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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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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Informing Disaster Response through Earth Observation: A User-Centric Model for Enhancing Disaster Response Using Geospatial Assets

Satellite observations can provide critical insights to building situational awareness and filling data gaps during disaster response. The National Aeronautics and Space Administration (NASA) Earth Science Division’s Disasters Program aims to advance Earth science data and information to support management decisions that prevent or mitigate the impacts of disasters. In support of this goal, NASA’s Disasters Response Coordination System (DRCS) employs a “One NASA” approach to coordinate and mobilize the Agency’s assets and expertise to provide geospatial information during disasters. The DRCS advances the utility of Earth observation information for supporting disaster response needs and builds skills in the emergency management and disaster response communities through improved coordination, engagement, and learning. The DRCS aims to support reduction of impact to lives and livelihoods by empowering communities to more effectively respond to disasters. The DRCS is organized across six NASA centers and managed by a project office located at NASA Langley, working in alignment with NASA Headquarters. The DRCS employs a user-centered framework that begins with requests from disaster responders (state, local, federal government and non-profits working at a national scale) and ends with after-action assessments that feed lessons learned and process improvements. This poster introduces the DRCS model and approach to expanding the use of Earth observations and geospatial information to support disaster response, share use cases for recent event activations working with decision-making organizations, and highlight initial lessons learned.

Remote Sensing↗
DOE OSTI2021

City Decision Analysis Resources and Tools

The City Decision Analysis at Any Scale Workshop was held February 17-18, 2021. This brochure was created to provide participants with a collection of tools and resources that support planning and implementation of clean energy goals for communities and businesses.

ACES↗
NASA NTRS2012

Cognitive Systems Modeling and Analysis of Command and Control Systems

Military operations, counter-terrorism operations and emergency response often oblige operators and commanders to operate within distributed organizations and systems for safe and effective mission accomplishment. Tactical commanders and operators frequently encounter violent threats and critical demands on cognitive capacity and reaction time. In the future they will make decisions in situations where operational and system characteristics are highly dynamic and non-linear, i.e. minor events, decisions or actions may have serious and irreversible consequences for the entire mission. Commanders and other decision makers must manage true real time properties at all levels; individual operators, stand-alone technical systems, higher-order integrated human-machine systems and joint operations forces alike. Coping with these conditions in performance assessment, system development and operational testing is a challenge for both practitioners and researchers. This paper reports on research from which the results led to a breakthrough: An integrated approach to information-centered systems analysis to support future command and control systems research development. This approach integrates several areas of research into a coherent framework, Action Control Theory (ACT). It comprises measurement techniques and methodological advances that facilitate a more accurate and deeper understanding of the operational environment, its agents, actors and effectors, generating new and updated models. This in turn generates theoretical advances. Some good examples of successful approaches are found in the research areas of cognitive systems engineering, systems theory, and psychophysiology, and in the fields of dynamic, distributed decision making and naturalistic decision making.

Norlander, Arne↗
DOE OSTI2024

Navigating Exascale Operational Data Analytics: From Inundation to Insight

In this paper, we address the challenges in achieving sustainable data-driven efficiency by providing a detailed exploration of the end-to-end operational data analytics (ODA) framework that evolved through two generations of supercomputer systems at the Oak Ridge Leadership Computing Facility (OLCF). This framework addresses large data streams ingested from heavily instrumented HPC environment that accumulates multi-terabytes per day. We outline the multifaceted data life cycle across HPC procurement, operations, and research & development, identifying key obstacles and design decisions that shape effective strategies in building and supporting data pipelines end-to-end. By sharing key insights and lessons learned from our experience, we offer recommendations for the HPC community on enabling sustainable operational data analytics and beyond. Our contributions aim to bridge the gap between potential and real benefits of operational data, guiding future efforts towards integrated and sustainable operational intelligence in high-performance computing environments.

