Results for “decision support framework” — Page 22

NASA NTRSDate not supplied

Applications of NASA’s LANCE Earth Observations for Ecosystem Assessments

As one of NASA's open and free data systems, NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) provides a wide range of near real-time, low latency and expedited data products from NASA and other earth science satellite missions to support users in their understanding of the value and distribution of ecosystem services. LANCE data can support the System of Environmental Economic Accounting-Ecosystem Accounting (SEEA-EA) framework by both facilitating the creation of ecosystem spatial extent and condition accounts, as well as the quantification and mapping of ecosystem services, much more quickly than routine data processing allows. During the past 13 years, LANCE near real-time satellite data products (e.g., surface reflectance, albedo, vegetation height, thermal anomalies, soil moisture, snow cover etc.) have been used to produce ecosystem-related indicators such as vegetation indexes, biomass, land cover maps, fire, and flood products. These ecosystem-related indicators can be integrated into ecosystem services models, tools, and platforms to monitor land cover and land use changes in ecosystem composition in support of land and natural resource management. When using ecosystem services accounts in decision-making, low latency is essential because having up-to-date information can be critical to support decisions being made. With adequate spatial resolution, long time series length, and low latency data products, LANCE would promote the sustainable use of land and natural resources.

Tian Yao↗

DOE OSTI2025

GenAI4UQ: A software for forward and inverse uncertainty quantification using conditional generative AI

We introduce GenAI4UQ, a software package for forward and inverse uncertainty quantification in model calibration, parameter estimation, and ensemble forecasting. GenAI4UQ leverages a generative AI-based conditional modeling framework to address limitations of traditional inverse modeling techniques, such as Markov Chain Monte Carlo (MCMC) methods. By replacing computationally intensive iterative processes with a direct, learned mapping, GenAI4UQ enables efficient calibration of input parameters and generation of predictions directly from observations. The software supports rapid ensemble forecasting with robust uncertainty quantification while maintaining computational and storage efficiency. Built-in auto-tuning of hyperparameters simplifies model training, ensuring accessibility for users with varying expertise. Its versatile conditional generative framework is applicable across diverse scientific domains. While GenAI4UQ offers significant advantages in flexibility and efficiency, users should interpret its uncertainty estimates with caution in data-sparse scenarios, as the model may overestimate uncertainty—an effect common to all surrogate-based approaches including MCMC with surrogate models. Despite this, GenAI4UQ transforms inverse modeling by providing a fast, reliable, and user-friendly solution. It empowers researchers and practitioners to quickly estimate parameter distributions and generate model predictions for new observations, facilitating efficient decision-making and advancing the state of uncertainty quantification in computational modeling.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2016

Integrating Spaceflight Human System Risk Research

NASA is working to increase the likelihood of human health and performance success during exploration missions as well as to maintain the subsequent long-term health of the crew. To manage the risks in achieving these goals, a system modelled after a Continuous Risk Management framework is in place. "Human System Risks" (Risks) have been identified, and approximately 30 are being actively addressed by NASA's Human Research Program (HRP). Research plans for each of HRP's Risks have been developed and are being executed. Inter-disciplinary ties between the research efforts supporting each Risk have been identified; however, efforts to identify and benefit from these connections have been mostly ad hoc. There is growing recognition that solutions developed to address the full set of Risks covering medical, physiological, behavioural, vehicle, and organizational aspects of exploration missions must be integrated across Risks and disciplines. This paper discusses how a framework of factors influencing human health and performance in space is being applied as the backbone for bringing together sometimes disparate information relevant to the individual Risks. The resulting interrelated information enables identification and visualization of connections between Risks and research efforts in a systematic and standardized manner. This paper also discusses the applications of the visualizations and insights into research planning, solicitation, and decision-making processes.

