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Development and operating experience of a short-period superconducting undulator at the Advanced Photon Source
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Continuous and fast calibration of the CMS experiment: design of the automated workflows and operational experience
The exploitation of the full physics potential of the LHC experiments requires fast and efficient processing of the largest possible dataset with the most refined understanding of the detector conditions. To face this challenge, the CMS collaboration has setup an infrastructure for the continuous unattended computation of the alignment and calibration constants, allowing for a refined knowledge of the most time-critical parameters already a few hours after the data have been saved to disk. This is the prompt calibration framework which, since the beginning of the LHC Run-I, enables the analysis and the High Level Trigger of the experiment to consume the most up-to-date conditions optimizing the performance of the physics objects. In the Run-II this setup has been further expanded to include even more complex calibration algorithms requiring higher statistics to reach the needed precision. This imposed the introduction of a new paradigm in the creation of the calibration datasets for unattended workflows and opened the door to a further step in performance. The paper reviews the design of these automated calibration workflows, the operational experience in the Run-II and the monitoring infrastructure developed to ensure the reliability of the service.
Natural Language Processing-Enhanced Nuclear Industry Operating Experience Data Analysis: Aggregation and Interpretation of Multi-Report Analysis Results
Industry-wide operating experience is a critical source of raw data for reliability and risk model parameter estimations for nuclear power plants. A large portion of operating experience data are failure events stored as reports that contain unstructured data, such as narratives. In current practice, a failure report is usually reviewed and manually coded by analysts. The coding is based on extracting several event characteristics such as system name, component type, sub-part type, failure mode, and failure cause. Event narratives are mostly used to help understand events and extract their characteristics. In this line of research, we aim to maximize the usage of event narratives by leveraging natural language processing (NLP) methods to automatically convert an event narrative to a causal graph. This research has promise to improve physical understanding of failure initiation and propagation and to facilitate use of non-failure data (e.g., near-misses and degradations) to complement the limited data pool of failures. In our previous work, we developed an NLP tool and applied it to analyze a number of licensee event reports submitted by U.S. nuclear power plants to the Nuclear Regulatory Commission. In this paper, we will report our recent research progress in aggregating the results of multiple reports, developing network model(s), and drawing statistical insights.
NRC Reactor Operating Experience Analysis and Trend Summary: 2022 Update
This report presents a summary of the Nuclear Regulatory Commission (NRC) reactor operating experience analyses with data through 2022 as well as the reliability and frequency trends identified in the 2022 update reports for component performance study, loss of offsite power analysis, initiating events analysis, and system study provided on the NRC Reactor Operating Experience Results and Databases website (https://nrcoe.inl.gov/).
NRC Reactor Operating Experience Analysis and Trend Summary: 2024 Update
This report presents a summary of the Nuclear Regulatory Commission’s (NRC’s) reactor operating experience analyses with data through 2024 as well as the reliability and frequency trends identified in the 2024 update reports for the component performance studies, loss-of-offsite power analysis, initiating events analysis, and system studies provided on the NRC Reactor Operating Experience Results and Databases website (https://nrcoe.inl.gov/).
Natural Language Processing-Enhanced Nuclear Industry Operating Experience Data Analysis to Support Risk Model Parameter Estimations
This set of slides has been prepared for a talk at the INL AI/ML Symposium held on September 8, 2022. Presentation outline: Background - Nuclear power plant operating experience data sources • Research focus and motivation - Analyzing free-text operating experience data: present and future • Research method - Input - Methodological steps - Output • Conclusions and next steps
Operating Experience Data Analysis for Digital Instrumentation and Control System Reliability and Risk Assessment in Nuclear Power Plants
The implementation of advanced digital instrumentation and control (DI&C) systems in U.S. nuclear power plants (NPPs) can bring significant advancements in reliability, monitoring, and control capabilities. However, these systems also introduce new challenges, particularly in assessing risks such as common-cause failures (CCFs) and establishing robust reliability estimates for DI&C components. Addressing these challenges is critical for ensuring the safe and efficient operation of NPPs. Recently, Idaho National Laboratory was tasked by the U.S. Nuclear Regulatory Commission (NRC) to conduct a DI&C reliability study using operating experience data from the nuclear industry. The two operating experience data sources for the study are the Institute of Nuclear Power Operations’ Industry Reporting and Information System (IRIS) and the NRC’s Licensee Event Report database which is hosted at Idaho National Laboratory at https://lersearch.inl.gov/LERSearchCriteria.aspx. This report provides a comprehensive examination of DI&C systems, including their architecture, operational advantages, and associated challenges. It reviews existing industry DI&C studies and failure mode taxonomies, along with reliability data from various industries. Through a detailed analysis of these databases, the study provides insights into DI&C system performance. Considerations should be given to incorporate DI&C failure data into the NRC's Integrated Data Collection and Coding System and updating the Reliability and Availability Data System to support ongoing DI&C reliability studies. Recommendations are also provided for modeling DI&C reliability and CCF in probabilistic risk assessment, thereby supporting risk-informed decision-making and enhancing the reliability and safety of NPPs.