Shin, Woong↗
DOE OSTI2023

LOCOMOTIVES - Comprehensive Impact and Cost Assessment Framework of Carbon Lowering Approaches for the US Rail Freight System

The goal of this project is to develop a tool to aid railroads and other stakeholders assess and approach the decarbonization of freight rail operations by identifying new, viable low-carbon energy storage and conversion systems for future locomotive systems and how they should be deployed on the existing US freight rail network. In the first quarter, the project focused on collecting data, establishing a simulation workflow, and engaging industry through the creation of the Industry Advisory Board (IAB). In the second quarter, the project focused on selecting fuel pathways and powertrain technologies, setting performance targets, conducting a techno-economic analyses, and developing the simulation framework that would serve as the backbone of the future toolhead. The third quarter involved developing an industry-oriented interactive dashboard powered by a five-step sequential framework, as well as holding industry advisory board meetings as per the initial technology-to-market plan. In the remaining project quarters, the NUFRIEND dashboard were fine-tuned with the help of IAB member feedback and in-depth scenario analyses were conducted to support the techno-economic analysis of energy sources. Additionally, dashboard documentation, project insights, and open-source code on GitHub were prepared and released. Throughout the project, the team completed testing and analysis of all model components, integrated all initial test scenarios, and conducted stakeholder engagement. Lower-carbon drop-in fuels can be deployed as admixtures and are considered uniform across the network at a desired penetration rate, while hydrogen and battery-electric technology deployment poses a more complex problem as they require significant investments to be made in the siting of refueling/charging facilities and the replacement of locomotive fleets. Thus, strategies for locating and sizing refueling/charging facilities on a railroad’s network to meet their energy demands were developed to inform deployment decisions. To address this challenge, the Northwestern University Freight Rail Infrastructure & Energy Network Decarbonization (NUFRIEND) framework presents a five-step sequential framework to select O-D paths, locate facilities, reroute flows, size facilities, and evaluate the deployment for alternative energy sources that require locomotive powertrains to be converted and new refueling infrastructure to be deployed. The NUFRIEND Framework is an industry-oriented tool for simulating the deployment of new energy technologies across the US freight rail network. The framework provides a comprehensive network-level optimization and scenario simulation tool for decarbonizing the freight rail sector, addressing the uncertainties surrounding technological developments by supporting sensitivity analyses for different operational and technological parameters through a transparent and flexible input module. It offers practical alternatives to diesel locomotives and can be applied for any railroad considering the specific network structure and freight demand, outputting evaluation metrics for the associated emissions and costs relative to diesel operations. A number of relevant simulation scenarios were run and analyzed for key insights on the value of different alternative technologies for freight rail decarbonization. The project developments and findings have been presented at numerous conferences and events.

08 HYDROGEN↗
DOE OSTI2026

Applying Particle Swarm Optimization and Extended Kalman Filtering to Model Kaplan Generation Dynamics for Hydropower Systems

Variable renewable generation is increasing the need for hydropower plants to provide fast and flexible grid support, which places new demands on plant-level dynamic models used for monitoring, control, and operational decision-making. This need is especially important for hydroelectric systems, where turbine and generator dynamics are strongly coupled, nonlinear, and time-varying, making accurate real-time representation difficult. To address this problem, this paper develops a digital twin (DT) framework for a synchronous generator–Kaplan turbine system using an explicit separation of slow turbine dynamics and fast generator dynamics. The turbine subsystem is represented by a six-coefficient model, whose parameters are identified offline using particle swarm optimization, while the generator subsystem is updated online through an extended Kalman filter for real-time state and parameter estimation. These models are integrated within a closed-loop simulation that includes a proportional–integral–derivative–double-derivative governor and excitation system, allowing the DT to track plant behavior under realistic operating conditions. Unlike prior studies that treat turbine and generator modeling separately or rely mainly on simulated inputs, the proposed framework is validated using real operational data from a hydropower plant. Results show that the DT reproduces terminal voltage, active power, and reactive power with a normalized root mean square error of approximately 5%. This hybrid offline–online formulation constitutes the main contribution of the work, providing an adaptive and practically deployable DT for hydropower systems with direct relevance to control improvement, performance monitoring, and grid-support applications under high renewable penetration.

