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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Explainable Artificial Intelligence Technology for Predictive Maintenance

The domestic nuclear power plant fleet has relied on labor-intensive and time-consuming preventive maintenance programs, thus driving up operation and maintenance costs to achieve high-capacity factors. Artificial intelligence and machine learning can help simplify complex problems, such as diagnosing equipment degradation, to enable more effective decision-making. Benefits will be felt not only within existing analog and digital instrumentation and control, but also work processes, the integration of people with technology, and most importantly, the business case. Together, these hold promise to make nuclear power more efficient and reduce costs associated with operation and maintenance. While the artificial intelligence and machine learning technologies hold significant promise in the nuclear industry, there are challenges or barriers to their adoption. This report outlines the those different machine learning adoption barriers (categorized as historical, technical, economic, regulatory, and user) that the industry must overcome to realize the full benefits of artificial intelligence and machine learning capabilities for long-term economic sustainability. This report also provides solutions for some of these barriers by focusing on improving the explainability of machine learning to encourage trust from the end-user. Trust and explainability are essential to machine learning adoption. This report focuses on research-developed solutions to some of these barriers while analyzing a non-safety-related system, namely the circulating water system. This system frequently experiences waterbox fouling which our models preemptively diagnoses then explains to the operator how those conclusions were reached. This report presents and discusses the inherent trade-off between machine learning performance (in terms of accuracy) and explainability, where highly accurate machine learning methods (such as deep-learning) are the least explainable, and the most explainable methods (such as decision trees) are the least accurate. In addition, explainability of artificial intelligence techniques in terms of transparency and post-hoc metrics are discussed. This report outlines the importance of data novelty and value of new information in evaluating both the explainability and trustworthiness. Novelty detection helps to establish consistency or inconsistency of the new data with respect to the training data. On the other hand, value of information could be a part of the user-centric visualization recommendation system that request additional information to be collected, thereby strengthening the machine learning outcomes. During this project, a copyrighted user-centric visualization that aligns with a human-in-the-loop approach was developed. The user-centric visualization presents different levels of information and can be tailored as per user credentials to gain user confidence. One of the salient features of the user-centric visualization is it presents machine learning methods with explainability metrics. A simplified version of the user-centric visualization was presented to 32 users with varying levels of machine learning expertise. Feedback was solicited to test the hypothesis that the app contained sufficient explainability and that the users would trust the algorithm. Overall, the app was positively received, and the hypothesis was supported. This report discusses the trust-but-verify framework – a potential approach to build user trust artificial intelligence. The framework discusses trust from the human level to artificial intelligence level. The fundamental premise of the trust but verify framework is derived from an observation of nuclear safety culture (i.e., nuclear power plant personnel do not rely on a singular source of data to make a decision). This also ties back to the user-centric visualization that presents different levels of information to achieve both explainability and trustworthiness of artificial intelligence. Even so, the adoption of artificial intelligence and machine learning in the nuclear industry faces additional barriers, namely regulatory and stakeholder readiness. To overcome these challenges, new solutions must gain regulatory approval and cater to stakeholder needs. The Nuclear Regulatory Committee has a 5-year strategic plan which prepares them for reviewing artificial intelligence technologies in licensee submissions. Early and frequent engagement with the regulator is encouraged. Additionally, artificial intelligence solutions should incorporate human-in-the-loop considerations and offer explainability. Stakeholders must prepare by hiring or training staff to adapt to advancing technology in everyday plant tasks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Demonstration and Evaluation of Explainable and Trustworthy Predictive Technology for Condition-based Maintenance

