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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Prioritization Risk Integration Simulation Model (PRISM) For Environmental Remediation and Waste Management - 12097

The PRISM (Prioritization Risk Integration Simulation Model), a computer model was developed to support the Department of Energy's Office of Environmental Management (DOE-EM) in its mission to clean up the environmental legacy from the Nation's nuclear weapons materials production complex. PRISM provides a comprehensive, fully integrated planning tool that can tie together DOE-EM's projects. It is designed to help DOE managers develop sound, risk-informed business practices and defend program decisions. It provides a better ability to understand and manage programmatic risks. The underlying concept for PRISM is that DOE-EM 'owns' a portfolio of environmental legacy obligations (ELOs), and that its mission is to transform the ELOs from their current conditions to acceptable conditions, in the most effective way possible. There are many types of ELOs - - contaminated soils and groundwater plumes, disused facilities awaiting D and D, and various types of wastes waiting for processing or disposal. For a given suite of planned activities, PRISM simulates the outcomes as they play out over time, allowing for all key identified uncertainties and risk factors. Each contaminated building, land area and waste stream is tracked from cradle to grave, and all of the linkages affecting different waste streams are captured. The progression of the activities is fully dynamic, reflecting DOE-EM's prioritization approaches, precedence requirements, available funding, and the consequences of risks and uncertainties. The top level of PRISM is the end-user interface that allows rapid evaluation of alternative scenarios and viewing the results in a variety of useful ways. PRISM is a fully probabilistic model, allowing the user to specify uncertainties in input data (such as the magnitude of an existing groundwater plume, or the total cost to complete a planned activity) as well as specific risk events that might occur. PRISM is based on the GoldSim software that is widely used for risk and performance assessment calculations. PRISM can be run in a deterministic mode, which quickly provides an estimate of the most likely results of a given plan. Alternatively, the model can be run probabilistically in a Monte Carlo mode, exploring the risks and uncertainties in the system and producing probability distributions for the different performance measures. The PRISM model demonstrates how EM can evaluate a portfolio of ELOs, and transform the ELOs from their current conditions to acceptable conditions, utilizing different strategic approaches. There are many types of ELOs - contaminated soils and groundwater plumes, disused facilities awaiting D and D, and various types of wastes waiting for processing or disposal. This scope of work for the PRISM process and the development of a dynamic simulation model are a logical extension of the GoldSim simulation software used by the OCRWM to assess the long-term performance for the Yucca Mountain Project and by NNSA to assess project risk at its sites. Systems integration modeling will promote better understanding of all project risks, technical and nontechnical, and more defensible decision-making for complex projects with significant uncertainties. It can provide effective visual communication and rapid adaptation during interactions with stakeholders (Administration, Congress, State, Local, and NGO). It will also allow rapid assessment of alternative management approaches. (authors)

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

Generic and robust method for automatic segmentation of PET images using an active contour model

Purpose: Although positron emission tomography (PET) images have shown potential to improve the accuracy of targeting in radiation therapy planning and assessment of response to treatment, the boundaries of tumors are not easily distinguishable from surrounding normal tissue owing to the low spatial resolution and inherent noisy characteristics of PET images. The objective of this study is to develop a generic and robust method for automatic delineation of tumor volumes using an active contour model and to evaluate its performance using phantom and clinical studies. Methods: MASAC, a method for automatic segmentation using an active contour model, incorporates the histogram fuzzy C-means clustering, and localized and textural information to constrain the active contour to detect boundaries in an accurate and robust manner. Moreover, the lattice Boltzmann method is used as an alternative approach for solving the level set equation to make it faster and suitable for parallel programming. Twenty simulated phantom studies and 16 clinical studies, including six cases of pharyngolaryngeal squamous cell carcinoma and ten cases of nonsmall cell lung cancer, were included to evaluate its performance. Besides, the proposed method was also compared with the contourlet-based active contour algorithm (CAC) and Schaefer’s thresholding method (ST). The relative volume error (RE), Dice similarity coefficient (DSC), and classification error (CE) metrics were used to analyze the results quantitatively. Results: For the simulated phantom studies (PSs), MASAC and CAC provide similar segmentations of the different lesions, while ST fails to achieve reliable results. For the clinical datasets (2 cases with connected high-uptake regions excluded) (CSs), CAC provides for the lowest mean RE (−8.38% ± 27.49%), while MASAC achieves the best mean DSC (0.71 ± 0.09) and mean CE (53.92% ± 12.65%), respectively. MASAC could reliably quantify different types of lesions assessed in this work with good accuracy, resulting in a mean RE of −13.35% ± 11.87% and −11.15% ± 23.66%, a mean DSC of 0.89 ± 0.05 and 0.71 ± 0.09, and a mean CE of 19.19% ± 7.89% and 53.92% ± 12.65%, for PSs and CSs, respectively. Conclusions: The authors’ results demonstrate that the developed novel PET segmentation algorithm is applicable to various types of lesions in the authors’ study and is capable of producing accurate and consistent target volume delineations, potentially resulting in reduced intraobserver and interobserver variabilities observed when using manual delineation and improved accuracy in treatment planning and outcome evaluation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Development of an integrated model for energy systems planning and carbon dioxide mitigation under uncertainty – Tradeoffs between two-level decision makers

