QUANTITATIVE ASSESSMENT OF CORROSION-FATIGUE IN NUCLEAR POWER PLANT COMPONENTS USING SIMULATION AND EXPERIMENTAL DATA

Stress and strain considerations in the design of mechanical components in nuclear power plants

The Korea Atomic Energy Research Institute (KAERI) has been collaborating with Brookhaven National Laboratory since 2007 to develop a realistic seismic risk evaluation system which includes the consideration of aging of structures and components in nuclear power plants (NPPs). This collaboration program aims at providing technical support to a five-year KAERI research project, which includes three specific areas that are essential to seismic probabilistic risk assessment: (1) probabilistic seismic hazard analysis, (2) seismic fragility analysis including the effects of aging, and (3) a plant seismic risk analysis. The understanding and assessment of age-related degradations of structures, systems, and components and their impact on plant safety is the major goal of this KAERI-BNL collaboration. Four annual reports have been published before this report as a result of the collaboration research.

We present a structural digital twin system based on component-based parametric reduced order modeling using Reduced Basis Finite Element Analysis (RB-FEA) framework. This enables fast, detailed, and holistic modeling of large-scale structures, such as floating and fixed production and drilling platforms, and crucially, the digital twin's parametric nature and fast solve time enables the system to be connected seamlessly with inspection and sensor data. We demonstrate RB-FEA based structural digital twins, and capabilities for connecting them to sensor data and data science methods to increase predictive power and engineering insight. We show how the digital twin is a key enabler for a Digital Thread – a data-driven architecture that links together information generated from across the product lifecycle. Inspection data, such as thickness measurements, can be directly mapped into the structural digital twin. Sensor data, such as motion and strain can be transformed into loading data, e.g. to estimate sea-states and calculate stress and fatigue accumulation based on actual conditions, or to "tune" model parameters to match data-based measurements, such as mode shapes and frequencies. This enables continuous tracking of the asset and assessment of its current state, based on quantitative and detailed structural analysis, at any time. Ultimately the structural digital twin provides an interpretation layer for integration of monitoring and inspection data, leading to a fully automated digital thread. We present a framework, based on a cloud-based simulation platform and Python-based scripting, to automate the workflows listed above. Digital twin provides a single source of truth about asset condition and allows data driven communication and decision taking between multiple stakeholders – operators, contractors, regulators, etc. We will show examples of real-world applications of RB-FEA digital twin for FPSO and Jacket platforms and development work for digital thread with emphasis on data driven value creation along the entire life cycle of the asset. The Reduced Basis FEA based structural digital twin is a key enabler of data driven digital thread in addition to sensor frameworks and data science systems. RB-FEA provides fast, parametric, and physics-based analysis providing a transparent interpretation layer for data obtained from other systems. It provides a key element allowing knowledge management along asset lifecycle – from concept to decommissioning. High fidelity, physics based global simulations, such as RB-FEA digital twins, are a key enabler for effective digital transformation of industrial operations. They enable real-time insights into asset performance that are otherwise unattainable, providing tools for Asset Integrity Management, Risk Based Inspection, Automation and Optimization of Operations as well as enabling full digital thread enhancing Safety and CAPEX and OPEX.

• Examined various battery types, BMS architectures, and modelling techniques. • Explored deterministic, non-deterministic, and hybrid SOC estimation methods. • Discussed SOH estimation and RUL prediction using various methods. • Exploration of digital twin technologies and battery digital twins (BDTs) for enhancing BMS functionality. • Discussion of future research directions in battery state estimation. Accurate state estimation is fundamental for the safety, reliability, and performance of electric vehicle (EV) batteries. This review highlights the critical need for precise evaluation of key battery states such as state of charge (SOC), state of health (SOH), and remaining useful life (RUL). It surveys the existing modelling and estimation techniques used in lithium-ion battery (LIB) systems. It explores parameter identification methods and the challenges associated with real-time monitoring and predictive diagnostics in dynamic EV environments. The paper further investigates the transformative role of digital twin (DT) technologies in addressing these challenges. By integrating battery digital twins technologies(BDTs): Internet of Things (IoT), cloud computing, artificial intelligence (AI), and extended reality (XR), it offers a virtual replica of the battery system. That enables continuous monitoring, predictive maintenance, and performance optimization. The review delves into the architecture, functions, challenges and future perspectives of battery digital twins (BDTs). And it emphasizes their potential to enhance traditional battery management systems (BMS) through intelligent, adaptive control. Through comparative analysis and identification of research gaps, this review provides a roadmap for future advancements in state estimation and digital twin integration.

