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This poster describes the findings in a related paper and information gleaned from the project. The aim of the project is to develop a methodology for more objective geothermal decision making, including more solid go/no-go decisions at specific points in the process, and to reduce subjectivity and increase reproducibility in the future.
The Solar Technical Assistance Team (STAT) is a network of solar technology and implementation experts who provide timely, unbiased expertise to assist policymakers and regulators in making informed decisions about solar programs and policies. Government officials can submit requests directly to the STAT for technical assistance. STAT then partners with experts in solar policy, regulation, finance, technology, and other areas to deliver accurate, up-to-date information to state and local decision makers. The STAT responds to requests on a wide range of issues -- including, but not limited to, feed-in tariffs, renewable portfolio standards, rate design, program design, workforce and economic impacts of solar on jurisdictions, and project financing.
Nowadays, it is widely accepted that scenario-based probabilistic risk assessment (PRA) is needed to support key decisions about new space flight systems. However, taking a risk estimate at face value and comparing it with a management “threshold” to determine whether a system is safe enough is too simplistic. This paper discusses considerations that need to accompany PRA results in particular decision contexts. The present point is not to criticize PRA itself; others have eloquently stressed the point that, for complex, high-stakes systems, whatever PRA’s imperfections, doing PRA is better than not doing it. But while all analysis results are hostage to underlying assumptions, this is especially true for PRA, and over the years, PRA results have occasionally been overinterpreted or otherwise misused. In short, there are more enlightened and less enlightened ways to understand and use PRA results. This paper discusses several issues relating to these concerns, and suggests ways of dealing with them.
A soft decision analyzer system is operable to interconnect soft decision communication equipment and analyze the operation thereof to detect symbol wise alignment between a test data stream and a reference data stream in a variety of operating conditions.
According to some embodiments, a plurality of monitoring nodes may each generate a series of current monitoring node values over time that represent a current operation of the industrial asset. A node classifier computer, coupled to the plurality of monitoring nodes, may receive the series of current monitoring node values and generate a set of current feature vectors. The node classifier computer may also access at least one multi-class classifier model having at least one decision boundary. The at least one multi-class classifier model may be executed and the system may transmit a classification result based on the set of current feature vectors and the at least one decision boundary. The classification result may indicate, for example, whether a monitoring node status is normal, attacked, or faulty.
While there are many advantages to electric public transit vehicles, they also pose new challenges for fleet operators. One key challenge is defining a charge scheduling policy that minimizes operating costs and power grid disruptions while maintaining schedule adherence. An uncoordinated policy could result in some buses running out of charge before completing their trip, while a grid agnostic policy might incur higher energy costs or cause an adverse impact on the grid’s distribution system. We present a grid aware decision-theoretic framework for electric bus charge scheduling that accounts for energy price, grid load, and bus status. The framework connects environmental models for traffic (SUMO) and the electric grid (GridLAB-D) to a planner, which evaluates bus charging decisions with regard to their long-term effect on grid reliability and cost. The framework has been evaluated on a simulation of Richland, WA’s bus and grid network, and compared to greedy methods.
We consider the setting of sequential decision making where, at each stage, potential actions are evaluated based on expected reduction in posterior uncertainty, given by mutual information (MI). As MI typically lacks a closed form, we propose an approach which maintains variational approximations of, both, the posterior and MI utility. Our planning objective extends an established variational bound on MI to the setting of sequential planning. The result, variational information planning (VIP), is an efficient method for sequential decision making. We further establish convexity of the variational planning objective and, under conditional exponential family approximations, we show that the optimal MI bound arises from a relaxation of the well-known exponential family moment matching property. Here, we demonstrate VIP for sensor selection, experiment design, and active learning, where it meets or exceeds methods requiring more computation, or those specialized to the task.
Security at every site is critical to making hydropower a strong contributor to the country's grid, but with ongoing development and expanding capabilities, the diversity of the existing hydropower fleet makes across-the-board investment decisions difficult. The threat of cyberattacks naturally increases as the interconnection of Information Technology and Operational Technology networks broadens. Hydropower plants require custom analyses that are specific to the unique challenges and characteristics of any given facility. Facilities, however, often do not have the necessary resources for managers to make informed decisions on investments based on assessed capabilities and risks.
Monitoring nodes may generate a series of current monitoring node values over time representing current operation of a cyber-physical system. A decision fusion computer platform may receive, from a local status determination module, an indication of whether each node has an initial local status of “normal”/“abnormal” and a local certainty score (with higher values of the local certainty score representing greater likelihood of abnormality). The computer platform may also receive, from a global status determination module, an indication of whether the system has an initial global status of “normal”/“abnormal” and a global certainty score. The computer platform may output, for each node, a fused local status of “normal” or “abnormal,” at least one fused local status being based on the initial global status. The decision fusion computer platform may also output a fused global status of “normal” or “abnormal” based on at least one initial local status.
