Dynamic risk analysis of marine and offshore systems suffering microbial induced stochastic degradation

Hybrid ontology for safety, security, and dependability risk assessments and Security Threat Analysis (STA) method for industrial control systems

Modeling and analysis of internal corrosion induced failure of oil and gas pipelines

Deep learning algorithms provide plausible benefits for efficient prediction and analysis of nuclear reactor safety phenomena. However, research works that discuss the critical challenges with deep learning models from the reactor safety perspective are limited. This article presents the state-of-the-art in deep learning application in nuclear reactor safety analysis, and the inherent limitations in deep learning models. In addition, critical issues such as deep learning model explainability, sensitivity and uncertainty constraints, model reliability, and trustworthiness are discussed from the nuclear safety perspective, and robust solutions to the identified issues are also presented. As a major contribution, a deep feedforward neural network is developed as a surrogate model to predict turbulent eddy viscosity in Reynolds-averaged Navier–Stokes (RANS) simulation. Further, the deep feedforward neural network performance is compared with the conventional Spalart Allmaras closure model in the RANS turbulence closure simulation. In addition, the Shapely Additive Explanation (SHAP) and the local interpretable model-agnostic explanations (LIME) APIs are introduced to explain the deep feedforward neural network predictions. Finally, exciting research opportunities to optimize deep learning-based reactor safety analysis are presented. 

Deep learning for safety assessment of nuclear power reactors: Reliability, explainability, and research opportunities

Advances in nuclear power system design and fault-based condition monitoring towards safety of nuclear-powered ships

Dynamic risk assessment with bayesian network and clustering analysis

In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.

/pdf/prognostics-and-health-management-in-nuclear-power-plants-an-4jkdy10w3k.pdf

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

One of the lessons learned from the Fukushima Daiichi nuclear power plant (NPP) accident is strengthening the station blackout (SBO) mitigation capabilities by enhancing defense in depth for all existing and new NPPs. One of the possible remedies is diverse and flexible coping strategies (FLEX). The objective of this study is to address the benefits of FLEX in various accident scenarios in terms of both risk and cost. FLEX was originally devised against SBO accidents. In this research, we investigate the fundamental plant responses against accidents considering two fundamentally different cases: accidents that lead to high pressure on the primary side and accidents that lead to low pressure on the primary side. Several uncertainties are associated with the characteristics of the FLEX portable equipment. Specifically, the time for FLEX deployment may depend on several factors such as type of accident, point of injection, availability of safety systems, battery backup timings, and human actions. This study utilizes a dynamic risk assessment framework to analyze accident scenarios and suggests a novel importance measure, which is a cumulative distribution function–based importance metric that characterizes the influence of input distribution on complete output distribution. The importance of the existing and newly developed FLEX strategy based on risk significance is illustrated with examples. The suggested measure provides clear insight into how FLEX affects risk of the whole system and additional risk margins thanks to new safety systems. 

Sensitivity Analysis of Effectiveness of FLEX Strategies Using a CDF-Based Importance Measure Under Accident Conditions

Economically optimal and safe operation of integrated energy systems (IES) requires optimization at many different time scales. A real-time optimization (RTO) workflow will attempt to maximize revenue and minimize operational costs on a time scale of minutes to hours. Such a workflow requires the use of a digital twin (DT), which is a virtual representation of a physical system. The DT is updated using real-time data from the physical system, and serves as a model in an optimization framework. The optimization results are then sent back to the physical system to complete the loop. This report details the progress made in developing building blocks for a DT/RTO framework. The Risk Analysis Virtual Environment (RAVEN) platform within the Framework for Optimization of Resources and Economics (FORCE) tool suite can perform many of the tasks required for building a DT and performing RTO. The first item of this report details RAVEN enhancements that enable RAVEN workflows to be run in various environments. Data communication between the physical system and its DT is essential for successful RTO. This includes preprocessing real-time data, loading data into a data warehouse, and querying the stored data. The second section of this report describes the progress made in implementing an adapter in Python in order for Deep Lynx to handle the data communication. Typical dispatch optimization frameworks are built on linear programming (LP). The prototype RTO workflow developed in this report uses an LP problem as a part of a receding-horizon- or economic model predictive control (EMPC) based optimization. The third section of this report details the framework of an RTO workflow in which the system consists of a simple electrical storage device. A DT can be built from a reduced-order model (ROM). Integrating a ROM into a typical LP optimization framework has been challenging because most optimization packages require the user to write algebraic expressions for the system model. The final section of this report shows how an externally built RAVEN ROM can be integrated in an RTO framework by using the Python package Pyomo. This demonstrates the RTO workflow capability from a software-only perspective and is an important step in demonstrating the capability to implement an RTO workflow for a physical system. 

Real-Time Optimization Workflow Status Update

Decarbonization efforts in the United States has resulted in the increasing interest in flexible operation of nuclear power plants or thermal storage-based hybrid energy systems integrated with the power grid. Consequently, the need for rapid and effective modeling and simulation of such integrated systems is becoming critical. This requires modeling a approach that offers capabilities for multi-domain simulation that allows to model coupled phenomena accurately. This work focused on developing a proof of concept of such multi-domain models by designing a nominal model of a nuclear power plant balance of plant (BOP) system using the Modelica libraries. To model the thermofluidic domain, three libraries are evaluated, namely, ORNL’s TRANSFORM, Modelon’s ThermalPower, and Casella’s ThermoPower. Next, the thermofluidic model is coupled with an electrical grid model built using the OpenIPSL. To understand the tradeoffs of each library, the response of each different model to power transients in the nuclear power plant was analyzed. Simulation results and modeling methods were compared. The BOP model in TRANSFORM was found to be stiff and may require more detailed component models, e.g. condenser and feed water heater, to model the BOP more realistically and rapidly.

Comparing Thermal Library Modeling Suites for Integrated Modeling of Nuclear Power Plant and Power Grids

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