13 HYDRO ENERGY↗
NASA NTRS2023

Advanced Information Systems Technology (AIST) Overview & Current and Recent Projects

"NASA's Advanced Information Systems Technology (AIST) Program identifies, develops, and supports adoption of software and information systems, as well as novel computer science technologies expected to be needed by the Earth Science Division in the 5-10-year timeframe. Overall, the AIST Program is focusing on advanced technologies and innovative concepts with three main objectives: O1. Enable new observation measurements and new observing systems design and operations through intelligent, timely, dynamic, and coordinated distributed sensing; O2. Enable agile science analyses that fully utilize the large amount of diverse observations using advanced analytic tools, visualizations, and computing environments, and that interact seamlessly with relevant observing systems; O3. Enable the development of integrated Earth Science frameworks that mirror the Earth with state-of-the-art models (Earth system models and others), timely and relevant observations, and analytic tools. This thrust will provide technology for enabling near- and long-term science and policy decisions (""""science decisions"""" including planning for the acquisition of new measurements; the development of new models or science analysis; the integration of Earth observations in novel ways; applications to inform choices, support decisions, and guide actions for societal benefit; etc.). AIST objectives aim at optimizing Earth Science mission return – NOS from an observation point of view and ACF/ESDT from an information analysis and utilization point of view. The assets and data accessed and utilized in these systems may come from NASA and non-NASA sources. "

Earth Science Remote Sensing; Information Systems↗
DOE OSTI2020

Evaluating long-term model-based scenarios of the energy system

Energy-economic models are used to provide science-based decision support in a variety of contexts. Analyses using these tools often involve defining a “reference” scenario, which serves as a counterfactual against which alternative scenarios are compared. Evaluating scenarios, including reference scenarios, is critical for establishing the credibility of the analyses these models support. We propose a framework for evaluating energy system scenarios which consists of three parts – a qualitative storyline, quantitative metrics, and evaluation criteria. We apply this framework to the reference scenario for GCAM-USA, a version of the global human-Earth system model GCAM (Global Change Assessment Model) with state-level detail in the United States, focusing on the evolution of the electric power sector. We develop new visual analytic tools to facilitate the evaluation of model outcomes in 51 sub-national regions, and demonstrate how scenario performance can be tracked and compared across four quantifications of the GCAM-USA reference scenario.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2014

An Integrated Reliability and Physics-Based Risk Modeling Approach for Assessing Human Spaceflight Systems

This paper presents an integrated reliability and physics-based risk modeling approach for assessing human spaceflight systems. The approach is demonstrated using an example, end-to-end risk assessment of a generic-crewed space transportation system during a reference mission to the International Space Station. The behavior of the system is modeled using analysis techniques from multiple disciplines in order to properly capture the dynamic time- and state- dependent consequences of failures encountered in different mission phases. We discuss how to combine traditional reliability analyses with Monte Carlo simulation methods and physics-based engineering models to produce loss-of- mission and loss-of-crew risk estimates supporting risk-based decision-making and requirement verification. This approach facilitates risk-informed design by providing more realistic representation of system failures and interactions; identifying key risk-driving sensitivities, dependencies, and assumptions; and tracking multiple figures of merit within a single, responsive assessment framework that can readily incorporate evolving design information throughout system development.

Risk assessment↗
NASA NTRS2021

Integrating Inland and Coastal Water Quality Data for Actionable Knowledge

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making.