Mindock, Jennifer↗

DOE OSTI2021

Physics-Infused AI/ML Based Digital-Twin Framework for Flow-Induced-Vibration Damage Prediction in a Nuclear Reactor Heat Exchanger

This report summarizes some of the ongoing work related to the development of an expert-elicitation-digital-twin framework for real time damage state prediction in heat exchanger components of a nuclear reactor. The framework is targeted towards predicting damage associated with coupled low cycle fatigue (associated with regular heat-up, cool-down and power operation transients) and high cycle fatigue (associated with flow induced vibration transients). The overall framework will be based on a NoSQL based database, physics-infused-geometry-dependent virtual-sensor data, different AI/ML techniques-based data-driven-predictive-model applications (Apps) and real-time plant sensor measurements available through few existing sensors. Towards this overall goal, this report updates some of the ongoing work, such as on implementation of a NoSQL Database (such as MongoDB), FE based heat transfer analysis of a heat exchanger (e.g. of a PWR steam generator) for generating geometry-dependent virtual sensor data and evaluation of various AI/ML models such as based on multivariate linear regression, ensembled decision-tree based Random-Forest and Gradient-Boosting regression and high-dimensional-kernel-function-transformation based Support-Vector-Machine regression models. The AI/ML models were evaluated for predicting multi-time-series thermal states at thousands of 3D point-clouds

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Spatially and Temporally Detailed Water and Carbon Footprints of U.S. Electricity Generation and Use

Electricity generation in the United States entails significant water usage and greenhouse gas emissions. However, accurately estimating these impacts is complex due to the intricate nature of the electric grid and the dynamic electricity mix. Existing methods to estimate the environmental consequences of electricity use often generalize across large regions, neglecting spatial and temporal variations in water usage and emissions. Consequently, electric grid dynamics, such as temporal fluctuations in renewable energy resources, are often overlooked in efforts to mitigate environmental impacts. The U.S. Department of Energy (DOE) has initiated the development of resilient energyshed management systems, requiring detailed information on the local electricity mix and its environmental impacts. This study supports DOE's goal by incorporating geographic and temporal variations in the electricity mix of the local electric grid to better understand the environmental impacts of electricity end users. We offer hourly estimates of the U.S. electricity mix, detailing fuel types, water withdrawal intensity, and water consumption intensity for each grid balancing authority through our publicly accessible tool, the Water Integrated Mapping of Power and Carbon Tracker (Water IMPACT). While our primary focus is on evaluating water intensity factors, our dataset and programming scripts for historical and real-time analysis also include evaluations of carbon dioxide (equivalence) intensity within the same modeling framework. This integrated approach offers a comprehensive understanding of the environmental footprint associated with electricity generation and use, enabling informed decision-making to effectively reduce Scope 2 water usage and emissions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2000

Ames Research Center FY 2000 Implementation Plan: Leading Technology into the New Millennium

This document presents the implementation plan for Ames Research Center (ARC) within the overall framework of the NASA Strategic Plan. It describes how ARC intends to implement its Center of Excellence responsibilities, Agency assigned missions, Agency and Enterprise lead programs, and other roles in support of NASA's vision and mission. All Federal agencies are required by the 1993 Government Performance and Results Act to implement a long-term strategic planning process that includes measurable outcomes and strict accountability. At NASA, this planning process is shaped by the Space Act of 1958, annual appropriations, and other external mandates, as well as by customer requirements. The resulting Strategic Plan sets the overall architecture for what we do, identifies who our customers are, and directs where we are going and why. The Strategic Plan is the basis upon which decisions regarding program implementation and resource deployment are made. Whereas the strategic planning process examines the long-term direction of the organization and identifies a specific set of goals, the implementation planning process examines the detailed performance of the organization and allocates resources toward meeting these goals. It is the purpose of this implementation document to provide the connection between the NASA Strategic Plan and the specific programs and support functions that ARC employees perform. This connection flows from the NASA Strategic Plan, through the various Strategic Enterprise plans to the ARC Center of Excellence, primary missions, Lead Center programs, program support responsibilities, and ultimately, to the role of the individual ARC employee.

