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At least 685 records · Page 38

All sources NASA NTRS DOE OSTI

NASA NTRS2017

Adaptive Management Using Remote Sensing and Ecosystem Modeling in Response to Climate Variability and Invasive Aquatic Plants for the California Sacramento-San Joaquin Delta Water Resource

The California Sacramento-San Joaquin River Delta is the hub for California's water supply and supports important ecosystem services, agriculture, and communities in Northern to Southern California. Expansion of invasive aquatic plants in the Delta coupled with impacts of changing climate and long-term drought is detrimental to the San Francisco Bay/California Delta complex. NASA Ames Research Center and the USDA-ARS partnered with the State of California to develop science-based, adaptive-management strategies for invasive aquatic plant in the Sacramento-San Joaquin Delta. Specific mapping tools developed utilizing satellite and airborne platforms provide regular assessments of population dynamics on a landscape scale and support both strategic planning and operational decision making for resource managers. San Joaquin and Sacramento River watersheds water quality input to the Delta is modeled using the Soil-Water Assessment Tool (SWAT) and a modified SWAT tool has been customized to account for unique landscape and management of agricultural water supply and drainage within the Delta. Environmental response models for growth of invasive aquatic weeds are being parameterized and coupled with spatial distribution/biomass density mapping and water quality to study ecosystem response to climate and aquatic plant management practices. On the water validation and operational utilization of these tools by management agencies and how they are improving decision making, management effectiveness and efficiency will be discussed. The project combines science, operations, and economics related to integrated management scenarios for aquatic weeds to help land and water resource managers make science-informed decisions regarding management and outcomes.

Adaptive↗

NASA NTRS1991

Large constraint length high speed viterbi decoder based on a modular hierarchial decomposition of the deBruijn graph

A method of formulating and packaging decision-making elements into a long constraint length Viterbi decoder which involves formulating the decision-making processors as individual Viterbi butterfly processors that are interconnected in a deBruijn graph configuration. A fully distributed architecture, which achieves high decoding speeds, is made feasible by novel wiring and partitioning of the state diagram. This partitioning defines universal modules, which can be used to build any size decoder, such that a large number of wires is contained inside each module, and a small number of wires is needed to connect modules. The total system is modular and hierarchical, and it implements a large proportion of the required wiring internally within modules and may include some external wiring to fully complete the deBruijn graph. pg,14.

Collins, Oliver↗

DOE OSTI2024

A Model-Based Systems Engineering Approach for Effective Decision Support of Modern Energy Systems Depicted with Clean Hydrogen Production

A holistic approach to decision-making in modern energy systems is vital due to their increase in complexity and interconnectedness. However, decision makers often rely on narrowly-focused strategies, such as economic assessments, for energy system strategy selection. The approach in this paper helps considers various factors such as economic viability, technological feasibility, environmental impact, and social acceptance. By integrating these diverse elements, decision makers can identify more economically feasible, sustainable, and resilient energy strategies. While existing focused approaches are valuable since they provide clear metrics of a potential solution (e.g., an economic measure of profitability), they do not offer the much needed system-as-a-whole understanding. This lack of understanding often leads to selecting suboptimal or unfeasible solutions, which is often discovered much later in the process when a change may not be possible. This paper presents a novel evaluation framework to support holistic decision-making in energy systems. The framework is based on a systems thinking approach, applied through systems engineering principles and model-based systems engineering tools, coupled with a multicriteria decision analysis approach. The systems engineering approach guides the development of feasible solutions for novel energy systems, and the multicriteria decision analysis is used for a systematic evaluation of available strategies and objective selection of the best solution. The proposed framework enables holistic, multidisciplinary, and objective evaluations of solutions and strategies for energy systems, clearly demonstrates the pros and cons of available options, and supports knowledge collection and retention to be used for a different scenario or context. The framework is demonstrated in case study evaluation solutions for a novel energy system of clean hydrogen generation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

AI-Guided Reasoning-Based Operator Support System for the Nuclear Power Plant Management

The Decision-making process in the Nuclear Power Plant (NPP) control room faces some challenges: operator incomplete knowledge, insufficient time for responding to the highly dynamic events, and a large number of indicators to monitor. Because of the complexity of the NPP system, it is hard to pre-plan all the failures/mitigative actions. An intelligent operator support system is vital to mitigate these shortcomings. In this paper, an AI declarative approach (Answer Set Programming (ASP)) is employed to represent our knowledge of the nuclear power plant in the form of logic rules. This represented knowledge is structured to form a reasoning-based operator support system. When an incident occurs, this ASP-based reasoning support system is demonstrated to be capable of fault identification (diagnosis), informing the operator of different scenarios and consequences, and generating the control options (decision making).