Highlights: • A bi-level model is developed for energy systems planning and carbon dioxide mitigation. • Tradeoffs between two-level decision-makers’ demands are quantified under uncertainty. • The energy and environmental impact of CO{sub 2} mitigation policy is analyzed for Beijing. • Results provide a range of decision alternatives between system optimality and reliability. A bi-level fuzzy programming (BFLP) method was developed for energy systems planning (ESP) and carbon dioxide (CO{sub 2}) mitigation under uncertainty. BFLP could handle fuzzy information and leader-follower problem in decision-making processes. It could also address the tradeoffs among different decision makers in two decision-making levels through prioritizing the most important goal. Then, a BFLP-ESP model was formulated for planning energy system of Beijing, in which the upper-level objective is to minimize CO{sub 2} emission and the lower-level objective is to minimize the system cost. Results provided a range of decision alternatives that corresponded to a tradeoff between system optimality and reliability under uncertainty. Compared to the single-level model with a target to minimize system cost, the amounts of pollutant/CO{sub 2} emissions from BFLP-ESP were reduced since the study system would prefer more clean energies (i.e. natural gas, LPG and electricity) to replace coal fuel. Decision alternatives from BFLP were more beneficial for supporting Beijing to adjust its energy mix and enact its emission-abatement policy. Results also revealed that the low-carbon policy for power plants (e.g., shutting down all coal-fired power plants) could lead to a potentially increment of imported energy for Beijing, which would increase the risk of energy shortage. The findings could help decision makers analyze the interactions between different stakeholders in ESP and provide useful information for policy design under uncertainty.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Methods to Automate Assessing the Outage Risk and Technical Specification Compliance for Planned Work and Current Plant Conditions. Light Water Reactor Sustainability Program report

This research effort is a part of the Light Water Reactor Sustainability (LWRS) Program, which is a research and development (R&D) program sponsored by the Department of Energy. The LWRS Program is performed in close collaboration with industry R&D programs that provide the technical foundations for licensing and managing the long term, safe, and economical operation of current nuclear power plants (NPPs). The LWRS Program serves to help the United States (U.S.) nuclear industry adopt new technologies and engineering solutions that facilitate the continued safe operation of these NPPs and the extension of their current operating licenses. The Outage Risk Management Improvement Pilot Project seeks to improve the management of nuclear power plant outages. This is done through the development of tools to assist in evaluating pending activities against requirements to detect undesired interactions. Significant efforts are expended to manage the nuclear risk of an outage. The utilities conduct pre outage risk assessments, based on a very detailed review of the outage schedule, to identify where combinations of outage work and out of service equipment would result in degraded conditions with respect to nuclear safety or regulatory compliance. Probabilistic risk assessment studies are conducted to quantify incremental core damage frequency as a result of the outage activities and system unavailability. These studies are usually presented to site and fleet management, the site plant operational review committee, and the NPP’s independent Nuclear Safety Review Board for concurrence that the outage is planned safely and that reasonable measures have been taken to reduce the added risk of conducting the outage. During the outage, the plant configuration is monitored continuously to ensure that it conforms to the approved safety plan. Deviations must be assessed and approved by management committees and, in some cases, the plant operational review committee. In virtually all outage meetings and job briefings, the current nuclear safety status of the plant is communicated, including information on the specific equipment necessary to meet the requirements of the nuclear safety plan. In addition, the operations and outage organizations implement several layers of physical and administrative barriers to prevent unintended interaction with the systems and equipment credited for nuclear safety. In spite of all these efforts, nuclear safety challenges still occur too frequently during outages. While some of these are due to failures of equipment credited for safety, the majority occur because of human error. These typically involve some form of interaction between work activities and plant configuration changes. Some of them are very subtle and are extremely challenging to detect in advance. Nevertheless, they are not acceptable and represent clear opportunities to improve nuclear safety during outages. This particular report will provide a demonstration of the capabilities in the development of a predictive software platform for improving configuration control and accessing the plant risks associated with outage work protocols and illustrate how the software assists in strategies to minimize these interactions. In addition to maintaining nuclear safety, there are numerous administrative requirements that must be complied with during an outage. This report will discuss the use of logic modelling to monitor planned work against technical specification requirements for given plant conditions and to integrate these requirements with shutdown risk models, clearance order requirements and other plant condition requirements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Tank Waste LDR Organics Data Summary for Sample-and-Send (Rev.1A)

