Verification, Validation, and Calibration Through a Causal Lens
While typical validation and verification approaches focus on identifying the associations between data elements using statistical and machine learning methods, the novel methods in this paper focus instead on identifying causal relationships between data elements. Statistical and machine-learning-based approaches are strictly data-driven, meaning that they provide quantitative comparison measures between data sets without explicitly considering […]
Model-Based Approaches to Generate Knowledge from Data in a Plant Reliability Context
One challenge that nuclear power plant system engineers are facing is that the amount of equipment reliability (ER) data being continuously generated are extremely large. These data elements come in different forms: textual (e.g., condition reports) and numeric (e.g., generated by monitoring systems) and they provide system engineers with valuable insights and information regarding the […]
Development of Analysis Methods that Integrate Numeric and Textual Equipment Reliability Data
Within the Light Water Reactor Sustainability (LWRS) program, the Risk-Informed Systems Analysis (RISA) Pathway is performing collaborative research on the development and deployment of technologies designed to assist operating nuclear power plants (NPPs) to reduce operating costs improve plant reliability and availability. One of the RISA research areas is focusing on the development of methods […]
Parametric and Sensitivity Analysis of a Physics-based Steam Generator Model
For this study, we used Python and machine-learning tools to perform a comprehensive parametric and sensitivity analysis on a steam generator (SG) model. (The Python model was based on a previously completed MATLAB framework for the Holtec SMR-160 SG.) We investigated the influence of various input parameters (e.g., heat transfer coefficient [HTC], Nusselt number, and […]
Development of Plant Reload Optimization Framework Capabilities for Core Design and Fuel Performance Analysis
The United States (U.S.) nuclear industry faces a challenge in maintaining required levels of safety while ensuring economic competitiveness to stay in business. Safety remains a key parameter for all aspects of light water reactor (LWR) nuclear power plant (NPP) operations. Safety can become more economical by using a risk-informed ecosystem, such as the one […]
Bayesian Inverse Uncertainty Quantification of a MOOSE-based Melt Pool Model for Additive Manufacturing using Experimental Data
Additive Manufacturing (AM) technology is being increasingly adopted in a wide variety of application areas because of its ability to rapidly produce, prototype, and customize designs. AM techniques has significant opportunities in nuclear materials with accelerated fabrication process and reduced cost. High-fidelity modeling and simulation of AM processes is being developed at the Idaho National […]
A CAUSAL APPROACH TO INTEGRATE COMPONENT HEALTH DATA INTO SYSTEM RELIABILITY MODELS
Two of the challenges of current plant reliability approaches are the ability to integrate plant health data, and to support decision making. Condition based, diagnostic, and prognostic data are in fact not considered into plant reliability models to inform system engineers on the most critical components. Currently, the propagation of quantitative health data from the […]