Source record↗

NASA NTRSDate not supplied

Implementing an Objectives-Driven, Risk-Informed, and Case-Assured Approach to Safety and Mission Success at NASA

NASA is developing a “Standard for Assurance of Space Flight Safety and Mission Success” that implements an objectives-driven, risk-informed, and case-assured approach to safety and mission success (S&MS) for NASA space flight programs and projects. The standard aligns with the philosophy of risk leadership that has recently been established in NASA policy to assure acceptable levels of flight crew safety and mission success risk. It is consistent with existing NASA risk management requirements and is compatible with NASA program management and systems engineering requirements. The methodology described in the standard is presented in terms of an S&MS assurance framework that is designed to allow substantial flexibility in the specific means by which programs and projects achieve acceptable mission S&MS risk. Such flexibility is necessary to accommodate the increasingly broad range of acquisition strategies employed by NASA, including commercial transportation services, as well as to accommodate the increasingly rapid evolution of space flight-related technologies and practices. A key feature of the S&MS assurance framework is the specification of S&MS success criteria for each life-cycle review (LCR). The S&MS assurance case is structured around these criteria, the satisfaction of which indicates that the program/project is adhering to the S&MS risk posture. This enables the evolving S&MS assurance case to be used as a fundamental program/project submittal at each LCR, where its inherent structure of argument, supported by evidence, directly supports the evaluation of the program/project with respect to the S&MS success criteria, and by extension, the S&MS risk posture. As such, the S&MS assurance case is integral to program/project systems engineering, risk management, and S&MS oversight activities, and provides the principal basis for S&MS risk acceptance by the Decision Authority throughout the program/project life cycle.

Risk Posture↗

DOE OSTI2024

Implementing an Objectives-Driven, Risk-Informed, and Case-Assured Approach to Safety and Mission Success at NASA

NASA is developing a “Standard for Assurance of Space Flight Safety and Mission Success” that implements an objectives-driven, risk-informed, and case-assured approach to safety and mission success (S&MS) for NASA space flight programs and projects. The standard aligns with the philosophy of risk leadership that has recently been established in NASA policy to assure acceptable levels of flight crew safety and mission success risk. It is consistent with existing NASA risk management requirements and is compatible with NASA program management and systems engineering requirements. The methodology described in the standard is presented in terms of an S&MS assurance framework that is designed to allow substantial flexibility in the specific means by which programs and projects achieve acceptable mission S&MS risk. Such flexibility is necessary to accommodate the increasingly broad range of acquisition strategies employed by NASA, including commercial transportation services, as well as to accommodate the increasingly rapid evolution of space flight-related technologies and practices. A key feature of the S&MS assurance framework is the specification of S&MS success criteria for each life-cycle review (LCR). The S&MS assurance case is structured around these criteria, the satisfaction of which indicates that the program/project is adhering to the S&MS risk posture. This enables the evolving S&MS assurance case to be used as a fundamental program/project submittal at each LCR, where its inherent structure of argument, supported by evidence, directly supports the evaluation of the program/project with respect to the S&MS success criteria, and by extension, the S&MS risk posture. As such, the S&MS assurance case is integral to program/project systems engineering, risk management, and S&MS oversight activities, and provides the principal basis for S&MS risk acceptance by the Decision Authority throughout the program/project life cycle.

42 ENGINEERING↗

NASA NTRS2021

ATM TestBed 2021 Simulating & Testing for the Future of Flight

TestBed is a NASA facility that not only connects geographically distributed tools without having to worry about obsolescence, but it is also a comprehensive one-stop-access simulation framework that allows researchers to develop and test solutions to current and future challenges more quickly by addressing development pain points, while also facilitating the connection of simulators and new tools to a variety of organizations, allowing for faster testing and development. Specifically, TestBed enables one to better characterize the impact of a new technology under development and to communicate its impact to stakeholders.