The domestic nuclear power plant (NPP) fleet has historically relied on labor-intensive and time-consuming predictive maintenance (PdM) programs, thus driving up operation and maintenance (O&M) costs to achieve high-capacity factors. Artificial intelligence (AI) and machine-learning (ML) can help simplify complex problems such as diagnosing equipment degradation to enable more effective decision-making efforts. The benefits of AI will be felt through more efficient plant O&M, improved work processes, and better integration of people and technology. Together, these benefits hold the promise to make nuclear power more sustainable by reducing O&M costs while improving employee engagement. While AI and ML technologies hold significant promise for the nuclear industry, there are challenges or barriers to their adoption. Explainability and trustworthiness of AI are two salient challenges that need to be addressed for wider deployment of these technologies in NPPs. This research focuses specifically on addressing the explainability and trustworthiness of AI technologies to advance the human, technical, and organization (HTO) readiness levels in adopting a risk-informed PdM strategy at commercial NPPs. In addition, this approach can be adapted to enhance the acceptability of AI in other nuclear applications with a few application-specific modifications. The technical approach ensuring wider adoption of AI technologies was developed by Idaho National Laboratory (INL)—in collaboration with Public Service Enterprise Group (PSEG), Nuclear, LLC—by utilizing the circulating water system (CWS) at two PSEG-owned plant sites for demonstration. Focused user studies were performed in collaboration with subject matter experts (SMEs) from PSEG and other nuclear domains to enhance human and organization readiness by building trust in AI-informed technologies. VIsualization for PrEdictive maintenance Recommendation (VIPER)—a Battelle Energy Alliance, LLC, copyrighted software—was developed and expanded to provide a user-centric visualization by incorporating inputs from the collaborating utility, human factors engineering guidelines, and data analysts. The VIPER software enables users, who may be unfamiliar with ML in general, to be interactively engaged by asking technical questions about PdM, work orders, diagnosis results and their confidence levels, the kind of data being used, and the types of ML algorithms employed. This interactive engagement enhances explainability and builds trust. One of the enabling accomplishments was the integration of large language models (LLMs), both text-based and vision-based, in the VIPER software.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2016

Staffing Strategies for Maintenance of Critical Software Systems at the Jet Propulsion Laboratory

The Mission Design and Navigation Software Group at the Jet Propulsion Laboratory (JPL) maintains mission critical software systems. We have good empirical data and models for maintenance demand—when defects will occur, how many and how severe they will be, and how much effort is needed to address them. However, determining the level of staffing needed to address maintenance issues is an ongoing challenge and is often done ad-hoc. There are two common strategies are (1) reactive – add/remove staff as needed to respond to maintenance issues, and (2) capacitive – retain a given staff size to address issues as they occur, proactively address issues and prevent defects.

software maintenance↗
DOE OSTI2022

Multi-Kernel Support Vector Machine based Predictive Maintenance of Circulating Water Pumps in Nuclear Power Plants

Multi-Kernel Support Vector Machine (MK-SVM) is a machine learning classification algorithm that can assist in the development of predictive maintenance strategies for nuclear power plant systems. Predictive maintenance can alleviate maintenance costs and enhance reliability of plant systems. In this work, MK-SVM is utilized for determining the health of the circulating water system (CWS) in a nuclear power plant.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2013

Savannah River Site Eastern Transportation Hub: A Concept For a DOE Eastern Packaging, Staging and Maintenance Center - 13143

The Department of Energy (DOE) is working to de-inventory sites and consolidate hazardous materials for processing and disposal. The DOE administers a wide range of certified shipping packages for the transport of hazardous materials to include Special Nuclear Material (SNM), radioactive materials, sealed sources and radioactive wastes. A critical element to successful and safe transportation of these materials is the availability of certified shipping packages. There are over seven thousand certified packagings (i.e., Type B/Type AF) utilized within the DOE for current missions. The synergistic effects of consolidated maintenance, refurbishment, testing, certification, and costing of these services would allow for efficient management of the packagings inventory and to support anticipated future in-commerce shipping needs. The Savannah River Site (SRS) receives and ships radioactive materials (including SNM) and waste on a regular basis for critical missions such as consolidated storage, stabilization, purification, or disposition using H-Canyon and HB-Line. The Savannah River National Laboratory (SRNL) has the technical capability and equipment for all aspects of packaging management. SRS has the only active material processing facility in the DOE complex and is one of the sites of choice for nuclear material consolidation. SRS is a logical location to perform maintenance and periodic testing of the DOE fleet of certified packagings. This initiative envisions a DOE Eastern Packaging Staging and Maintenance Center (PSMC) at the SRS and a western hub at the Nevada National Security Site (NNSS), an active DOE Regional Disposal Site. The PSMC's would be the first place DOE would go to meet their radioactive packaging needs and the primary locations projects would go to disposition excess packaging for beneficial reuse. These two hubs would provide the centralized management of a packaging fleet rather than the current approach to design, procure, maintain and dispose of packagings on a project-by-project basis. This initiative provides significant savings in packaging costs and acceleration of project schedules. In addition to certified packaging, the PSMC would be well suited for select designs of 7A Type A packaging and Industrial Packaging. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Transition and Transfer of Remediated FUSRAP Sites from USACE to US DOE for Long-Term Surveillance and Maintenance - 16209