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS2015

A Framework for the Cross-Sectoral Integration of Multi-Model Impact Projections: Land Use Decisions Under Climate Impacts Uncertainties

Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impactmodel setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making. Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop- and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making

farmlands↗

DOE OSTI2020

Risk Model for EM Decision Support Toolsets

The Government Office of Accountability (GAO) has published several reports identifying the need for the Department of Energy (DOE) Office of Environmental Management (EM) to address mounting costs for DoE's cleanup program. DOEEM could greatly benefit from independent decision tool-sets/models that would allow them to evaluate options and inform business decision at the enterprise level considering site-specific life cycle costs and system plans. Program goals: Develop a tool-set that enables EM to independently evaluate alternatives, assess outcomes from different contracting strategies, and inform critical decisions for the enterprise. Project goals: Adaption of a Risk Model for integration with complimentary tool-sets for project level decision making. Operational Events: Discrete event model that evaluates operational variables (e.g. capacity, throughput, maintenance constraints) and identify bottlenecks. Identifying and Bounding Project Risk: Identify risks that impact confidence in meeting goals (e.g. production). Life cycle Cost: Evaluate impacts of staffing levels, inventory, and capital investments on life cycle costs. Methods and approach: Monte Carlo Analysis is being executed to generate results: Input derives from Risk Register Data; Tied to Projected and Target Schedules; Incorporates float duration within the model. Assumes associated risk mitigation actions being completed within a timeline of five fiscal years. Metrics include: Confidence in Meeting Production Goal; Confidence in Safety Standards; Confidence in Continuous Operation; Other Metrics can be added as appropriate regarding specific site needs. The adapted risk model can be used as a stand alone decision tool or can be integrated complementary tool-sets (i.e. process and cost models) for project-specific decisions. These support tool-sets can then be integrated with others for site- and complex- level evaluations. Future work includes designing an adaptable and modular framework that would allow integration of multiple tool-sets for holistic and/or targeted evaluation of alternative strategies for decision making that could lead to risk and cost reduction across the enterprise.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support

Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling. The tremendous potential benefits of SA are, however, yet to be fully realized, both for advancing mechanistic and data-driven modeling of human and natural systems, and in support of decision making. In this perspective paper, a multidisciplinary group of researchers and practitioners revisit the current status of SA, and outline research challenges in regard to both theoretical frameworks and their applications to solve real-world problems. Six areas are discussed that warrant further attention, including (1) structuring and standardizing SA as a discipline, (2) realizing the untapped potential of SA for systems modeling, (3) addressing the computational burden of SA, (4) progressing SA in the context of machine learning, (5) clarifying the relationship and role of SA to uncertainty quantification, and (6) evolving the use of SA in support of decision making. An outlook for the future of SA is provided that underlines how SA must underpin a wide variety of activities to better serve science and society.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

A comparison between residential relocation timing of Sydney and Chicago residents: A Bayesian survival analysis

We report that understanding households' behaviour in residential relocation timing is of great importance in the field of transport engineering and economics. This research aims to develop a residential relocation model by considering the potential dynamic impacts of other households' decisions and variables, including economic and demographic attributes, housing features, intra-household decision-making structures, travel mode choice, and other life-course attributes. A multivariate parametric survival model with both fixed and time-varying covariates is developed. To the best of the authors' knowledge, this study is the first paper in the literature of residential relocation timing to propose the use of a Bayesian model in contrast to the widely used classic frequentist approach and have conducted a discussion on its advantages. An emerging residential relocation dataset collected for two cities in Australia and the USA (Sydney and Chicago cities) has been used, which covers residence, vehicle ownership, occupation, education, economic and demographic attributes of respondents. A comprehensive comparison between the results of two cities and a comparison between two Bayesian and frequentist approaches are made. This study confirms the impact of life-course variables, intra-household decision-making behaviours, and sociodemographic attributes on home mobility. According to the model outputs, the accelerating or decelerating impact of explanatory variables on the relocation timing has been almost the same in the two cities. The Bayesian model was confirmed to have some advantages over the frequentist model, including being straightforward to interpret, availability of making inferences on the results, and ease of handling complex models, and optimisation convergence complexities.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Reliability modeling in a predictive maintenance context: A margin-based approach