Security at every site is critical to making hydropower a strong contributor to the country's grid, but with ongoing development and expanding capabilities, the diversity of the existing hydropower fleet makes across-the-board investment decisions difficult. The threat of cyberattacks naturally increases as the interconnection of Information Technology and Operational Technology networks broadens. Hydropower plants require custom analyses that are specific to the unique challenges and characteristics of any given facility. Facilities, however, often do not have the necessary resources for managers to make informed decisions on investments based on assessed capabilities and risks.
This work focuses on developing and implementing immersive digital twins to support decision-making processes in construction automation and energy simulation analysis. The use of digital twins enables users to explore and interact with virtual replicas of physical systems, providing a unique and valuable perspective for problem-solving. In the context of construction automation, we present two case studies that integrate immersive visualization and worker-machine interaction. In the context of energy simulation analysis using digital twins, we describe our digital twin framework that combines interactive visualization with machine learning techniques to approximate computationally expensive simulations, allowing for faster and more efficient analysis of complex energy systems. Through these research endeavors, we demonstrate the potential of immersive digital twins in decision-making across various domains.
In Multi-Policy Decision-Making (MPDM), many computationally-expensive forward simulations are performed in order to predict the performance of a set of candidate policies. In risk-aware formulations of MPDM, only the worst outcomes affect the decision making process, and efficiently finding these influential outcomes becomes the core challenge. Recently, stochastic gradient optimization algorithms, using a heuristic function, were shown to be significantly superior to random sampling. In this disclosure, it was shown that accurate gradients can be computed-even through a complex forward simulation—using approaches similar to those in dep networks. The proposed approach finds influential outcomes more reliably, and is faster than earlier methods, allowing one to evaluate more policies while simultaneously eliminating the need to design an easily-differentiable heuristic function.
The Decision Support Tool helps users address four discrete challenges: (1) complex requirements of relevant codes and standards, (2) technical complexity of solar assets, (3) undefined cyber risk severity, and (4) unclear roles and responsibilities. Content includes an analysis of cyber vulnerability risks, a decision support resource for policymakers to mitigate cyber risks to solar photovoltaic systems, and background resources for informing policy development. A key component of this tool is the Probable Risk Assessment (PRA), based on established, formal variables, models, and consequences, which helps users understand the determined risk and assigned ownership of the physical components. It helps states draw logical lines between vulnerabilities and mitigative solutions, of which some have been further detailed as Cybersecurity Advisory Team for State Solar (CATSS) tools.
A series of site investigations and decision-support analyses have been performed related to a chromium plume in the regional aquifer beneath the Los Alamos National Laboratory (LANL). Based on the collected data and site information, alternative conceptual and numerical models representing governing subsurface processes with different complexity and resolution have been developed. The current conceptual model is supported by multiple lines of evidence based on comprehensive analyses of the available data and modeling results. The model is applied for decision-support analyses related to estimation of contaminant- arrival locations and chromium mass flux reaching the regional aquifer, and to optimization of a site monitoring-well network. Plume characterization is a challenging and non-unique problem because multiple models and contamination scenarios are consistent with the site data and conceptual knowledge. To solve this complex problem, an advanced methodology based on model calibration and uncertainty quantification has been developed within the computational framework MADS (http://mads.lanl.gov). This work implements high-performance computing and novel, efficient and robust model analysis techniques for optimization and uncertainty quantification (ABAGUS, Squads, multi-try (multi-start) techniques), which allow for solving problems with large degrees of freedom. (authors)
The U.S. Department of Energy (DOE) owns a low-level radioactive waste (LLW) disposal site at Material Disposal Area G, in Los Alamos, New Mexico, USA. Area G has been the primary LLW disposal site for Los Alamos National Laboratory (LANL) since the 1960's, and is now approaching the end of its service life. In addition to LLW, Area G is host to a variety of other wastes, the disposition of which must be determined before closure of the site. A probabilistic Radiological Risk Assessment (RRA) for Area G is used in order to support decision making regarding some wastes that are not addressed in the extant Area G Performance Assessment (PA) and Composite Analysis (CA). Between 1979 and 1987, 33 special shafts were augered into the Bandelier Tuff at Area G. This tuff is present across Pajarito Plateau on the eastern slopes of the Jemez Mountains, and varies widely in its consistency, from weakly indurated non-welded layers to welded layers that uphold the mesa cliffs of the Plateau. These mesas are home to LANL, Area G, and the town of Los Alamos. The 33 Shafts were lined with steel casing, and contain remote-handled (RH) transuranic wastes (TRU) resulting from experiments and analysis performed in special glove boxes at the Chemistry and Metallurgy Research (CMR) facility at LANL. Some of these wastes originated as used ('spent') nuclear fuel. The purpose of the Area G RRA is to evaluate the potential future risk to humans and the environment from the RH TRU in the 33 Shafts in the context of the risk associated with the surrounding wastes at Area G. The analysis is responsive to expectations outlined in DOE Order 458.1, Radiation Protection of the Public and the Environment, and is informed by the Manual and Guidance accompanying DOE O 435.1, Radioactive Waste Management. Because the waste meets the