Ghada Y.H. El Serafy↗
NASA NTRSDate not supplied

Leveraging Existing Satellite Precipitation Missions for AOS Applications Development

The Atmosphere Observing System (AOS) seeks to explore fundamental questions of how interconnections between aerosols, clouds and precipitation impact our weather and climate, addressing real-world challenges to benefit society. AOS will provide key information to enhance the communities’ ability to improve weather and air quality forecasting today, seasonal to sub-seasonal changes in the near future, and societal challenges resulting from climate change in the decades to come. A fundamental component of the AOS mission is ensuring that applications are considered to the greatest extent possible in mission design.As a result, the Applications Impact Team (AIT) was implemented to address this objective.The overarching goal of the AIT is to help improve the capacity for transitioning science to applications to make it possible to more quickly and effectively inform decisions that will directly benefit society. We seek to maximize AOS benefit to impact decisions through early engagement in the mission development phase in order to prepare stakeholders to apply observations as soon as AOS mission data becomes available.To support these efforts, we leverage existing mission applications activities and initiatives, such as NASA’s GPM and TROPICS missions, to form a framework to enhance precipitation applications for AOS. Engaging with existing missions helps identify and understand data needs, gaps and opportunities for current and future stakeholders, determine what precipitation data products are of highest value and use, and helps connect stakeholders with current mission data that can serve as AOS proxy data, among others. In this presentation, we provide an overview of AOS AIT activities and initiatives and highlight how existing satellite precipitation missions and their applications activities can play a critical role in AOS applications development during mission design.

Andrea Portier↗
DOE OSTI2024

Application of quantitative risk assessment to address stakeholder questions in geologic carbon storage

Ambitious international greenhouse gas emissions reduction targets demand a rapid transformation to a low-carbon economy. This transformation includes the accelerated adoption of carbon dioxide (CO2) capture and storage (CCS) technology. However, as with any large-scale engineering enterprise, the widespread commercial-scale deployment of geologic carbon storage (GCS) raises important questions about technology and cost-effectiveness, safety, environmental risk, and long-term liability. Effectively assessing and managing risks and liability associated with GCS projects is a key technical need throughout the project life cycle-from site selection and permitting to monitoring design, operational risk management, and post-operational site closure. This presentation highlights recent advancements in tools for quantitative risk assessment, being developed by the National Risk Assessment Partnership (NRAP). NRAP is a multi-year, multinational laboratory research collaboration sponsored by the U.S. Department of Energy's Office of Fossil Energy and Carbon Management. Our focus will be on these tools' applications in addressing critical stakeholder questions related to supporting permitting to ensure secure and environmentally protective storage; designing effective and efficient monitoring plans; evaluating the effectiveness of remedial actions and risk management alternatives; and informing liability assessment and investment decisions. This paper will detail the key functionality of NRAP’s Open-Source Integrated Assessment Model (NRAP-Open-IAM), a computational framework for assessing leakage risk and containment assurance. This model features streamlined workflows for calculating leakage risk profiles, delineating risk-based area of review, and assessing contingency plans and post-injection site care requirements. ORION is an open-source, observation-based ensemble forecasting toolkit to help operators assess the seismic hazard at a carbon storage site. The State of Stress Analysis Tool (SOSAT), designed to assess subsurface stress conditions and evaluate geomechanical risk resulting from CO2 injection in an area of interest will also be presented. We will also introduce a prototype model to evaluate storage project costs and liability associated with risk management. The Technoeconomic and Liability Evaluation for Storage (TALES) model uses results from forecasts of leakage and induced seismicity risk to estimate the lifecycle cost of managing risk. Finally, a preliminary example of how the NRAP Risk-based Adaptive Monitoring Plan (RAMP) tool can be used to design efficient and effective site monitoring plans and estimate the detectability of fluid leakage will be provided. The relevance of these tools for addressing key stakeholder questions amidst uncertainty will be emphasized.