Current system reliability methods (typically based on fault trees or reliability block diagrams) can effectively propagate reliability data from the asset to the system level in order to identify system critical points. However, employed asset reliability data are an approximated integral representation of the past industrywide operational experience, and they neglect the present asset health status (available, for example, from online monitoring data and diagnostic assessments) and forecasted health projection (when available from prognostic models). Asset health should be informed solely by that specific asset’s current and historical performance data and should not be an approximated integral representation of the past industrywide operational experience (as currently performed by system reliability models through Bayesian updating processes). Sensor data, diagnostic assessments, and prognostic assessments are in fact not considered in plant reliability models used to inform system engineers on the most critical assets. In addition, the propagation of quantitative health data from the asset to the system level is a challenge given the diverse nature and structure of health data elements (e.g., vibration spectra, temperature readings, expected failure time). Ideally, in a predictive maintenance context, system reliability models should support decision making by propagating available health information from the asset to the system level in order to provide a quantitative snapshot of system health and identify the most critical assets. Here, this paper is directly addressing these two goals by proposing a different approach for reliability modeling that relies on asset diagnostic and prognostic assessments, along with monitoring data to measure asset health. The propagation of health data from the asset to the system level is performed through fault tree models not in probability terms, but in terms of margin where margin is the “distance” between the present status and an undesired event (e.g., failure or unacceptable performance). Through a cause-effect lens, while classical reliability models target the effect associated with asset performance, a margin-based approach focuses on the cause of an undesired asset performance (i.e., its health). Hence, thinking of reliability in terms of margins implies decision-making based on causal reasoning. We will show how fault tree models can be solved using a margin language and how this process can effectively assist system engineers to identify the most critical assets.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Quick Talk: Resilient Communities, Maryland

Groundswell, Inc., in partnership with the Maryland Energy Administration (MEA), Ayika Solutions, the National Renewable Energy Lab (NREL), and the Baltimore Office of Sustainability, is advancing a vision for resilient, community-centered energy systems in Maryland. This effort aligns with Groundswell's commitment to a clean energy economy that equitably benefits all communities, closing gaps in representation, wages, and access to wealth generation, savings, and local jobs within a diverse energy supply chain. Project Vision and Objectives The project emphasizes "energy resilience," defined as a community’s capacity to anticipate, adapt to, and swiftly recover from power disruptions through inclusive planning and technical solutions. The approach centers on Resilience Hubs—designated community facilities equipped to support residents before, during, and after power-related disruptions. These hubs offer emergency resources, coordinate communications, and deliver essential services while reducing carbon emissions, ultimately enhancing residents' quality of life. Additionally, Resilience Hubs foster social connections, support disaster preparedness, and provide spaces for community programming and everyday use. The goals of Resilient Communities Maryland include: Developing a replicable framework for community energy resilience, applicable beyond Maryland. Defining community-led indicators of social impact and resilience. Establishing equitable decision-making processes in project design and implementation. Informing MEA of practical, community-based resilience strategies to shape future state initiatives. Approach Groundswell and its partners prioritize collaborative engagement with local stakeholders, ensuring that solutions are responsive to each community's unique needs. By integrating feedback from residents, local organizations, and government entities, the team has developed a holistic model for resilience that balances immediate benefits, such as improved emergency response, with long-term gains in social equity and environmental sustainability. Key Outcomes This initiative has produced valuable resources and insights, including: A Community Energy Resilience Framework: Designed to guide other communities in establishing energy resilience strategies tailored to their specific needs. Case Studies: Documenting best practices and lessons learned from pilot projects to serve as models for future resilience efforts. Community-Driven Impact Metrics: Highlighting the positive social impacts that resilience initiatives can bring, such as improved social connectedness and local decision-making power. Recommendations for MEA: Offering evidence-based insights to inform policies and funding opportunities that support resilient energy infrastructure in vulnerable communities. Next Steps Groundswell's final report will detail findings, including the potential for scaling resilience hubs across Maryland and other regions, with a focus on equitable access and community-driven outcomes. This project underscores the importance of aligning energy resilience with social equity, reinforcing community agency, and building systems that withstand and recover from future challenges effectively and sustainably.