Development and operational experience of the web based application to collect, manage, and release the alignment and calibration configurations for data processing at CMS
The alignment and calibration workflows at the Compact Muon Solenoid (CMS) experiment are fundamental to provide a high quality physics data and to maintain the design performance of the experiment. To facilitate the operational efforts required by the experiment, the alignment and calibration team has developed and deployed a set of web-based applications to search, navigate and prepare a consistent set of calibrations to be consumed in reconstruction of data for physics, accessible through the Condition DB Browser. The Condition DB Browser hosts also various data management tools, including a visualization tool that allows to easily inspect alignment an calibration contents, an user-defined notification agent for delivering updates on modification to the database, a logging service for the user and the automatic online-to-offline condition uploads. In this paper we report on the operational experience of this web application from 2017 data taking, with focus on new features and tools incorporated during this period.
U.S. Nuclear Operating Experience Program for Probabilistic Risk Assessment Parameter Estimations
This presentation describes the overall process of the U.S. Nuclear Regulatory Commission (NRC)operating experience (OpE) program to estimate probabilistic risk assessment parameters, the estimations of initiating event frequencies, the component unreliability, and the quality assurance and quality control activities for the OpE program. This presentation can be used for training, workshop, or knowledge transfer purpose.
Digital Instrumentation & Controls Study with Operating Experience Data
The slides present the plan and working progress on the digital instrumentation and control (DI&C) study with nuclear industry operating experience data. They were originally prepared as a working document and presented to the NRC, but then was asked by the NRC to present to an EPRI/NRC meeting on DI&C. The slides will also be distributed to the meeting participants.
OPERATING EXPERIENCE AND ITS RELATIONSHIP TO EMBEDDED DIGITAL DEVICES IN SAFETY SYSTEMS
Interest in embedded digital devices (EDDs) for monitoring and providing control of components is increasing and the added functionality digital brings such as diagnostics, displays, control functions is welcomed in both the nuclear and other industries. As a result, pure analog-based components continue to disappear from the industrial I&C marketplace. Equipment with EDDs, also known by related terms such as smart devices, intelligent devices, and digital devices of limited functionality, can include sensors, breakers, priority logic modules, time delay relays, pumps, valve actuators, motor control centers, and uninterruptible power supplies. Not only is equipment consisting of analog and older digital technology being replaced with commercial grade products containing EDDs, but EDDs can also be retrofitted to work with existing components. EDDs may be installed physically within a component, such as in a relay. There are also external devices that some would not consider an EDD, but that operate in almost all technical ways identically to one internal to a component such as in a valve controller. In fact, the digital portions may be located hundreds of feet from the actual component.Due to the potential of being used in an identical manner and without alteration from their application in other industries, operating experience (OpE) for EDDs may be much more applicable than in the case of other more system level devices. Clear separation and independence of functionality provided with EDDs may also allow greater applicability of existing OpE from industry for changes that can be shown not to affect its safety function. However, OpE also demonstrates issues with different conditions between other industries and nuclear power plants, such as issues with undeclared changes or undeclared digital devices and the new failure modes they may introduce, issues with the reporting of OpE in industry, and a lack of transparency into OpE for EDDs.Understanding and addressing these issues will be key in the future use of EDDs in nuclear power plants, but failure rate data is difficult to obtain for EDDs, and lessons learned from the use of EDDs are difficult to collect and identify.Key words: embedded digital device, smart device, operating experience, failure modes
Applying AI/ML Techniques to U.S. Nuclear Operating Experience Program
Idaho National Laboratory (INL) has provided technical assistance to the U.S. Nuclear Regulatory Commission (NRC) in reliability and risk analysis including the operating experience (OpE) program since the 1980s. The U.S. nuclear OpE program provides input parameters to the NRC Standardized Plant Analysis Risk models and the industry probabilistic risk assessment (PRA) models. While earlier PRA focuses were on at-power, internal event analysis, the risks from external hazards and during low power shutdown (LPSD) operation could be significant and the needs to develop LPSD PRA and external hazards PRA are on the rise. One issue in developing LPSD PRA is the reasonable estimation of shutdown initiative event (SDIE) frequencies. INL has developed and is maintaining an SDIE database for the NRC. However, this database is based on the reviewing of Licensee Event Reports (LERs), which is believed to be only a subset of “actual” shutdown initiating events occurred in the industry. This paper investigates a new approach to identify and characterize shutdown initiating events from the Institute of Nuclear Power Operations (INPO) industry database using machine learning techniques. The main process in this approach is to find out the relationship between key words in event descriptions and the SDIE categories as in the NRC SDIE database. The relationship can then be applied to the INPO database and search for SDIEs.
