Real-Time Resilience of Energy Systems [Slides]
This presentation summarizes the real-time resilience of energy systems (FY20-FY22) LDRD project.
24 POWER TRANSMISSION AND DISTRIBUTION↗
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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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Analytical small-signal stability analysis of low-inertia power system frequency response considering secondary frequency regulation
Modern power systems are increasingly vulnerable to frequency instability as inverter-based resources (IBRs) replace synchronous machines and reduce system rotational inertia. Existing small-signal frequency stability assessment methods are either computationally intensive, relying on simulation-driven approaches, or lack analytical stability regions that explicitly account for secondary frequency response (SFR). This paper introduces new analytical frameworks that enable evaluation small-signal frequency stability while explicitly incorporating tunable IBR and SFR parameters. Using Kharitonov’s theorem with an overbounding approach, explicit small-signal stability criteria are derived. In addition, based on Białas’ criterion and Hurwitz matrix, analytical stability regions are established to reveal feasible design spaces for SFR and IBR parameters tuning. Extensive Matlab/Simulink-based simulations validate the accuracy and computational efficiency of the proposed methods, demonstrating that coordinated tuning of SFR and IBR parameters can substantially enhance system resilience. By bridging analytical rigor with practical tunability, this work provides an analytical framework for assessing small-signal frequency stability in low-inertia grids, supporting the real-time, scalable, and resilient operation of sustainable power systems.
14 SOLAR ENERGY↗
Designing Graceful Degradation into Complex Systems: The Interaction Between Causes of Degradation and the Association with Degradation Prevention and Recovery
System resilience is critical to safety in air traffic control. An important element of maintaining resilience is the ability of systems to degrade gracefully. Of the available graceful degradation research, a majority of studies have focused primarily on technological causes of degradation only, limiting an ecologically valid understanding of the causes of degradation in air traffic control, and the preventative and mitigative strategies that enable graceful degradation. The current study aimed to address this research gap by investigating causes of degradation in air traffic control across the broad categories of technology, the environment, and the human operator, and the potential interactions between these causes. 12 retired controllers participated in semi-structured interviews focused on previous experience of causes of degradation and mitigation strategies. Findings provide an understanding of causation of degradation in air traffic control, and the prevention and mitigation strategies that moderate the relationship between cause and system effect. Findings confirmed that causes appear to interact to create compound, multiple effects on overall system performance. Findings also revealed prevention and mitigation strategies utilized to moderate the effect of the cause on the system. In order to gain an ecologically valid understanding of the causes of degradation, and effective prevention or mitigation strategies, causes from multiple categories, and the interactions between them, must be identified. Findings have implications for designers of future air traffic control systems to ensure the ability of the system to gracefully degrade, as well as risk assessment and system validation processes.
human performance↗
Designing Graceful Degradation into Complex Systems: Identification of Causes of Degradation, Interactions, and Mitigation of Degradation in Air Traffic Control
System resilience is critical to safety in air traffic control. An important element of maintaining resilience is the ability of systems to degrade gracefully. Of the available graceful degradation research, a majority of studies have focused primarily on technological causes of degradation only, limiting an ecologically valid understanding of the causes of degradation in air traffic control, and the preventative and mitigative strategies that enable graceful degradation. The current study aimed to address this research gap by investigating causes of degradation in air traffic control across the broad categories of technology, the environment, and the human operator, and the potential interactions between these causes. 12 retired controllers participated in semi-structured interviews focused on previous experience of causes of degradation and mitigation strategies. Findings provide an understanding of causation of degradation in air traffic control, and the prevention and mitigation strategies that moderate the relationship between cause and system effect. Findings confirmed that causes appear to interact to create compound, multiple effects on overall system performance. Findings also revealed prevention and mitigation strategies utilized to moderate the effect of the cause on the system. In order to gain an ecologically valid understanding of the causes of degradation, and effective prevention or mitigation strategies, causes from multiple categories, and the interactions between them, must be identified. Findings have implications for designers of future air traffic control systems to ensure the ability of the system to gracefully degrade, as well as risk assessment and system validation processes.