definition of TRU, the regulatory context also takes into consideration the regulation governing the disposal of TRU from the U.S. Environmental Protection Agency (EPA): 40 CFR 191, Environmental Radiation Protection Standards for Management and Disposal of Spent Nuclear Fuel, High-Level and Transuranic Radioactive Wastes. Given the broader regulatory context for the RRA, the analysis is subject to different assumptions from those made in the existing DOE O 435.1 PA and CA, such as allowing for future occupation of the site. The analysis begins with a comprehensive evaluation of features, events, processes, and exposure scenarios (FEPS) for Area G and the wastes it contains. These FEPS are screened to eliminate from further consideration those of extremely low probability and/or consequence, and a conceptual site model (CSM) is subsequently developed. The scope and structure of the Area G RRA Model is informed by this CSM, and the Area G RRA Model is developed using the GoldSim systems analysis modeling platform. This paper outlines the development of a defensible, transparent, and reasonably realistic model, which is based on the state of knowledge of the wastes, the site, and the FEPS that govern contaminant transport and human exposures. Probabilistic model input distributions represent uncertainties inherent in the real and modeled systems. The results of the Area G RRA Model will inform decision analysis regarding the disposition of the RH TRU in the 33 Shafts. (authors)
Hanford's 242-A Evaporator has had the mission of reducing waste volume in the Hanford Tank Farms since the late 1970's. Until 2014, the Documented Safety Analysis for the 242-A facility considered protection and controls of the collocated worker (workers outside the facility) and the public. In 2010, Tank Farms Nuclear Safety engineers initiated a significant revision of the Documented Safety Analysis to implement DOE-STD-3009, Change Notice 3, which requires the protection of the facility worker in addition to the collocated worker and public. Through the use of an extensive process hazards analysis performed on the facility, it was concluded that it was essential to control conditions in the primary evaporation vessel (C-A-1) such that flammable gasses could not build up to the Lower Flammability Limit, and that another control was necessary to ensure that the C-A-1 vessel could not overflow. Early control decision meetings held during the development of the Documented Safety Analysis revision yielded no simple solution to the protection of workers from a flammable gas deflagration. Controls selected for the waste overflow accident were all mitigative in nature (i.e. the detection of a waste overflow after it already happened). Engineering could not replace the mitigative controls with preventive controls without attempting to credit some of the existing facility interlocks associated with the C-A-1 vessel operations. Software and hard-wired interlocks had been used at the facility to prevent overheating of the waste and overfilling of the C-A-1 vessel. These interlocks were considered useful to the prevention of the flammable gas deflagration and vessel overfilling accidents, but lacked the pedigree and reliability necessary to credit the interlocks as safety significant. It was at this stage of the Documented Safety Analysis control decision and control development that engineering became aware of the DOE's acceptance of the ISA-84 instrumented system standard. It became apparent that the ideal solution was not to attempt to credit existing interlock systems, but to develop a new system of 'safety interlocks' to the ISA-84 standard. A meeting was held with upper management of both engineering and operations to propose the use of safety instrumented systems at the 242-A facility. URS Corporation (now AECOM), who had recently supported the Savanah River site in developing ISA-84 controls, was consulted to support the development of two new instrumented systems for the 242-A facility. In addition to the use of the newly adopted ISA-84 standard, the failure modes and effect analysis process was implemented in a very effective manner to support the development of the new safety instrumented systems. Through a brand new methodology, and through a very dedicated and hard-working engineering, projects, and operations team, the new controls were designed, built, tested, and commissioned. At this time, five successful waste reduction campaigns have been performed at the facility with the new safety instrumented systems. During these waste processing campaigns, trips and spurious trips of the SIS have occurred - all of which acted properly. (authors)
SADA is a collaboration between the University of Tennessee and Oak Ridge National Laboratory to present a freeware desktop application integrating environmental risk analytics, spatial modeling, and decision sciences for use at EPA, NRC, DOE, and DoD contaminated sites. The software embeds risk assessment, uncertainty modeling, and downstream decision processes within a spatial context. Updates to Version 6 will provide will include a rich GIS environment and a new 3-D visualization model which permits immersive visualization of contamination, with topology, and the built environment. The risk models presented are developed in conjunction with Superfund to implement all scenarios in their Regional Screening Levels (RSLs) and radionuclide preliminary remediation goals (PRGs). In addition, SADA will be able to ingest high resolution LandScan population data providing insights into the proximity of population to contamination. A brief overview of the progress in provided along with anticipated next steps. (authors)
In a short time the Swedish nuclear operator OKG have to shift from a operator of three nuclear power plants at one site to a company where both decommissioning and operation will take place in parallel for numerous of years as a result of a decision to permanently shut down two of the three reactors. The paper will be focusing on the challenges to change organization and mind-set from operations to decommissioning within parts of a company while still maintaining and developing the highest standard for the plant still in operation, and this in the combination with a decision of permanent shutdown earlier than expected. The paper will also address how OKG managed to create and maintain momentum in essential processes like permits, defueling and project planning before, during and short after the organization was changed and some of the employees had to leave the company. (authors)