Operational Experience from LCLS-II Cryomodule Testing
This paper describes the initial operational experience gained from testing Linac Coherent Light Source II (LCLS-II) cryomodules at Fermilab’s Cryomodule Test Facility (CMTF). Strategies for a controlled slow cooldown to 100 K and a fast cooldown past the niobium superconducting transition temperature of 9.2 K will be described. The test stand for the cryomodules at CMTF is sloped to match gradient in the LCLS-II tunnel at Stanford Linear Accelerator (SLAC) laboratory, which adds an additional challenge to stable liquid level control. Control valve regulation, Superconducting Radio-Frequency (SRF) power compensation, and other methods of stabilizing liquid level and pressure in the cryomodule 2.0 K SRF cavity circuit will be discussed. Several different pumping configurations using cold compressors and warm vacuum pumps have been used on the cryomodule 2.0 K return line and the associated results will be described.
Operating Experience and Lessons Learned in the Use of Soft-Sided Packaging for Transportation and Disposal of Low Activity Radioactive Waste
This paper describes the operating experience and lessons learned at U.S. Department of Energy (DOE) sites as a result of an evaluation of potential trailer contamination and soft-sided packaging integrity issues related to the disposal of low-level and mixed low-level (LLW/MLLW) radioactive waste shipments. Nearly 4.3 million cubic meters of LLW/MLLW will have been generated and disposed of during fiscal year (FY) 2010 to FY 2015—either at commercial disposal sites or disposal sites owned by DOE. The LLW/MLLW is packaged in several different types of regulatory compliant packaging and transported via highway or rail to disposal sites safely and efficiently in accordance with federal, state, and local regulations and DOE orders. In 1999, DOE supported the development of LLW containers that are more volumetrically efficient, more cost effective, and easier to use as compared to metal or wooden containers that existed at that time. The DOE Idaho National Engineering and Environmental Laboratory (INEEL), working in conjunction with the plastic industry, tested several types of soft-sided waste packaging systems that meet U.S. Department of Transportation requirements for transport of low specific activity and surface contaminated objects. Since then, soft-sided packaging of various capacities have been used successfully by the decontamination and decommissioning (D&D) projects to package, transport, and dispose D&D wastes throughout the DOE complex. The joint team of experts assembled by the Energy Facility Contractors Group from DOE waste generating sites, DOE and commercial waste disposal facilities, and soft-sided packaging suppliers conducted the review of soft-sided packaging operations and transportation of these packages to the disposal sites. As a result of this evaluation, the team developed several recommendations and best practices to prevent or minimize the recurrences of equipment contamination issues and proper use of soft-sided packaging for transport and disposal of waste.
Applying AI/ML Techniques to Nuclear Operating Experience Program and Low-Power Shutdown Initiating Event Study
This is a presentation to the INL AI/ML Activities Workshop in May 2023 discussing the application of AI/ML in nuclear operating experience program and low power shutdown initiating event study.
NRC Reactor Operating Experience Analysis and Trend Summary: 2020 Update
This report presents a summary of the reliability and frequency trends identified in the 2020 update reports for component performance study, loss of offsite power analysis, initiating events analysis, and system study provided on the Nuclear Regulatory Commission (NRC) Reactor Operating Experience Results and Databases web site (https://nrcoe.inl.gov/).