Currently, nuclear power is a major source of carbon-free electricity in the United States. However, keep operating the nuclear reactor fleet is a challenge due to ever increasing cost associated with operation and maintenance of an ageing nuclear reactor fleet. The advancement of artificial intelligence and machine learning (AI-ML) techniques can reduce some of the operation and maintenance costs. This is by adoption of a suitable Digital Twin (DT) framework. The DT model can be considered as a live model of an actual reactor component, which continually adapts to operational changes based on the collected offline and online data and information and can forecast the future state of a component at any given time. This will help with migrating from current reactive/periodic inspection-maintenance regime to a proactive/predictive inspection-maintenance regime. In turn, it will help increase availability and efficiency of a component, extend its useful life cycle and reduce its life cycle inspection-maintenance cost.A DT system has to be primarily based on AI-ML data-driven analytics framework that indirectly uses data from physics based virtual sensor measurements (from multi-physics digital model of the physical asset) and real sensor measurements. The sensor measurements are either continuous sensor measurements from an online monitoring (OLM) system and/or non-continuous sensor measurements from offline in-service inspections that employ Non-Destructive Testing (NDT) methods. However, the overdependence of a DT system on data (from virtual and real sensor measurements) necessitates access to a suitable database system in addition to the development of core analytics techniques and generation of underlying data. The database has to store hundreds to thousands of sensor measurements (either from virtual or real sensor measurements) with structured/un-structured data source, from continuous/non-continuous data source and to further work in a real time and agile environment. This type of multi-faceted requirements needs a suitable storage solution or database that must be efficient in providing big data storage, high scalability, high concurrent reading with low latency and high availability. &nbsp;Although traditional relational database management systems (RDBMS) such as Structured Query Language (SQL) based databases are heavily used in industry (e.g. banking and ecommerce industries), the trend is increasingly shifting toward “NoSQL database”. &nbsp;NoSQL database typically refers to any non-relational database with “NoSQL” represents “non-SQL” or “not only SQL”. The increasing trend toward using NoSQL database arises from the need to circumvent some of the issues associated with RDMS type database.In this paper, we will discuss some of the issues associated with SQL based database and the reason for choosing a NoSQL database for DT applications. We will show some of the results related to NoSQL based commercially available MongoDB database. The goal is to ultimately develop a DT framework for nuclear reactor heat-exchangers (e.g. steam generator used in a pressurized water reactor), with hundreds of subcomponents such as tubes that transfer the heat between primary loop (which directly produces the heat through nuclear chain reaction) and secondary loop (which transfers the primary-loop heat to steam that drives the turbine). We will demonstrate a strategy towards storing virtual sensor measurements (from finite element-based heat transfer model) at hundreds of locations (even within a single tube) and real sensor measurements (from continuous OLM sensors and from non-continuous NDT measurements).

A Review of SQL vs NoSQL Database for Nuclear Reactor Digital Twin Applications: With Example MongoDB Based NoSQL Database for Digital Twin Model of a Pressurized-Water-Reactor Steam-Generator

Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2021/volume/85543

ASME 2021 International Mechanical Engineering Congress and Exposition (IMECE2021)

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