Air Traffic Management↗

DOE OSTI2025

Network Slicing for Federated Learning in Operational Technology Environment

Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA) environments are essential to modern infrastructure, facing challenges in ensuring low-latency, high-throughput communication while mitigating cyber threats. This paper presents a framework integrating Federated Learning (FL) and network slicing with Quality of Service (QoS) to enable real-time monitoring without disrupting OT operations. Leveraging digital twin technology and Network Function Virtualization (NFV), the architecture supports predictive analytics and Industry 4.0 requirements. FL facilitates decentralized model training, preserving data privacy and scalability, though it introduces potential throughput constraints. Network slicing addresses this by creating dedicated virtualized segments optimized for performance and security. Advanced fault tolerance at the container and instance levels enhances system reliability. The proposed architecture ensures high throughput, low latency, and secure orchestration for real-time anomaly detection in OT networks. Performance evaluations validate its efficiency in throughput, deployment, and learning accuracy, providing a robust foundation for future ICS automation and data-driven decision-making.

Delgado, Brian G. Rodiles [University of Texas at ↗

NASA NTRS2015

NASA System Safety Handbook. Volume 2: System Safety Concepts, Guidelines, and Implementation Examples

This is the second of two volumes that collectively comprise the NASA System Safety Handbook. Volume 1 (NASASP-210-580) was prepared for the purpose of presenting the overall framework for System Safety and for providing the general concepts needed to implement the framework. Volume 2 provides guidance for implementing these concepts as an integral part of systems engineering and risk management. This guidance addresses the following functional areas: 1.The development of objectives that collectively define adequate safety for a system, and the safety requirements derived from these objectives that are levied on the system. 2.The conduct of system safety activities, performed to meet the safety requirements, with specific emphasis on the conduct of integrated safety analysis (ISA) as a fundamental means by which systems engineering and risk management decisions are risk-informed. 3.The development of a risk-informed safety case (RISC) at major milestone reviews to argue that the systems safety objectives are satisfied (and therefore that the system is adequately safe). 4.The evaluation of the RISC (including supporting evidence) using a defined set of evaluation criteria, to assess the veracity of the claims made therein in order to support risk acceptance decisions.

Safety Case↗

DOE OSTI2025

AI Benchmark Democratization and Carpentry

Benchmarks are a cornerstone of modern machine learning, enabling reproducibility, comparison, and scientific progress. However, AI benchmarks are increasingly complex, requiring dynamic, AI-focused workflows. Rapid evolution in model architectures, scale, datasets, and deployment contexts makes evaluation a moving target. Large language models often memorize static benchmarks, causing a gap between benchmark results and real-world performance. Beyond traditional static benchmarks, continuous adaptive benchmarking frameworks are needed to align scientific assessment with deployment risks. This calls for skills and education in AI Benchmark Carpentry. From our experience with MLCommons, educational initiatives, and programs like the DOE's Trillion Parameter Consortium, key barriers include high resource demands, limited access to specialized hardware, lack of benchmark design expertise, and uncertainty in relating results to application domains. Current benchmarks often emphasize peak performance on top-tier hardware, offering limited guidance for diverse, real-world scenarios. Benchmarking must become dynamic, incorporating evolving models, updated data, and heterogeneous platforms while maintaining transparency, reproducibility, and interpretability. Democratization requires both technical innovation and systematic education across levels, building sustained expertise in benchmark design and use. Benchmarks should support application-relevant comparisons, enabling informed, context-sensitive decisions. Dynamic, inclusive benchmarking will ensure evaluation keeps pace with AI evolution and supports responsible, reproducible, and accessible AI deployment. Community efforts can provide a foundation for AI Benchmark Carpentry.

von Laszewski, Gregor [Virginia U.]↗

DOE OSTI2025

Integrated Hydro-terrestrial Modeling 2.0: Progress and Path Forward on Building a National Capability