Nearly all of the operating U.S. commercial nuclear power plants (NPPs) are seeking, or have already received, license renewal from the Nuclear Regulatory Commission (NRC). As such, the nuclear industry is directing increased attention toward ensuring the safe and reliable operation of the various working components in NPPs, including the thousands of miles of power and signal cables that tie together complex plant systems. Because wholesale cable replacement is not an affordable option for aging NPPs, in-situ techniques for assessing the condition of installed cables are of critical importance. The goal of the research presented in this report is to refine existing techniques and to develop novel cable monitoring and testing methods for new and aging NPPs in the U.S. and worldwide.

Piezoelectric vibration sensors (PVSs) are widely applied to vibration detection in aerospace engines due to their small size, high sensitivity, and high-temperature resistance. The precise prediction of their remaining useful life (RUL) under high temperatures is crucial for their maintenance. Notably, digital twins (DTs) provide enormous data from both physical structures and virtual models, which have potential in RUL predictions. Therefore, this work establishes a DT framework containing six modules for sensitivity degradation detection and assessment on the foundation of a five-dimensional DT model. In line with the sensitivity degradation mechanism at high temperatures, a DT-based RUL prediction was performed. Specifically, the PVS sensitivity degradation was described by the Wiener–Arrhenius accelerated degradation model based on the acceleration factor constant principle. Next, an error correction method for the degradation model was proposed using real-time data. Moreover, parameter updates were conducted using a Bayesian method, based on which the RUL was predicted using the first hitting time. Extensive experiments on distinguishing PVS samples demonstrate that our model achieves satisfying performance, which significantly reduces the prediction error to 8 h. A case study was also conducted to provide high RUL prediction accuracy, which further validates the effectiveness of our model in practical use.

A nuclear power plant is composed of a number of primary components. Maintaining the integrity of these components is one of the most critical issues in nuclear industry. In order to maintain the integrity of these primary components, a complicated procedure is required including regular in-service inspection, failure assessment, fracture mechanics analysis, etc. Also, experts in different fields have to co-operate to resolve the<br/> integrity issues on the basis of inspection results. This integrity evaluation process usually takes long, and thus,<br/> is detrimental for the plant productivity. Therefore, an effective safety evaluation system is essential to manage the integrity issues on a nuclear power plant. In this paper, a web-based fatigue life evaluation system for primary components in nuclear power plant is proposed. This system provides engineering knowledge-information and concurrent and collaborative working environment through internet, and thus, is expected to raise the efficiency of integrity evaluation procednres on primary components of a nuclear power plant.<br/>

Acoustic sensing technique provides an efficient measure to detect and diagnose incipient failures occurred in plant components. The study reported here investigates the feasibility of adaptive super-directive microphone array technique for detection and localization of anomaly in nuclear power plants. The technique extracts anomaly information of objective components from the acoustic signals obtained at geometrically arranged multiple microphones. A specific signal processing was used to obtain a super-directive sensitivity of the microphone array and to adapt the sensitivity following the acoustic environmental change. Two adaptive sensing algorithms have been developed and implemented in a personal computer, and their abilities to extract the objective acoustic signal have been tested through numerical simulation. For appropriate number of the microphones, the satisfactory performance of the sound extraction has been obtained within reasonable computational load. The practical applicability of this technique to component monitoring in nuclear power plants has been confirmed through the present study.

The aging management system for the mechanical components of nuclear power plants (NPPs) must be established and used by the licensee in such a way that the quality of safety relevant components is guaranteed for the completely designed lifetime of the NPP. This demands an extensive plant life management with special emphases on the knowledge of the degradation in material properties. The basic safety concept in Germany observes this circumstance. Lifetime extension of the German NPPs is an aim of the current valid coalition agreement of the German government. Operational extension of interim storage facilities requires, in comparison to the aging management system for NPP, an aging management system adapted to the special circumstances of spent fuel storage casks. Extension of interim storage periods for spent fuel casks beyond the designed lifetime requires, in comparison to the components of an NPP, an increasing knowledge of material degradation with potential impact on cask integrity, e.g. le...