The US Department of Energy (DOE) expects to receive the transfer of 10 FUSRAP Sites from the US Army Corps of Engineers (USACE) over the next 10 years; however, the timing of the transfers is highly dependent upon federal funding of the ongoing remedial actions. When remediation for each site is complete and the 2-year operations and maintenance period has concluded, each site will transfer from USACE to DOE for long-term surveillance and maintenance (LTS and M). US DOE's Office of Legacy Management (LM) will accept program responsibility for these sites and conduct LTS and M activities required to maintain protectiveness, preserve site-specific knowledge, and retain the cleanup and stewardship records while keeping stakeholders informed. Since the last FUSRAP site transfer occurred in 2007, LM in coordination with USACE intends to establish a transition process to promote the seamless transfer of sites from the time when the first record of decision is signed to the completion of FUSRAP activities. The approach to transfer active FUSRAP sites to completed sites status has been historically outlined in foundational documents such as the 1999 Memorandum of Understanding and supporting letters of agreement between the two agencies. As more complex FUSRAP sites are completed, this transition process will provide a model between the two agencies to communicate future long-term care liabilities. Ultimately, the FUSRAP transition process is structured to acquire and preserve site knowledge and information necessary for protecting the environment and public health. As of 2015, LM has transitioned and accepted programmatic responsibility for over 90 sites. From LM's perspective, successful transition of any site includes understanding the long-term environmental liabilities. LM uses site transition framework requirements from past transitions to develop site-specific transition plans. Site-specific transition plans are developed by LM in coordination with USACE and executed during the 2-year operations and maintenance period. An integrated project team of subject matter experts is assembled to address the conditions of the transitioning site; acquire a site records collection; evaluate site operations and final site conditions and associated risks; identify and contact stakeholders; and document the basis for site LTS and M requirements. While the majority of the transition activities are completed by LM, close coordination between US DOE LM and USACE throughout this process is essential for an effective and seamless transfer to assure that there is no lapse in site protectiveness. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Enhancing PV Inverter Reliability Through Predictive Maintenance: Insights from Retrofitting of PV Inverters

Photovoltaic (PV) systems represent a cornerstone in the global shift toward sustainable energy generation, with inverters serving as the crucial link between solar panels and the grid. Despite their pivotal role, inverters are susceptible to failures, contributing significantly to maintenance events and operational disruptions in large-scale PV plants. This white paper investigates the importance and methodologies of predictive maintenance strategies that monitor the component-level pre-failure signatures on PV inverters. Through a comprehensive survey of literature and industry professionals, insights on preventive maintenance and retrofitting practices have been gathered. The expert elicitation helps shed light on common challenges and opportunities for improving PV system reliability, particularly inverter reliability. By addressing these challenges, the white paper aims to enhance the long-term viability and effectiveness of solar PV plants in the renewable energy landscape, contributing to the global transition toward environmentally friendly energy generation.

14 SOLAR ENERGY↗
DOE OSTI2025

Rooftop Unit (RTU) Maintenance Guide

The commercial buildings industry has long emphasized affordability, energy efficiency, and useful service life in rooftop unit (RTU) equipment. The trade-offs between these characteristics can impact decisions about the initial cost of equipment and ongoing operating costs. Often, the desire for lower initial costs leads to adoption of low-efficiency systems or neglect of proper maintenance in the interest of short-term cost savings. Maintenance of these systems is a critical step to ensure system efficiency and reliability, low operating costs, and a reasonable payback period. This guide is intended for facility managers who own or are interested in purchasing and installing RTUs and need to familiarize themselves with the maintenance requirements. This resource contains best practices, important considerations, and additional information related to commercial RTU equipment, but does not replace manufacturer-specific instructions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Using a Large Language Model for Accurate Technical Language Generation in the Predictive Maintenance of Circulating Water Systems in Nuclear Power Plants