It is the role of the U.S. federal government and its supporting agencies, including academia and future scientists, to ensure that its people have sustained and equitable freshwater services, as well as the critical knowledge necessary to make decisions about the future as it relates to freshwater services. Clear and consistent information and guidance from federal agencies is critical. Integrated Hydro-Terrestrial Modeling (IHTM), as a United States (U.S.) national capability, focuses on understanding, quantifying, and managing the replenishment of water supply through hydrologic cycle processes and their governing forces. To provide that information, we need enhanced IHTM capabilities that capitalize on the strengths of each U.S. governmental agency and its core mission. The first IHTM workshop was held in 2019, and its subsequent report was published in 2020. The U.S. Global Change Research Program (USGCRP) and member agencies held a second IHTM workshop (IHTM 2.0) from October 31 to November 2, 2023 in Reston, Virginia. The IHTM 2.0 workshop focused on the need to support a multiscale framework to accelerate research insights, better integrate operational and planning perspectives, and bridge national-to-regional capabilities to address major interdependent societal water challenges. The workshop was organized according to a “WHAT” and “HOW” framework, with the common underlying “WHY” being the integrated water resource challenges and the “WHO” defined through interagency and cooperating academic partners. The report provides a summary of plenary presentations and breakout discussions, and a road map that focuses on near-term activities.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Sequential Decision Making (SDM) for Mesh Refinement and Model Selection in Multiscale, Multi-Physics Applications

Intelligent automation and decision support are needed to enhance computational efficiency and robustness in multiscale and multi-physics problems, including materials science, manufacturing, and climate and weather modeling. Current scientific computing approaches for enabling decisions by scientists fail to explore the role of learning, reasoning, and probabilistic planning. Often these decisions are not performed in real-time during the computation but are made prior to the start of the computation, which must be interrupted in order to make changes to the prior choices. Such interruptions at different stages of the computation increase the total computing time and the need for a human expert to frequently monitor the results. State of art scientific computing methods consist of rule-based algorithms that cannot automatically adapt to a dynamically changing computing environment. The development of a Sequential Decision Making (SDM) framework will automate scientific computing by optimizing the policies for mesh refinement, time-stepping, model and algorithm selection, resource allocation, and pre and post-processing. Our agent SDM framework for scientific computing will consist of data-driven learning (Classifier), automated reasoning (contextual knowledge), and probabilistic planning (Reinforcement Learning). In this project, we focused on three problems to demonstrate our SDM framework on a set of ordinary and partial differential equations. Classification of Lorenz system regions using Feed-Forward Neural Networks examined learning in the SDM framework. On the other hand, reasoning and planning in the SDM framework were used in two problems: adaptive time-stepping for nonlinear ODEs using on-policy RL algorithms, and adaptive mesh refinement for 2-D PDEs using off-policy RL algorithms.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2020

Integrated assessment of climate change impacts on crop productivity and income of commercial maize farms in northeast South Africa

Agriculture in South Africa sustains about 70% of the region’s population for food, income and employment, playing an important role for food security and the local economy. The focus of the study was the commercial maize farms of the Free State Province given their importance in the National economy. The Regional Integrated Assessment (phase I) was implemented to assess climate change and adaptation that links climate, crops, economic data and tools developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP). In this context, the“system”is defined as a whole of agronomic and socio-economic factors. Within that framework three core questions were being evaluated: (i) Impacts of climate change under current system; (ii) Impacts of climate change under future system; (iii) The role of adaptation under climate change and the future system. Maize production will decrease between 10% to 16% as a result of projected climate impacts. Also, current agricultural production systems are negatively affected by climate change with an increase in poverty rates between 2% to 3%. The projected adoption of the adapted technology would result in positive increased net returns and a decrease in poverty rate of between 12%and 22%. The results of this study show that implementing adaptation measures and other strategies as indicated by the local stakeholders will have positive impacts on the agricultural production systems and can contribute to support and inform climate change policy decision making such as the development of National Adaptation Plans.