A Digital Twin (DT) for Electric Vehicles (EVs) is a virtual replica or digital model of a physical EV that captures its characteristics, performance, and real-time behavior. This study presents the development of a comprehensive DT model for EVs, designed to enable real-time monitoring, performance optimization, and predictive maintenance. The suggested framework features physical modeling and data-driven analytics to replicate the dynamic behavior of the EV powertrain, with particular emphasis on the battery, motor, and thermal subsystems. Using real driving cycle data (FTP-75), the DT continuously assesses key performance indicators (KPIs) such as battery state of charge (SoC), state of health (SoH), temperature, and motor torque to indicate real-world operating conditions. A degradation-aware battery model and simplified thermal dynamics are integrated to estimate remaining useful life (RUL) and enhance reliability. DT's predictive capability enables early fault detection and adaptive control strategies, minimizing downtime and advancing energy efficiency. Simulation results demonstrate the model's ability to accurately track SoC and thermal responses with minimal estimation error, validating its capability for integration in intelligent EV management systems. This innovative approach aims to improve the overall efficiency and reliability of EVs, thereby supporting a more sustainable transportation ecosystem.

Design of NPP (nuclear power plant) safety important systems (relating to a critical information infrastructure) is basing on observing the regulatory documents and proving reliable plant performance during all its life-cycle. An aspect of the increased requirements for reliable and safe NPP APCS (automated process control system) performance is the necessity of perfecting NPP operator information support systems. An approach is considering, under which a core of the operator information support system is a flexible modeling software package that can be used as a digital shadow or digital twin in the dependence of solved tasks. This approach applies to a wide range of tasks of upper-level systems of NPP APCS.

As of February 2014, the NRC has renewed the operating licenses for 73 nuclear units, allowing for up to 60 years of safe operation. In addition, the NRC has license renewal applications under review for 18 units and 9 additional units have announced plans to submit applications over the next few years [1]. This brings the total of renewed licenses and plans for renewal to 100% of the operating nuclear units in the U.S. By the end of 2014, there will be 38 nuclear plants that will have operated for more than 40 years and will be eligible to seek a subsequent license renewal (or almost 40% of the nuclear units expected to be operating at the end of 2014). In 2013, nuclear plant owners of 5 units shutdown operation or announced plans to shutdown by the end of 2014. However, most of the remaining operating plant owners are keeping the option open for long term operation beyond 60 years. NRC and the U.S. nuclear industry have made significant progress in preparing the way for subsequent license renewal applications. This paper presents the status of the U.S. license renewal process and issues being addressed for possible applications for subsequent renewals for up to 80 years of operation.

During design of Class I components in Nuclear Power Plants, cumulative usage factors (CUF) are conservatively calculated to estimate fatigue damage, and results must be below the limits of the applicable codes. Nevertheless, when these results are used to evaluate the possibility of using these components for an extended life, the results are frequently above code limits. Many Nuclear Power Plants have installed commercial fatigue monitoring systems at critical components in order to assess transient severity and cycle count for life extension fatigue calculations among other reasons. Since the commissioning of the system, unexpected operation modes and thermal stratification was discovered and evaluations needed to be done. Findings, interpretations and solving are presented in this paper.

The reactor coolant pump (RCP) is one of the most important components in nuclear power plants. It operated in high temperature, high-pressure, high speed and radiative environment, so a long-term security and reliable operations is required. Many internal flow analysis of RCPs was carried out, mainly foucs on steady and unsteady flow field at different operating points in RCP. The research about flow passage components, such as the blade of the RCP, is relatively few. When the RCPs operates in the nuclear power plant, the flow field lashed against the impeller of the RCP, results in a network of small cracks is found on the surface of impeller. For example, periodic vibration caused by a break in a guide vane leaded to cracking of two pieces blades of impeller in a large power plant in southwest of China, and this accident caused much economic loss. The computational method of stress due to the hydraulic reason is an important problem of the RCP. In this work, at first the CFD simulation including the case, guide vane, impeller, inlet and outlet at different operation points is studied, and the result of the pressure distribution on impeller blade is loaded on the impeller using fluid-structure interation (FSI) method. The result showed that the maximum von Mises stress appears on the trailing edge close to the impeller hub which has a large change in gradient of stress and which is prone to fatigue failure. The maximum stress on the impeller is mainly in proportion to the operating power. The maximum stress on the impeller have periodical characteristic, which is due to the number of blade of diffuser. All of these equip us with better understand of the fatigue and fracture of RCP, and it make sense to protect the fatigue damage and promote the stability of RCP.

Quantification of dislocations densities in zirconium hydride by X-ray line profile analysis