Machine learning (ML) methods for predictive maintenance (PdM) are emerging as effective proactive strategies for diagnosing equipment degradation and enabling effective decision-making. However, explainability and trustworthiness of artificial intelligence are two salient challenges that need to be addressed for wider deployment of these technologies in nuclear power plants (NPPs). Large language models (LLMs) offer a unique approach to tackle these challenges by explaining PdM, work orders, diagnosis results, and ML algorithms to users, who may not be familiar with ML and PdM in general. Moreover, by dynamically retrieving relevant information from technical documents and evaluating factuality of LLM generation, the accuracy and relevance of LLM generations can be improved. This work demonstrates using LLMs to explain the causes and consequences of circulating water system failures based on multiyear NPP work orders. This work tests the capability of multimodal LLM approaches in explaining the differences in the circulating water system from both the Salem and Hope Creek NPPs using both text and image resources. This work also demonstrates the use of multimodal LLMs in describing the diagnosis tab of a predictive maintenance software named VIsualization for PrEdictive maintenance Recommendation (VIPER) to users.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2013

Structural health and prognostics management for the enhancement of offshore wind turbine operations and maintenance strategies

Offshore wind turbines are an attractive source for clean and renewable energy for reasons including their proximity to population centers and higher capacity factors. One obstacle to the more widespread installation of offshore wind turbines in the USA, however, is that recent projections of offshore operations and maintenance costs vary from two to five times the land-based costs. One way in which these costs could be reduced is through use of a structural health and prognostics management (SHPM) system as part of a condition-based maintenance paradigm with smart loads management. Our paper contributes to the development of such strategies by developing an initial roadmap for SHPM, with application to the blades. One of the key elements of the approach is a multiscale simulation approach developed to identify how the underlying physics of the system are affected by the presence of damage and how these changes manifest themselves in the operational response of a full turbine. A case study of a trailing edge disbond is analysed to demonstrate the multiscale sensitivity of damage approach and to show the potential life extension and increased energy capture that can be achieved using simple changes in the overall turbine control and loads management strategy. Finally, the integration of health monitoring information, economic considerations such as repair costs versus state of health, and a smart loads management methodology provides an initial roadmap for reducing operations and maintenance costs for offshore wind farms while increasing turbine availability and overall profit.

17 WIND ENERGY↗
DOE OSTI2017

The human CTC1/STN1/TEN1 complex regulates telomere maintenance in ALT cancer cells

Maintaining functional telomeres is important for long-term proliferation of cells. About 15% of cancer cells are telomerase-negative and activate the alternative-lengthening of telomeres (ALT) pathway to maintain their telomeres. Recent studies have shown that the human CTC1/STN1/TEN1 complex (CST) plays a multi-faceted role in telomere maintenance in telomerase-expressing cancer cells. However, the role of CST in telomere maintenance in ALT cells is unclear. Here, we report that human CST forms a functional complex localizing in the ALT-associated PML bodies (APBs) in ALT cells throughout the cell cycle. Suppression of CST induces telomere instabilities including telomere fragility and elevates telomeric DNA recombination, leading to telomere dysfunction. In addition, CST deficiency significantly diminishes the abundance of extrachromosomal circular telomere DNA known as C-circles and t-circles. Suppression of CST also results in multinucleation in ALT cells and impairs cell proliferation. Our findings imply that the CST complex plays an important role in regulating telomere maintenance in ALT cells. - Highlights: • CST localizes at telomeres and ALT-associated PML bodies in ALT cells throughout the cell cycle. • CST is important for promoting telomeric DNA replication in ALT cells. • CST deficiency decreases ECTR formation and increases T-SCE. • CST deficiency impairs ALT cell proliferation and results in multinucleation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Maintenance strategy, structural design, and site layout of the ST-E1 fusion power plant

An effective fusion reactor maintenance scheme enables safe operations and short downtimes. This in turn leads to high availability, which is critical to the commercial viability of a power-producing plant. In tokamak-based fusion power plants, the chosen maintenance approach has a significant impact on the spatial design of the tokamak, as well as the surrounding infrastructure, and therefore needs to be considered from the outset. Tokamak Energy has developed a pre-concept design of a fusion power plant, ST-E1. This work describes the major drivers and constraints that have been considered, presents the tokamak architecture and chosen maintenance regime, and discusses how this enables the plant’s two-phased approach to demonstrating commercial operations. It also shows the implications for the design of other systems areas, in particular the machine structural arrangement and bioshield and hot cell layout. The reactor core segmentation and removal scheme replaces entire toroidal segments radially through a large vacuum port, along a single axis only. The result is a change-tolerant machine and plant layout that can accommodate the evolving designs of the tokamak.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Risk-informed Predictive Analytics To Achieve Cost-effective Condition-based Monitoring And Maintenance Strategy