Integrated assessment↗

DOE OSTI2023

Study of Storage Requirements and Costs for Shaping Renewables and Nuclear Energy (FY23 Summary Report)

To decarbonize electricity generation primarily by using wind and solar resources, it is likely that additional low (or zero) carbon dioxide (CO 2 ) alternatives will be required for ensuring a reliable electricity supply. This study extends the work and modeling framework that we consolidated in FY 2022, by assessing the zero-CO 2 pathways of a Texas power system in which the availability of variable renewable energy (VRE), nuclear energy, and energy storage types (i.e., battery and thermal energy storage [TES]) are considered the sole resources available for expansion. Using the Risk Analysis Virtual Environment (RAVEN) and Holistic Energy Resource Optimization Network (HERON) frameworks, this study investigates the market of two nuclear energy technologies (i.e., large light-water reactors [LWRs] and LWR-type small modular reactors [SMRs]) under two plausible storage coupling scenarios (i.e., electric coupling and direct thermal coupling). We also explore optimal environments for nuclear energy deployment, and identify key performance characteristics that make nuclear energy economically viable in areas with significant intermittent energy sources. Our analysis encompasses 24 cases. We model the least-cost grid systems, highlight the potential for a coupled LWR-TES approach, and utilize SMRs to achieve deep decarbonization in an affordable, technically feasible manner. We also examine seasonal variations in balancing electricity supply and demand, and account for varying performance, costs, and grid constraints. Overall, the findings of our modeling afford valuable insights for supporting future technology investment decisions in the energy sector.

25 ENERGY STORAGE↗

NASA NTRS2022

An ODE-Fitting Approach to Estimate Critical Aircraft Performance Parameters for Trajectory Prediction

Ground-based decision support tools (DST) in air traffic management (ATM) typically perform trajectory prediction based on aircraft performance model (APM) parameters, but some or all of these parameters might not be readily available. In particular, the three critical parameters required for trajectory prediction are the thrust setting, drag coefficients, and takeoff weight of the aircraft. Unfortunately, these parameters are coupled and appear together in physics-based kinetic models. Past approaches utilize data from a specific phase of flight (climb, level flight or descent), where one or more of the parameters are assumed to be known and estimate the remaining unknown parameters. This approach introduces bias/errors and also does not extend to scenarios where all of the above parameters are not known with sufficient accuracy. This paper is the first of its kind to propose a generalized framework for simultaneous estimation of all three critical APM parameters (thrust, drag, and mass). The proposed approach utilizes data from both the climb and descent phases and fits the ordinary differential equation for altitude in each phase using historical trajectory data available from radar tracks or ADS-B. The approach yields a set of optimized APM parameters that are best suited to fit each historical flight record. The methodology is applied on on sample flights from three different aircraft types, and the results demonstrate low fit error and consequently will yield a high level of prediction accuracy.

trajectory prediction, aircraft performance model,↗

NASA NTRSDate not supplied

An ODE-Fitting Approach to Estimate Critical Aircraft Performance Parameters for Trajectory Prediction

Ground-based decision support tools (DST) in air traffic management (ATM) typically perform trajectory prediction based on aircraft performance model (APM) parameters, but some or all of these parameters might not be readily available. In particular, the three critical parameters required for trajectory prediction are the thrust setting, drag coefficients, and takeoff weight of the aircraft. Unfortunately, these parameters are coupled and appear together in physics-based kinetic models. Past approaches utilize data from a specific phase of flight (climb, level flight or descent), where one or more of the parameters are assumed to be known and estimate the remaining unknown parameters. This approach introduces bias/errors and also does not extend to scenarios where all of the above parameters are not known with sufficient accuracy. This paper is the first of its kind to propose a generalized framework for simultaneous estimation of all three critical APM parameters (thrust, drag, and mass). The proposed approach utilizes data from both the climb and descent phases and fits the ordinary differential equation for altitude in each phase using historical trajectory data available from radar tracks or ADS-B. The approach yields a set of optimized APM parameters that are best suited to fit each historical flight record. The methodology is applied on on sample flights from three different aircraft types, and the results demonstrate low fit error and consequently will yield a high level of prediction accuracy.

trajectory prediction↗