human performance↗
Designing Graceful Degradation into Complex Systems: The Interaction Between Causes of Degradation and the Association with Degradation Prevention and Recovery
System resilience is critical to safety in air traffic control. An important element of maintaining resilience is the ability of systems to degrade gracefully. Of the available graceful degradation research, a majority of studies have focused primarily on technological causes of degradation only, limiting an ecologically valid understanding of the causes of degradation in air traffic control, and the preventative and mitigative strategies that enable graceful degradation. The current study aimed to address this research gap by investigating causes of degradation in air traffic control (ATC) across the broad categories of technology, the environment, and the human operator, and the potential interactions between these causes. 12 retired controllers (ATCOs - Air Traffic Control Officers) participated in semi-structured interviews focused on previous experience of causes of degradation and mitigation strategies. Findings provide an understanding of causation of degradation in air traffic control, and the prevention and mitigation strategies that moderate the relationship between cause and system effect. Findings confirmed that causes appear to interact to create compound, multiple effects on overall system performance. Findings also revealed prevention and mitigation strategies utilized to moderate the effect of the cause on the system. In order to gain an ecologically valid understanding of the causes of degradation, and effective prevention or mitigation strategies, causes from multiple categories, and the interactions between them, must be identified. Findings have implications for designers of future air traffic control systems to ensure the ability of the system to gracefully degrade, as well as risk assessment and system validation processes.
Graceful Degradation↗
Resilient Inverter-Driven Black Start with Collective Parallel Grid-Forming Operation
As modern power systems are experiencing exceptional changes with increasing penetrations of inverter-based resources (IBRs), system restoration using IBRs has received attention. Using local grid-forming (GFM) assets near consumers, engineered to establish grid voltages in the absence of a stiff grid, i.e., bottom-up restoration, a distribution system could obtain high system resilience by not relying on the bulk power system restoration, which requires significant human intervention and procedure. This paper studies the technical feasibility of the novel approach with detailed electromagnetic transient (EMT) simulations. To thoroughly evaluate the potential of GFM inverters and the technical challenges in IBR-driven black start, a detailed three-phase inverter model is developed, including negative-sequence control for voltage balance and a phase-by- phase current limiter to sustain momentary overloading during the black start. To examine dynamic aspects of the black-start process, the EMT simulation also models transformer and motor dynamics to emulate their inrush and startup behaviors as well as network dynamics. In addition, active involvement of grid- following distributed energy resources is also studied to facilitate the black-start process. By allowing multiple GFM inverters to collectively black start without leader-follower coordination, we demonstrate that a system can achieve high resilience even with a fraction of assets lost. Two test cases of inverter-driven black start, using two and one GFM inverters, respectively, for a heavily unbalanced 2-MVA distribution feeder are demonstrated. Takeaways for further study and field deployment are provided.
grid-forming inverter↗
Data-driven Community-centered Resilient Assessment and Planning Toolkit for Nexus of Energy and Water (DCRAPT-NEW)
Urban areas, including Detroit and Pittsburgh, have suffered significant dual outages of the electrical and water infrastructure in the past decade due, in part, to the increasing number of extreme weather events. With increasing temperatures and rainfall intensity, these regions need to prepare for increasing extreme events through community-based energy and water resilience analysis, planning, and enhancement. This project developed a suite of open-source, open-access, community-centered, data-driven assessment and distributed energy resource (DER) and planning tools for energy and water resilience enhancement in urban areas. Through establishing a multi-level community awareness and engagement mechanism and a comprehensive collection of power outage and flooding data, an innovative group of community energy and water resilience assessment and planning tools have been developed for a wide range of users with differing and variable sets of data available to them. The developed tools include (1) DOE EAGLE-I data-driven, deep-learning assisted resilience assessment and DER planning tools at the county level with socioeconomic factors incorporated; (2) Utility annual power outage data-driven tools for long term resilience assessment and DER planning and 15-min power outage data-driven tools for short term resilience assessment and planning; (3) Detailed engineering tools for energy and water systems resilience assessment and planning when the system topology and component fragility curves are available; (4) Alternative Resiliency Metric Calculation that extracts and separates outage and restoration processes; and (5) Co-optimization tools that evaluate the resilience of the power and sewage system and allow users to conduct joint planning with energy and wastewater systems. The developed tools provide planners, decision-makers, and stakeholders with powerful capabilities to systematically evaluate system/community resilience and optimal and actionable guidance for enhancing resilience while prioritizing DER investments. The tools have been used and validated in Detroit and Pittsburgh and can be used in other areas of the nation. In addition, this project will (1) advance the knowledge and applications of machine-learning methods in analyzing and fusing different layers of information and generating meaningful data points such as generating rare weather events; (2) significantly improve the energy and water resilience of the identified communities in Detroit and Pittsburgh and prepare for more frequent and severe weather conditions; (3) help communities assess extreme weather event impacts and address short-term and long-term resilience-related issues The developed tools have been made public via GitHub and demonstrated to community stakeholders and utility companies via the two annual workshops and numerous community engagement meetings. The project outcomes are also disseminated through publications in various journals and conference proceedings, and presentations at top conferences.