decision support↗
NASA NTRS1993

Reactive system verification case study: Fault-tolerant transputer communication

A reactive program is one which engages in an ongoing interaction with its environment. A system which is controlled by an embedded reactive program is called a reactive system. Examples of reactive systems are aircraft flight management systems, bank automatic teller machine (ATM) networks, airline reservation systems, and computer operating systems. Reactive systems are often naturally modeled (for logical design purposes) as a composition of autonomous processes which progress concurrently and which communicate to share information and/or to coordinate activities. Formal (i.e., mathematical) frameworks for system verification are tools used to increase the users' confidence that a system design satisfies its specification. A framework for reactive system verification includes formal languages for system modeling and for behavior specification and decision procedures and/or proof-systems for verifying that the system model satisfies the system specifications. Using the Ostroff framework for reactive system verification, an approach to achieving fault-tolerant communication between transputers was shown to be effective. The key components of the design, the decoupler processes, may be viewed as discrete-event-controllers introduced to constrain system behavior such that system specifications are satisfied. The Ostroff framework was also effective. The expressiveness of the modeling language permitted construction of a faithful model of the transputer network. The relevant specifications were readily expressed in the specification language. The set of decision procedures provided was adequate to verify the specifications of interest. The need for improved support for system behavior visualization is emphasized.

Crane, D. Francis↗
NASA NTRS1990

Supplemental knowledge acquisition through external product interface for CLIPS

Traditionally, the acquisition of knowledge for expert systems consisted of the interview process with the domain or subject matter expert (SME), observation of domain environment, and information gathering and research which constituted a direct form of knowledge acquisition (KA). The knowledge engineer would be responsible for accumulating pertinent information and/or knowledge from the SME(s) for input into the appropriate expert system development tool. The direct KA process may (or may not) have included forms of data or documentation to incorporate from the SME's surroundings. The differentiation between direct KA and supplemental KA (indirect) would be the difference in the use of data. In acquiring supplemental knowledge, the knowledge engineer would access other types of evidence (manuals, documents, data files, spreadsheets, etc.) that would support the reasoning or premises of the SME. When an expert makes a decision in a particular task, one tool that may have been used to justify a recommendation, would have been a spreadsheet total or column figure. Locating specific decision points from that data within the SME's framework would constitute supplemental KA. Data used for a specific purpose in one system or environment would be used as supplemental knowledge for another, specifically a CLIPS project.

Saito, Tim↗
NASA NTRS2009

Data Mining and Complex Problems: Case Study in Composite Materials

Data mining is defined as the discovery of useful, possibly unexpected, patterns and relationships in data using statistical and non-statistical techniques in order to develop schemes for decision and policy making. Data mining can be used to discover the sources and causes of problems in complex systems. In addition, data mining can support simulation strategies by finding the different constants and parameters to be used in the development of simulation models. This paper introduces a framework for data mining and its application to complex problems. To further explain some of the concepts outlined in this paper, the potential application to the NASA Shuttle Reinforced Carbon-Carbon structures and genetic programming is used as an illustration.

Rabelo, Luis↗
DOE OSTI2024

A Tool to Incorporate Non-Energy Impacts in Energy Efficiency Investment Decision Making for Firms

Energy efficiency is a key demand-side strategy for sustainability, recently identified by the United States Department of Energy as a pillar of industrial decarbonization. The increased focus on decarbonization and the requirement for efficiency to enable electrification, another decarbonization pillar, due to the spark spread between natural gas and electricity prices, make energy efficiency increasingly relevant. Still, industries face challenges in adopting energy efficiency measures. Researchers have long found a gap in adoption of even those measures with a profitable net present value, attributed to lack of strategic value among other barriers (see for rigorous exploration and taxonomy). One solution to facilitate energy efficiency projects is the inclusion of non-energy impacts, as this has been shown to double potential deployment of such projects at system level. Energy efficiency can provide valuable benefits outside of simple operating cost reductions, from decreased pollution to enhanced productivity. The inclusion of these benefits in decision making assessments faces hurdles due to inconsistency of ancillary benefits across projects, difficulties in quantifying impacts and the need for additional measurement to quantify them. The decision-making tools to support this have been designed primarily for the European context. We begin with a stakeholder engagement process to better characterize the U.S. decision making process surrounding adoption of energy efficiency investments. Characterization of non-energy impacts has developed substantially over recent decades. Cagno et al. provided a framework for studying the applicability of these impacts to energy efficiency projects, listing 120 key performance indicators focused mainly on reductions of costs/harms. Other researchers have included impacts on the strategic and revenue side that can be merged into this framework as well. We seek a tractable set of impacts that can be included in a decision-making tool in the US, and as such are well suited to US industry, management and decision making processes. We also seek to understand how to best quantify or characterize these impacts. This work will demonstrate the results of a survey conducted among US manufacturing industry decision makers to assess the decision making landscape of stakeholders as well as the most relevant performance indicators for energy efficiency projects.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION,↗
DOE OSTI2021