The research involves developing risk-informed predictive analytic capabilities to achieve condition-based monitoring and maintenance strategies to reduce overall maintenance costs. The research utilizes data (real-time data, periodic data, and institutional knowledge) related to a particular plant asset from a specific nuclear plant site to develop risk-informed predictive analytic algorithms. The developed algorithms and codes are used to optimize the maintenance strategy and estimate/forecast generation costs based on the state of health of the plant asset. Developed codes specifically include 1. Parameter estimation code based on Bayesian inference 2. Statistical data analysis code 3. Feature engineering code 4. Health classifier code 5. Diagnosis code 6. Prognosis code 7. Hazard code 8. Generation risk code 9. Economic code

Agarwal, Vivek↗
DOE OSTI2013

Y-12 Groundwater Protection Program CY 2009 Triennial Report Of The Monitoring Well Inspection And Maintenance Program, Y-12 National Security Complex, Oak Ridge, Tennessee

This document is the triennial report for the Well Inspection and Maintenance Program of the Y- 12 Groundwater Protection Program (GWPP), at the U.S. Department of Energy (DOE) Y-12 National Security Complex (Y-12). This report formally documents well inspection events conducted on active and inactive wells at Y-12 during calendar years (CY) 2007 through 2009; it documents well maintenance and plugging and abandonment activities completed since the last triennial inspection event (CY 2006); and provides summary tables of well inspection events, well maintenance events, and well plugging and abandonment events during the reference time period.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Y-12 Groundwater Protection Program CY2012 Triennial Report Of The Monitoring Well Inspection And Maintenance Program Y-12 National Security Complex, Oak Ridge, Tennessee

This document is the triennial report for the Well Inspection and Maintenance Program of the Y- 12 Groundwater Protection Program (GWPP), at the U.S. Department of Energy (DOE) Y-12 National Security Complex (Y-12). This report formally documents well inspections completed by the GWPP on active and inactive wells at Y-12 during calendar years (CY) 2010 through 2012. In addition, this report also documents well inspections performed under the Y-12 Water Resources Restoration Program, which is administered by URS|CH2M Oak Ridge (UCOR). This report documents well maintenance activities completed since the last triennial inspection event (CY 2009); and provides summary tables of well inspections and well maintenance activities during the reference time period.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Data Sharing Report for the Quantification of Removable Activity in Various Surveillance and Maintenance Facilities at the Oak Ridge National Laboratory Oak Ridge TN

The U.S. Department of Energy (DOE) Oak Ridge Office of Environmental Management (OR-EM) requested that Oak Ridge Associated Universities (ORAU), working under the Oak Ridge Institute for Science and Education (ORISE) contract, provide technical and independent waste management planning support using American Recovery and Reinvestment Act (ARRA) funds. Specifically, DOE OR-EM requested that ORAU plan and implement a sampling and analysis campaign targeting potential removable radiological contamination that may be transferrable to future personal protective equipment (PPE) and contamination control materials—collectively referred to as PPE throughout the remainder of this report—used in certain URS|CH2M Oak Ridge, LLC (UCOR) Surveillance and Maintenance (S&M) Project facilities at the Oak Ridge National Laboratory (ORNL). Routine surveys in Bldgs. 3001, 3005, 3010, 3028, 3029, 3038, 3042, 3517, 4507, and 7500 continuously generate PPE. The waste is comprised of Tyvek coveralls, gloves, booties, Herculite, and other materials used to prevent worker exposure or the spread of contamination during routine maintenance and monitoring activities. This report describes the effort to collect and quantify removable activity that may be used by the ORNL S&M Project team to develop radiation instrumentation “screening criteria.” Material potentially containing removable activity was collected on smears, including both masselin large-area wipes (LAWs) and standard paper smears, and analyzed for site-related constituents (SRCs) in an analytical laboratory. The screening criteria, if approved, may be used to expedite waste disposition of relatively clean PPE. The ultimate objectives of this effort were to: 1) determine whether screening criteria can be developed for these facilities, and 2) provide process knowledge information for future site planners. The screening criteria, if calculated, must be formally approved by Federal Facility Agreement parties prior to use for ORNL S&M Project PPE disposal at the Environmental Management Waste Management Facility (EMWMF). ORAU executed the approved sampling and analysis plan (SAP) (DOE 2013) while closely coordinating with ORNL S&M Project personnel and using guidelines outlined in the Waste Handling Plan for Surveillance and Maintenance Activities at the Oak Ridge National Laboratory, DOE/OR/01-2565&D2 (WHP) (DOE 2012). WHP guidelines were followed because the PPE waste targeted by this SAP is consistent with that addressed under the approved Waste Lot (WL) 108.1 profile for disposal at EMWMF—this PPE is a “future waste stream” as defined in the WHP. The SAP presents sampling strategy and methodology, sample selection guidelines, and analytical guidelines and requirements necessary for characterizing future ORNL S&M Project PPE waste. This report presents a review of the sample and analysis methods including data quality objectives (DQOs), required deviations from the original design, summary of field activities, radiation measurement data, analytical laboratory results, a brief presentation of results, and process knowledge summaries.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