The presence of organic chemicals regulated under the Resource Conservation and Recovery Act (RCRA) Land Disposal Restrictions (LDR) adds complexity to treating and disposing of the low activity fraction of Hanford tank waste if a low temperature treatment method such as grouting is used (SRNL-STI-2020-00228). The complexity arises from the fact that the baseline vitrification method is considered by the Washington State Department of Ecology (Ecology) as providing adequate thermal treatment for organics; a status not automatically extended to a lowtemperature process, such as solidifying the waste in a cementitious waste form. In addition, the Environmental Protection Agency (EPA) LDR program is intended to ensure that wastes are properly treated prior to disposal. Proper treatment makes hazardous waste less harmful to groundwater by reducing the mobility and/or toxicity of the hazardous constituents in the waste. EPA guidance indicates that stabilization/solidification of waste for organics could be considered impermissible dilution under the LDR dilution prohibition. In addition, waste storage activities at Hanford have required transferring and blending waste within the tank system and these activities have potentially altered the concentrations of the hazardous constituents. The LDR dilution prohibition found in 40 Code of Federal Regulations (CFR) 268.3 states that “… no generator, transporter, handler, or owner or operator of a treatment, storage, or disposal facility shall in any way dilute a restricted waste or the residual from treatment of a restricted waste as a substitute for adequate treatment …”. Hence, if LAW is to be treated using low temperature stabilization (such as cementation), then it is important to demonstrate both how past storage activities have contributed to the removal (by vacuum evaporation), or destruction (by in situ decomposition) of the LDR organics and how future retrieval and waste feed preparation will contribute to their removal (by filtration and ion exchange). Demonstrating these processes helps validate that cementation without additional organic treatment does not necessarily represent impermissible dilution. To aid in implementing cementitious solidification of Low Activity Waste (LAW), WRPS has been developing a regulatory and processing LDR treatment variance strategy termed “Sampleand-Send” that relies, in part, on demonstrating that in situ decomposition reactions along with historic evaporation of tank waste has destroyed or removed most of the LDR organics possibly associated with Hanford Tank Waste (SRNL-STI-2020-00582, SRNL-STI-2021-00453, SRNL-STI-2022-00391). Under the Sample-and-Send concept, Hanford tank waste would be retrieved, processed through a Tank-Side Cesium Removal-like system, and staged as a candidate feed that would then be sampled to confirm the waste acceptance criteria is met for solidification in an LAW cementitious treatment facility. If it can be shown that LDR organics are at concentrations below the waste acceptance criteria (WAC) for cementitious stabilization and have been sufficiently removed (by historic evaporation or by filtration and ion exchange during Cs removal), destroyed (by historic in situ decomposition), or are not soluble in LAW above the WAC then additional organic treatment is not needed prior to creating a cementitious final waste form and the concept of Sample-and-Send would be proposed to establish a non-rulemaking site-specific treatment variance using the specified method of treatment “STABL” to remove sampling requirements of the waste form after treatment. Waste not meeting the WAC could either be routed to the Hanford Waste Treatment and Immobilization Plant for LAW vitrification, or further processed by evaporation or chemical oxidation before solidifying in a cementitious waste form. A key component in implementing the Sample-and-Send strategy is identifying which of the 207 LDR organic compounds associated with the RCRA Part A permit application waste codes for the Double Shell Tanks (DSTs) and Single Shell Tanks (SSTs) and any applicable Underlying Hazardous Constituents (UHCs) from 40 CFR 268.48 should be considered as potentially present and thus subject to regulation. In addition, it is also necessary to understand the solubility volatility, and reactivity of these compounds in LAW to identify which of the potentially present LDR organic compounds are not soluble above regulatory levels or are likely to have been removed by historic evaporation or destroyed by in situ decomposition reactions. If there are potentially present LDR organic compounds that have not been removed or destroyed and are soluble above regulatorily significant concentrations then a treatability variance may be needed for these species to eliminate any concerns pertaining to impermissible dilution. The spreadsheet accompanying this calculation report contains the data and logic computations needed to screen the list of 207 LDR organics associated with Hanford tank waste to identify those potentially present and to indicate which compounds may need to be included in a treatability variance.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Development of Analysis Methods that Integrate Numeric and Textual Equipment Reliability Data

