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Over the past decade, the use of artificial intelligence techniques in the field of health-monitoring has gained significant interest, especially for structures such as building and bridges. This project proposes development of an Artificial Intelligence (AI) framework for the data-driven condition monitoring of nuclear structural systems and equipment, where the vibration response is governed by multiple localized modes unlike that in buildings and bridges. Hence, techniques such as signal processing and pattern recognition will be employed to extract degradation-sensitive features. Degraded locations can potentially exhibit damage such as localized yielding, cyclic fatigue, or initiation of cracking. Moreover, such locations can at times go undetected by current inspection techniques. Therefore, this research proposes a framework, which utilizes sensor response to generate an AI database for predicting degraded locations and severity in nuclear structural systems and equipment. Degradation severity will be classified as minor, moderate, and severe, along with incorporation of uncertainty.

The Center for Nuclear Energy Facilities & Structures, has been established and is administered by North Carolina State University to conduct research in the areas of structural engineering, mechanics, risk assessment, hazard mitigation, and construction engineering and to promote research, education, and training in the Research Area. The CENTER has developed core research, non-core research, and technology transfer activities.

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The objective of the proposed research is to use advanced modeling and simulation tools to determine if the building-equipment interaction help in reduction of response of secondary systems when subjected to high frequency motions. The motivation for conducting the proposed research is driven by the anticipated savings in the enormous effort and cost that is currently faced by the nuclear industry in attempting to qualify equipment, piping, and structures for the updated seismic hazard containing high frequency motions.

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The proposed project builds upon the previous work of CNEFS in which CNEFS helped develop capabilities for Fault Tree Analysis in the Idaho National Lab (INL)������������������s MASTADON toolkit. The proposed project focuses on the following specific tasks ��������������� Extend the fault-tree analysis and quantification to include event-trees ��������������� Convert C++ code to MOOSE objects and implement in MASTODON. ��������������� Create examples for PRA in MASTODON using the new fault-tree and event-tree quantification implementations ��������������� Benchmark examples with Saphire ��������������� Document these examples on the MASTODON website

In recent years, there is an increasing interest in the nuclear industry to focus on identifying tools, methods and opportunities to optimize construction activities and reduce costs of operation and maintenace. One of the promising tools is the use of digital twins. A digital twin is a continuously updated representation of an actual structure as it degrades. It uses the observations from maintenance and sensor data as input to continuously update the simulation and data-driven models while considering the effect of uncertainties. There is a need for more demonstrations of digital twins use cases to open the door for more nuclear industry applications. Conduct an exploratory project to demonstrate the various steps needed in the development of a digital twin on a piping system and to develop a computational framework for assessing degradation mechanisms. To achieve the high level objective, the contractor will build a piping system consists of individual pieces of pipes, elbows and flanges. The details of the piping system will be discussed with the EPRI project manager (PM).

A DT-DAP (Digital Twin Development and Assessment Process) methodology has been formulated at NCSU in the ARPA-E sponsored project. DT-DAP can be very effective in guiding the design, training, testing, and application of DTs to improve effectiveness, accuracy and acceptance of system design and safety analysis.

The main objective of the proposed work is to develop, demonstrate, and evaluate a probabilistic risk assessment (PRA) software platform needed to address the major challenges of the current legacy PRA tools, such as better quantification speed, integration of multi-hazard models into traditional PRAs, and model modification simplification and documentation automation. To achieve the main objective, we will first perform benchmarking and profiling of current PRA tools, such as SCRAM and SAPHIRE, to investigate the current bottlenecks in the quantification speed and memory requirements. Secondly, we will design, implement, and benchmark a PRA software platform based on a web-based stack using the latest technologies available to overcome the mentioned challenges. Finally, we will evaluate the performance gains of this framework by modeling and quantifying large PRA models that would have been too expensive to run using the legacy PRA tools.