14 SOLAR ENERGY↗

DOE OSTI2023

Organizational System Resilience to Disinformation: A Viable Systems Model Exploration

This paper explores the utility of organizational system modeling frameworks to provide valuable insight into information flows within organizations and subsequently the opportunities for increasing resilience against disinformation campaigns targeting the system's ability to utilize information within its decision making. Disinformation is a growing challenge for many organizations and in recent years has created delay in decision making. Here the paper has utilized the viable systems model (VSM) to characterize organizational systems and used this approach to outline potential subsystem requirements to promote resilience of the system. The results of this paper can support the development of simulations and models considering the human elements within the system as well as support the development of quantitative measures of resilience.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

NASA NTRS2013

Wind Turbine Contingency Control Through Generator De-Rating

Maximizing turbine up-time and reducing maintenance costs are key technology drivers for wind turbine operators. Components within wind turbines are subject to considerable stresses due to unpredictable environmental conditions resulting from rapidly changing local dynamics. In that context, systems health management has the aim to assess the state-of-health of components within a wind turbine, to estimate remaining life, and to aid in autonomous decision-making to minimize damage to the turbine. Advanced contingency control is one way to enable autonomous decision-making by providing the mechanism to enable safe and efficient turbine operation. The work reported herein explores the integration of condition monitoring of wind turbines with contingency control to balance the trade-offs between maintaining system health and energy capture. The contingency control involves de-rating the generator operating point to achieve reduced loads on the wind turbine. Results are demonstrated using a high fidelity simulator of a utility-scale wind turbine.

condition monitoring↗

NASA NTRS2010

Reactive Sequencing for Autonomous Navigation Evolving from Phoenix Entry, Descent, and Landing

Virtual Machine Language (VML) is an award-winning advanced procedural sequencing language in use on NASA deep-space missions since 1997, and was used for the successful entry, descent, and landing (EDL) of the Phoenix spacecraft onto the surface of Mars. Phoenix EDL utilized a state-oriented operations architecture which executed within the constraints of the existing VML 2.0 flight capability, compatible with the linear "land or die" nature of the mission. The intricacies of Phoenix EDL included the planned discarding of portions of the vehicle, the complex communications management for relay through on-orbit assets, the presence of temporally indeterminate physical events, and the need to rapidly catch up four days of sequencing should a reboot of the spacecraft flight computer occur shortly before atmospheric entry. These formidable operational challenges led to new techniques for packaging and coordinating reusable sequences called blocks using one-way synchronization via VML sequencing global variable events. The coordinated blocks acted as an ensemble to land the spacecraft, while individually managing various elements in as simple a fashion as possible. This paper outlines prototype VML 2.1 flight capabilities that have evolved from the one-way synchronization techniques in order to implement even more ambitious autonomous mission capabilities. Target missions for these new capabilities include autonomous touch-and-go sampling of cometary and asteroidal bodies, lunar landing of robotic missions, and ultimately landing of crewed lunar vehicles. Close proximity guidance, navigation, and control operations, on-orbit rendezvous, and descent and landing events featured in these missions require elaborate abort capability, manifesting highly non-linear scenarios that are so complex as to overtax traditional sequencing, or even the sort of one-way coordinated sequencing used during EDL. Foreseeing advanced command and control needs for small body and lunar landing guidance, navigation and control scenarios, work began three years ago on substantial upgrades to VML that are now being exercised in scenarios for lunar landing and comet/asteroid rendezvous. The advanced state-based approach includes coordinated state transition machines with distributed decision-making logic. These state machines are not merely sequences - they are reactive logic constructs capable of autonomous decision making within a well-defined domain. Combined with the JPL's AutoNav software used on Deep Space 1 and Deep Impact, the system allows spacecraft to autonomously navigate to an unmapped surface, soft-contact, and either land or ascend. The state machine architecture enabled by VML 2.1 has successfully performed sampling missions and lunar descent missions in a simulated environment, and is progressing toward flight capability. The authors are also investigating using the VML 2.1 flight director architecture to perform autonomous activities like rendezvous with a passive hypothetical Mars sample return capsule. The approach being pursued is similar to the touch-and-go sampling state machines, with the added complications associated with the search for, physical capture of, and securing of a separate spacecraft. Complications include optically finding and tracking the Orbiting Sample Capsule (OSC), keeping the OSC illuminated, making orbital adjustments, and physically capturing the OSC. Other applications could include autonomous science collection and fault compensation.

asteroid↗

DOE OSTI2026

Mobility Hubs: Workforce Landscape Assessment, Development Opportunities, and Local Transportation Planning

Through the U.S. Department of Energy's Energy to Communities (E2C) program, NLR, other national laboratory experts, and select organizations provide Expert Match - free, short-term technical assistance to address near-term energy challenges and questions. Expert Match is for community stakeholders who have decision-making power or influence in their community but need access to additional energy expertise to inform key upcoming decisions. This Expert Match request supported Equticity in Chicago, IL with an analysis workforce development considerations and opportunities and is intended to support decision-making on micromobility initiatives.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2011

