident sequence analysis. A set of metrics are computed to capture different aspects of SMR resilience to earthquakes, specifically the ability to withstand seismic disruption, mitigate consequences and restore normal operation. The MCS framework allows accounting for the aleatory and epistemic uncertainties of the PSHA and fragility parameters. An application is given with regards to an advanced Nuclear Power Plant (aNPP) consisting of four reactor units of NuScale SMR design. A comparison is made to a conventional NPP (cNPP), i.e., a typical large reactor of equivalent generation capacity. Both plants are fictitiously located on the Garigliano nuclear site (southern Italy). The results show that resilient features of SMRs overcome cNPPs in terms of post-accident scenario mitigation and restoration capabilities