Data sharing report characterization of the surveillance and maintenance project miscellaneous process inventory waste items Oak Ridge National Laboratory, Oak Ridge, TN

The U.S. Department of Energy (DOE) Oak Ridge Office of Environmental Management (EM-OR) requested Oak Ridge Associated Universities (ORAU), working under the Oak Ridge Institute for Science and Education (ORISE) contract, to provide technical and independent waste management planning support under the American Recovery and Reinvestment Act (ARRA). Specifically, DOE EM-OR requested ORAU to plan and implement a sampling and analysis campaign to target certain items associated with URS|CH2M Oak Ridge, LLC (UCOR) surveillance and maintenance (S&M) process inventory waste. Eight populations of historical and reoccurring S&M waste at the Oak Ridge National Laboratory (ORNL) have been identified in the Waste Handling Plan for Surveillance and Maintenance Activities at the Oak Ridge National Laboratory, DOE/OR/01-2565&D2 (WHP) (DOE 2012) for evaluation and processing for final disposal. This waste was generated during processing, surveillance, and maintenance activities associated with the facilities identified in the process knowledge (PK) provided in Appendix A. A list of items for sampling and analysis were generated from a subset of materials identified in the WHP populations (POPs) 4, 5, 6, 7, and 8, plus a small number of items not explicitly addressed by the WHP. Specifically, UCOR S&M project personnel identified 62 miscellaneous waste items that would require some level of evaluation to identify the appropriate pathway for disposal. These items are highly diverse, relative to origin; composition; physical description; contamination level; data requirements; and the presumed treatment, storage, and disposal facility (TSDF). Because of this diversity, ORAU developed a structured approach to address item-specific data requirements necessary for acceptance in a presumed TSDF that includes the Environmental Management Waste Management Facility (EMWMF)—using the approved Waste Lot (WL) 108.1 profile—the Y-12 Sanitary Landfill (SLF) if appropriate; EnergySolutions Clive; and the Nevada National Security Site (NNSS) (ORAU 2013b). Finally, the evaluation of these wastes was more suited to a judgmental sampling approach rather than a statistical design, meaning data were collected for each individual item, thereby providing information for item-byitem disposition decisions. ORAU prepared a sampling and analysis plan (SAP) that outlined data collection strategies, methodologies, and analytical guidelines and requirements necessary for characterizing targeted items (ORAU 2013b). The SAP described an approach to collect samples that allowed evaluation as to whether or not the waste would be eligible for disposal at the EMWMF. If the waste was determined not to be eligible for EMWMF disposal, then there would be adequate information collected that would allow the waste to be profiled for one of the alternate TSDFs listed above.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

Analyses in Support of Risk-Informed Natural Gas Vehicle Maintenance Facility Codes and Standards: Phase I

Safety standards development for maintenance facilities of liquid and compressed gas fueled large-scale vehicles is required to ensure proper facility design and operation envelopes. Standard development organizations are utilizing risk-informed concepts to develop natural gas vehicle (NGV) codes and standards so that maintenance facilities meet acceptable risk levels. The present report summarizes Phase I work for existing NGV repair facility code requirements and highlights inconsistencies that need quantitative analysis into their effectiveness. A Hazardous and Operability study was performed to identify key scenarios of interest. Finally, scenario analyses were performed using detailed simulations and modeling to estimate the overpressure hazards from HAZOP defined scenarios. The results from Phase I will be used to identify significant risk contributors at NGV maintenance facilities, and are expected to form the basis for follow-on quantitative risk analysis work to address specific code requirements and identify effective accident prevention and mitigation strategies.

33 ADVANCED PROPULSION SYSTEMS↗