Constructive Engineering of Simulations

Joint experimentation that investigates sensor optimization, re-tasking and management has far reaching implications for Department of Defense, Interagency and multinational partners. An adaption of traditional human in the loop (HITL) Modeling and Simulation (M&S) was one approach used to generate the findings necessary to derive and support these implications. Here an entity-based simulation was re-engineered to run on USJFCOM's High Performance Computer (HPC). The HPC was used to support the vast number of constructive runs necessary to produce statistically significant data in a timely manner. Then from the resulting sensitivity analysis, event designers blended the necessary visualization and decision making components into a synthetic environment for the HITL simulations trials. These trials focused on areas where human decision making had the greatest impact on the sensor investigations. Thus, this paper discusses how re-engineering existing M&S for constructive applications can positively influence the design of an associated HITL experiment.

Snyder, Daniel R.↗

NASA NTRSDate not supplied

A Web-Based Negotiation Tool for Conflict Resolution in Upper Class E Traffic Management

In Upper Class E airspace 60,000 feet (or Flight level / FL600), vehicles such as High Altitude Long Endurance (HALE) balloons and slow fixed-wing gliders have diverse vehicle characteristics and limited maneuverability. Due to these unique characteristics of high-altitude operations, a new type of strategic negotiation-based conflict resolution method has been proposed to avoid potential conflict between vehicles in Upper Class E airspace, well in advance of the conflict point and with ample time for bilateral negotiation. This paper introduces the first real-time web-based negotiation tool for high-altitude operations. To facilitate negotiation, this tool incorporates a bilateral negotiation model with an algorithm to assess conflict risks and generate new flight trajectories with a given flight path deviation. This tool allows users to make decisions during negotiation manually and automatically while enforcing the needed constraints for negotiation to be successful. Operators can make decisions at every step while they interact with each other on separate devices. To conduct sensitivity analysis, a mechanism that automates human inputs to the user interface is also developed for this web-based negotiation tool, such that fast-time simulations can be constructed and used to explore various scenarios to gain insights into this web-based negotiation model. Using this tool, a study was conducted to evaluate the impact of different negotiation strategies, vehicle types, vehicle crossing angles, and operator response times during the negotiations on metrics such as total negotiation completion time, number of negotiation rounds, and extra flight distance to avoid the conflict due to negotiation, compared to ones without negotiation. The results suggest that operators benefit from using negotiated flight paths with quick response times, across various crossing angles and vehicle types. Various Negotiation Strategies are investigated to simulate the behaviors associated with different types of negotiations. The findings demonstrate that, in comparison to the conventional method where a single operator assumes full responsibility, negotiation-based strategic deconfliction reduces the total extra flight distance by an average of 24% - 27%. On an individual basis, each operator may be able to save an average of 65% of their extra flight distance.

Upper Class E Traffic Management (ETM)↗

NASA NTRS2017

Sensitivity Analysis Method to Address User Disparities in the Analytic Hierarchy Process

Decision makers often face complex problems, which can seldom be addressed well without the use of structured analytical models. Mathematical models have been developed to streamline and facilitate decision making activities, and among these, the Analytic Hierarchy Process (AHP) constitutes one of the most utilized multi-criteria decision analysis methods. While AHP has been thoroughly researched and applied, the method still shows limitations in terms of addressing user profile disparities. A novel sensitivity analysis method based on local partial derivatives is presented here to address these limitations. This new methodology informs AHP users of which pairwise comparisons most impact the derived weights and the ranking of alternatives. The method can also be applied to decision processes that require the aggregation of results obtained by several users, as it highlights which individuals most critically impact the aggregated group results while also enabling to focus on inputs that drive the final ordering of alternatives. An aerospace design and engineering example that requires group decision making is presented to demonstrate and validate the proposed methodology.

Marie Ivanco↗

NASA NTRS1992

Determining rules for closing customer service centers: A public utility company's fuzzy decision

In the present work, we consider the general problem of knowledge acquisition under uncertainty. A commonly used method is to learn by examples. We observe how the expert solves specific cases and from this infer some rules by which the decision was made. Unique to this work is the fuzzy set representation of the conditions or attributes upon which the decision make may base his fuzzy set decision. From our examples, we infer certain and possible rules containing fuzzy terms. It should be stressed that the procedure determines how closely the expert follows the conditions under consideration in making his decision. We offer two examples pertaining to the possible decision to close a customer service center of a public utility company. In the first example, the decision maker does not follow too closely the conditions. In the second example, the conditions are much more relevant to the decision of the expert.

Dekorvin, Andre↗