Framework for optimization of long-term, multi-period investment planning of integrated urban energy systems

In order to achieve stringent greenhouse gas emission reductions, a transition of our entire energy system from fossil to renewable resources needs to be designed. Such an energy transition brings two main challenges: most renewables generate variable electric energy, yet most demand is currently not electric (carrier mismatch) and does not always manifest at the same time as supply (temporal mismatch). Integrating multiple energy infrastructures can address both challenges by using the synergy between different energy carriers; building on existing infrastructure, while allowing a robust and flexible integration of the new. This paper proposes an optimization framework for long-term, multi-period investment planning of urban energy systems in an integrated manner. We formulate it as a mixed-integer linear program, combining a capacitated facility location with a multi-dimensional, capacitated network design problem. It includes generation and network expansion planning as well as interconnections between networks and storage infrastructure for each energy system. It can incorporate pathway effects like techno-economic developments, policy measures, and weather variations. The intended use is to support urban decision makers with long-term investment planning, though it can be tailored to fit other geographical or temporal scales. We demonstrate the model using two cases based on an average city in The Netherlands, which wants to reduce its CO 2 -emissions with 95% by 2050. In the first case, we include explicit carbon-emission constraints to study the effects of the carrier mismatch. In the second case, we implement interannual weather variations to analyze the temporal mismatch. The results give valuable insights into the energy transition design strategy for urban decision makers. They also show the future potential, as well as the computational challenges of the optimization framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2021

FRESCO: A Framework for Spacecraft Systems Autonomy

Achieving the science exploration and defense goals of the following decades will require flight systems capable of operations with limited operator contact, system mode changes and retasking based on sensor data, and complex robotic operations. To support these capabilities, increasingly autonomous flight systems are required that can perform dedicated mission functions, e.g. payload targeting and communications, and system-level functions, e.g. planning and goal monitoring. Architecting an autonomous system requires a well-reasoned, self-consistent framework to avoid \textit{ad hoc} design choices that will introduce complexity and risk. The Framework for Robust Execution and Scheduling of Commands On-Board, FRESCO, is the result of lessons learned in developing a software architecture to enable autonomous solar system exploration. FRESCO generalizes this work to offer a modular, software-agnostic approach to developing verifiable architecture for autonomous space systems. FRESCO specifies guiding principles, functions, interfaces, and interactions from which mission-specific autonomous control architectures can be derived. FRESCO is a principled framework relying on explicit, state-based goal definitions, centralized management of state knowledge, clearly separated control boundaries, and hierarchical reasoning. Using components from FRESCO reference architecture, an autonomous decision-making architecture can be designed for spacecraft which can then be mapped to flight software architecture. FRESCO is flexibly defined to enable autonomous control of flight systems built using extensive software and hardware heritage. Finally, FRESCO-derived architectures support a spectrum of operator/spacecraft interactions, ranging from traditional commanding to goal-driven commanding with the ability to change mission goals autonomously. FRESCO has been used in defining the autonomy architectures for the ASTERIA mission and have been demonstrated in laboratory and software simulation for small body rendezvous and in-space servicing missions.

Kolcio, Ksenia↗