13 HYDRO ENERGY↗
Going beyond reliability to robustness and resilience in space systems
The words reliability, robustness, and resilience, are often used interchangeably to describe tough and dependable systems but the distinctions between them suggest how to design more serviceable space systems. Reliability is simply the quality of consistently performing well. A system that dependably meets its design requirements in the specified environments is reliable. The designers may not consider themselves responsible for failures under unanticipated conditions. Robustness is the capability of performing without failure under a wide range of conditions, which can go beyond the expected range to include possible off-nominal conditions. Resilience is the ability to recover from or adapt to damaging events, such as failures, accidents, external disruptions, and repurposing. Such changes are usually unanticipated. They often invalidate the usual operating assumptions and cause system failure. Reliability, robustness, and resilience describe dependable performance under increasingly difficult conditions, first the specified environment, then a wider possible environment, and finally unanticipated damaging events. These three are increasingly desirable and increasingly difficult to achieve. Engineering for resilience would design systems that can ignore or repair failures, survive accidents, and recover from disruptions. Increasing the resilience of space systems, the ability to perform after unanticipated events, would greatly increase space crew safety. Improving reliability and robustness can be done by dealing with known sources of problems, but improving resilience requires implementing a general approach to reducing the impact of unknown future events. Two contrasting approaches are reducing system complexity and adding supervisory control. The need for resilience has been claimed for decades but little has been accomplished. Systems designers assume that they understand requirements, technologies, designs, architectures, integration, testing, operations, and environments. The potential problems of changes, failures, accidents, unknown environments, and unknown unknowns are ignored. Systems designers are typically overconfident and ignore the need for robustness and resilience.
Harry W Jones↗
Resilient Inverter-Driven Black Start with Collective Parallel Grid-Forming Operation: Preprint
As the modern power systems are experiencing exceptional changes with increasing penetration of inverter-based resources (IBRs), system restoration using IBRs has received attention. Using local grid-forming (GFM) assets near consumers, engineered to establish grid voltages in the absence of a stiff grid, i.e., bottom-up restoration, a distribution system would obtain high system resilience, by not relying on the bulk power system restoration requiring significant human intervention and procedure to restore. This paper studies the technical feasibility of the novel approach with detailed electromagnetic transient (EMT) simulations. To thoroughly evaluate the potential of GFM inverters and technical challenges in IBR-driven black start, a detailed three-phase inverter model is developed, including negative-sequence control for voltage balance and a phase-by-phase current limiter to sustain momentary overloading during the black start. To examine dynamic aspects of the black start process, the EMT simulation also models transformer and motor dynamics to emulate their inrush and start-up behaviors as well as network dynamics. In addition, active involvement of grid-following distributed energy resources (DER) is also studied to facilitate the black start process. It is shown that, by allowing multiple GFM inverters to collectively black start without master-slave coordination, a system can achieve high resilience even with a fraction of assets lost. Two test cases of inverter-driven black start using two and one GFM inverters, respectively, for a heavily unbalanced 2-MVA distribution feeder are demonstrated. Takeaways for further study and field deployment are provided.