Within the Light Water Reactor Sustainability (LWRS) program, the Risk-Informed Systems Analysis (RISA) Pathway is performing collaborative research on the development and deployment of technologies designed to assist operating nuclear power plants (NPPs) to reduce operating costs improve plant reliability and availability. One of the RISA research areas is focusing on the development of methods and tools designed to optimize plant operations (e.g., maintenance/replacement schedules, optimal maintenance postures for plant structures, systems, and components [SSCs]) in a manner that is more cost effective than current approaches and makes better use of available SSC health data. The Risk-Informed Asset Management (RIAM) project targets this research area by creating a direct bridge between component equipment reliability (ER) data and system engineer decision making regarding maintenance activity scheduling and component aging management. In this respect, one challenge that NPP system engineers are facing is that the amount of ER data being continuously generated is not only extremely large in size, but it comes in different forms: textual (e.g., condition or maintenance reports) and numeric (e.g., generated by monitoring systems). All these data elements provide them with valuable insights and information regarding: 1) the discovery of anomalous behaviors or degradation trends, 2) the identification of the possible causes behind such behaviors/trends, and 3) the prediction of their direct consequences. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers/databases), others are conceptual in nature: data elements come in different formats (e.g., numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). The activities performed by the RIAM project during FY23 directly tackles the need to simultaneously integrate the analysis of ER data in all its forms, numeric and textual. Note that such task has never been performed before due to the complexity of the systems under consideration but, most importantly, because of the technical challenges behind the harmonization of ER data formats and the lack of adequate computational methods to analyze them. Our approach borrows ideas and concepts from the medical field where integration of several data sources is vital to assist medical practitioners to perform correct diagnosis and indicate optimal treatments. In our view a NPP asset is equivalent to a patient in a medical context. The main difference is the complexity of a human body is a magnitude more complex when compared to typical assets commonly present in NPPs (e.g., centrifugal pumps, or motor operated valves). This simplifies our first requirement when analyzing heterogenous ER data formats: to put data into “context”. Context is here intended as the additional piece of information that is needed by ER data analysis tools to understand what these data elements are referring to, i.e., which king of knowledge they are generating. In our context, this knowledge can be translated into models that capture the form and functional architecture of assets/systems, their dependencies, and how they interact. These models actually emulate the knowledge that that NPP system engineers possess about assets and systems; this is their key of success when analyzing ER data, their challenge is ability to handle large amount of data. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional, i.e. cause-effect, relations. Then, ER data elements are processed by identifying first of all which elements of the developed MBSE elements they are referring to. For numeric ER data this task is fairly easy since it is possible to precisely pinpoint what MBSE elements the corresponding sensor are observing (e.g., bearing temperature of a centrifugal pump). Task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process as “knowledge extraction”. Once again, we borrow the experience in the medical field where methods to extract knowledge from textual data have been developed in the past decade. The missing element for us is the availability of a complete dictionary of NPP related concepts (in addition to the MBSE models presented earlier) that can put “text into context”. In FY23, such dictionary has been developed along with all the computational elements required for knowledge extraction. Lastly, once numeric and textual ER data elements have been processed and “understood”, then the last step is the discovery of possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if the

97 MATHEMATICS AND COMPUTING↗