grid-forming inverter↗
Accelerating Resilience of the Community through Holistic Engagement and use of Renewables (ARCHER) Planning Framework
The primary objective of the Accelerating Resilience of the Community through Holistic Engagement and Use of Renewables (ARCHER) initiative was to identify and incorporate the unique variations in energy burden, social vulnerability, living conditions, and access to essential services that differ across communities. By accounting for these localized factors—down to the neighborhood level—the project supports more targeted and effective investments in community resilience. The framework seeks to establish practical planning guidance, methods, and performance measures for community energy resilience, integrate community-level and electric utility system resilience planning, and assess its effectiveness through comparison with conventional and operational planning approaches. A key component of the project was its data exchange platform, which is used to evaluate and demonstrate the tools, methodologies, and planning approaches developed through ARCHER. This open-source platform enables developers and vendors of distribution and outage management systems to build upon the research by incorporating its concepts into their own tools and workflows. This capability is enabled by the transparent availability of data, functional requirements, and the underlying information model. The project yielded several important insights. First, meaningful engagement with communities is essential to achieving comprehensive resilience outcomes. Second, resilience planning is most effective when electric grid considerations and broader community needs are addressed in a coordinated manner. Third, the use of platforms that allow for real-time input from communities can enhance utility responsiveness during restoration activities. Fourth, a structured and systematic planning approach can successfully translate ARCHER concepts into practice. Fifth, the development of an integrated metric that reflects both grid performance and community impacts provides a more holistic basis for evaluating resilience. Finally, incorporating community engagement and equity considerations into grid operations is critical, particularly during severe weather events that result in extended outages.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Invite the Chaos Monkey: Test Effect Payloads for Tuning ICS Incident Response
In 2010, when Netflix moved their operational systems to the cloud, they developed a pseudo-random mechanism to cause deliberate outages and failures of key system elements to ensure that automated systems for detecting and remediating outages were mature and fully functional. This Chaos Monkey spawned an entire Simian Army designed to ensure system resilience and enable failure response before issues became outages. Through the DARPA RADICS program, Provatech, INL, and other researchers have developed a mechanism for creating controllable failures via Test Effect Payloads (TEPs) for Industrial Control Systems in order to enable an asset owner to tune network and process sensor technology, increase the effectiveness of incident response and remediation, and highlight areas where resilient technology could bring benefit to Operational Technology. This talk will explore what a TEP is, how to create a TEP, and suggest what a systematic system resilience plan using TEP’s might look like. We will also discuss how regulated entities might employ TEP’s in a compliant resilience strategy.
42 ENGINEERING↗
Operational Resilience of Nuclear-Renewable Integrated-Energy Microgrids
The increasing prevalence and severity of wildfires, severe storms, and cyberattacks is driving the introduction of numerous microgrids to improve resilience locally. While distributed energy resources (DERs), such as small-scale wind and solar photovoltaics with storage, will be major components in future microgrids, today, the majority of microgrids are backed up with fossil-fuel-based generators. Small modular reactors (SMRs) can form synergistic mix with DERs due to their ability to provide baseload and flexible power. The heat produced by SMRs can also fulfill the heating needs of microgrid consumers. This paper discusses an operational scheme based on distributed control of flexible power assets to strengthen the operational resilience of SMR-DER integrated-energy microgrids. A framework is developed to assess the operational resilience of SMR-DER microgrids in terms of system adaptive real-power capacity quantified as a response area metric (RAM). Month-long simulation results are shown with a microgrid developed in a modified Institute of Electrical and Electronics Engineers (IEEE)-30 bus system. The RAM values calculated along the operational simulation reflect the system resilience in real time and can be used to supervise the microgrid operation and reactor’s autonomous control.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions
Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.
J Lemery↗
Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions
Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.
Medical Operations↗
Frequency Resilience Enhancement for Power Systems with High Penetration of Grid-Forming Inverters
Abstract—Grid following inverter-based renewable generation has replaced conventional generation in recent years, resulting in lower system inertia. The frequency resilience in such a lower inertia system is critical for emergency mitigation. In this paper, we propose a resilience metric based on frequency recovery to quantitatively represent system resilience in terms of the rate of change of frequency. Application of grid-forming converters provides a means to improve system resilience by providing virtual inertia. An under-frequency load shedding strategy is applied to further support frequency recovery in cases with high penetration of grid-forming inverters. Case studies are designed and performed in a modified IEEE 9-bus test system using the PSCAD/EMTDC platform. Simulation results demonstrate the validity of the proposed resilience metric and the effectiveness of the strategy for reducing frequency excursion in inverter-based power systems. citation: J. Gui, H. Lei, T. R. McJunkin and B. K. Johnson, "Frequency Resilience Enhancement for Power Systems with High Penetration of Grid-Forming Inverters," 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 2023, pp. 1-5, doi: 10.1109/ISGT51731.2023.10066357.
14 SOLAR ENERGY↗
Practical Implementation of GPU-based Computing at the Grid Edge for Resilience Scenarios
This paper presents a practical implementation of GPU-accelerated computing at the grid edge to enhance power system resilience through next-generation smart meters. Advanced Metering Infrastructure (AMI) systems rely predominantly on centralized processing architectures, which limit real-time response capabilities during grid disturbances. This work proposes the integration of GPU-enabled computational platforms directly within smart meter to enable local execution support for power system analytics, fault detection algorithms, and optimization routines. The proposed framework uses the Julia programming language to leverage highperformance parallel computing capabilities while maintaining code portability and development efficiency. We use two experimental scenarios to benchmark the computational feasibility of this approach: sparse linear system solutions representative of power flow analyses, and multi-stage production cost simulations incorporating unit commitment and economic dispatch operations. Results demonstrate that computationally intensive power system algorithms, such as those supporting resilience scenario calculations, can be effectively executed at the distribution edge using commercially available embedded GPU hardware. Keywords—GPU acceleration, edge computing, smart meters, grid resilience, AMI, resilience.
De Souza, Reubun [School of Electrical Engineering↗
Novel Observing Strategies (NOS) and Earth System Digital Twins (ESDT) for Disaster Resilience
"Earth systems have now been observed continuously for more than 50 years, not only from space but also from airplanes, balloons and in-situ sensors. With the addition of commercial remote sensing providers, many more Internet-of-Things sensors and new NASA and international observatories being planned, these incredible amounts of data will soon be augmented by even larger amounts of diverse data and therefore will become more and more difficult to access, integrate, understand and utilize. At the same time, because of climate change and its impacts, in order to predict, manage, and mitigate the effects of extreme science events and disasters, the information produced by all of this data needs to be optimized, organized and analyzed in such a way that it can be utilized by traditional as well as many new non-traditional users. This calls for the development of observing systems that are agile, coordinated and responsive to events of interest, and for information systems to be dynamic, interactive and fast. With these objectives in mind, the Advanced Information Systems Technology (AIST) Program has been developing technologies that will allow for the development of Novel Observing Strategies (NOS) in a distributed, coordinated fashion (i.e., similar to an “Internet-of-Earth-Things”), as well as for the development of novel information systems or Earth System Digital Twins (ESDT) that will build a digital replica of the past and current states of the Earth systems, will derive forecasts of future states under nominal assumptions and will also offer the capability to investigate many hypothetical evolution scenarios under varying impact assumptions. Both NOS and ESDT will be facilitated by the unprecedented advances of Artificial Intelligence technologies, especially Machine Learning (ML), for extracting relevant information from these large amounts of data, for fusing and assimilating diverse data together and for running complex models faster. Together NOS and ESDT will help build the capabilities needed to anticipate, predict and mitigate the effects of future disasters."
Earth Science Remote Sensing; Information Systems↗
Autonomous System Technologies for Resilient Airspace Operations
Increasing autonomous systems within the aircraft cockpit begins with an effort to understand what autonomy is and developing the technology that encompasses it. Autonomy allows an agent, human or machine, to act independently within a circumscribed set of goals; delegating responsibility to the agent(s) to achieve overall system objective(s). Increasingly Autonomous Systems (IAS) are the highly sophisticated progression of current automated systems toward full autonomy. Working in concert with humans, these types of technologies are expected to improve the safety, reliability, costs, and operational efficiency of aviation. IAS implementation is imminent, which makes the development and the proper performance of such technologies, with respect to cockpit operation efficiency, the management of air traffic and data communication information, vital. A prototype IAS agent that attempts to optimize the identification and distribution of "relevant" air traffic data to be utilized by human crews during complex airspace operations has been developed.
Houston, Vincent E.↗