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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Graph theory and nighttime imagery based microgrid design

Reducing the duration and frequency of blackouts in remote communities poses an engineering challenge for grid operators. Outage effects can also be mitigated locally through microgrids. This paper develops a systematic procedure to account for these challenges by creating microgrids prioritizing high value assets within vulnerable communities. Nighttime satellite imagery is used to identify vulnerable communities. Using an asset classification and rating system, multi-asset clusters within these communities are prioritized. Infrastructure data, geographic information systems, satellite imagery, and spectral clustering are used to form and rank microgrid candidates. A microgrid sizing algorithm is included to guide through the microgrid design process. Finally, an application of the methodology is presented using real event, location, and asset data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Vulnerability Detection Methodology for a Digital Signal Processor Micro-Controller at Edge Level Distributed Energy Resource Controller

Integration of renewable energy and energy storage based Distributed Energy Resource (DER) assets to the grid has seen an upsurge over the past few decades. Traditionally, energy would be generated at a thermal or nuclear power plant and then transmitted over long distance through the transmission grid and then supply to the customers at the distribution grid. Modern renewable energy based DER assets have brought energy production at the edge of distribution grid, which has made the grid vulnerable to cyber intrusion as the power flow controllers for such DER assets are now distributed across the grid from distribution level to transmission level. This paper discusses the vulnerability posed by such power flow controllers of DER assets and demonstrates a detection methodology for unauthorized access and manipulation of system configuration for Digital Signal Processor based Microcontrollers. Experimental results proving efficacy of the methodology have been shown in paper.

Bhowmik, Pankaj↗
DOE OSTI2020

A Framework for Evaluating the Resilience Contribution of Solar PV and Battery Storage on the Grid

Motivated by decreased cost and climate change concerns, penetration of solar photovoltaic (PV) and battery energy storage has been continually increasing. The variability in solar generation assets has led to many challenges for utilities and researchers. Therefore, there is increasing interest in the area of resilience of the grid and contribution from its assets. This paper describes a proposed framework for evaluating the resilience contribution of solar generation and battery storage. The metric provides a quantifiable adaptive capacity measure with uncertainty for the contribution assets proved to the bulk grid. A case study using long and short-term solar generation, with and without battery storage, demonstrates the framework and provides useful insight to the resilience solar and battery storage assets contribute.

14 SOLAR ENERGY↗
DOE OSTI2024

Digital Analytics, Causal Knowledge Acquisition and Reasoning for Technical Language Processing

Complex engineering systems such as nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) data elements that contain information on the status of components, assets, and systems. Some of this information is textual in form and can be found in documents such as incident reports (IRs) and work orders (WOs). Analyses of textual data in current NPPs-using natural language processing (NLP) methods-have been expanded over the last decade, and it is only recently that the true potential of such analyses has emerged. So far, applications of NLP methods have mostly been limited to classification and prediction, the goal being to identify the nature of the textual element (e.g., safety or non-safety related). Here, we target a more complex problem: automatically extracting knowledge from a textual element in order to assist system engineers in conducting system health assessments. Knowledge extraction is a very broad concept, and its definition may vary depending on the application context. Our methods are a blend of both rule-based and machine learning (ML) algorithms. For our purposes, knowledge extraction means identifying the systems or assets mentioned in a given textual element, as well as the type of event described (e.g., component failure or maintenance activity). In addition, we want to capture details such as measured quantities and the temporal/cause-effect relations between events. In this tool, we also demonstrate how textual data elements are preprocessed in order to handle typos, acronyms, and abbreviations. One main feature of these methods is that they are not based solely on data, but are in fact model-based. In other words, they also rely on MBSE models that are designed to capture-from a functional point of view-the architecture of the systems/assets under consideration. The main purpose of such models is to digitally emulate system engineers' knowledge of system and asset architecture and to identify dependencies among systems, assets, and components. Provided these models, analyses of textual and numeric ER data can be performed by first identifying the OPM model elements to which the ER data elements are referring. The relationships between ER data elements are then identified by checking for any temporal or logical dependencies.

Mandelli, Diego [Idaho National Laboratory (INL), ↗
DOE OSTI2020

Accobeam - Acoustic Collimated Beam

Researchers at Los Alamos have developed a collimated acoustic imaging technology for non-destructive characterization of highly attenuating media, such as concrete, rocks, mud, and metal casings. The technology is used to investigate structural defects that could compromise important assets such as well boreholes, building structures and sensitive materials like explosives. Current acoustic methods are often unable to penetrate such materials deeply for more informative analysis, which limits what can be learned about asset structural integrity, crucial information companies require to make investment decisions such as fixing or replacing structures well before assets are completely compromised. ACCObeam provides requisite penetration and high-resolution acoustic imaging with applications in borehole imaging, explosives threat evaluation, and structural health evaluation. This technology will potentially save companies significant money by producing accurate and useful visual analyses to better understand the actual condition of assets in the field and respond accordingly. Los Alamos is searching for a collaboration partner to develop this technology for commercial applications that will lead to licensing opportunities for broad industry deployment.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Igiugig Site Visit Report

The National Renewable Energy Laboratory (NREL) team conducted a site visit during January 23-25, 2019 with the Igiugig Village Council (IVC) and other stakeholders to assist the community of Igiugig in refining their long-term energy strategy. The agenda included a tour of the village, data collection, presentations, long-term visioning exercises, identification of energy scenarios, and a school presentation. Participants in the site visit activities spanned a range of local, regional, government, and industry participation. There were 24 attendees at the all-day community workshop on Thursday, January 24th, with participants from the IVC, NREL, Bristol Bay Native Association (BBNA), Bristol Bay Native Corporation (BBNC), Lake and Peninsula Borough, Alaska Energy Authority, Intergrid, ORPC, Deer Stone Consulting, the Southwest Alaska Municipal Conference, and University of Alaska Fairbanks - Alaska Center for Energy and Power. Igiugig is a well-organized rural Alaskan community, with a leadership team that appears to have broad community support. The use of consensus-based decision making is one tangible example of how the leadership team actively invokes and promotes community-focused thinking. The NREL team observed anecdotal evidence of how this approach seems to be strengthening the community: active participation in the visioning exercise, a student hosted fund-raiser dinner at the school, a clean and organized landfill, and well-maintained roads and buildings. Igiugig has wind, solar, and river hydrokinetic resources readily available within the community. Wind has shown to be a promising resource in the region, and has been integrated into microgrids around the state, but the community has had mixed success with wind technologies: some devices failed quickly and others continue to operate. The economics of solar energy are improving in Alaska, and economical solar projects are being installed around the state. Considering that economic activity in Igiugig peaks during the summer sport fishing season, solar could prove to be a valuable supplement to the electrical system. Igiugig has been a test site for two different river hydrokinetic devices, and they have began a third project to operate ORPC's RivGen device, which is delivering valuable device performance data, operations and maintenance experience, and design refinement information. Already, this device has made over 7-million revolutions, and delivered over 8 MWh of power to the community. Igiugig's river resource is fairly unique because it is available year-round and has the potential to provide reliable base-load power for months at a time. A preliminary investigation of this diverse resource mix suggests that Igiugig could achieve very high levels (70% or more) of annual renewable generation contributions. A critical step in pursuing this path is identifying the mix of energy assets (generation and storage) that best meets the community's budget, needs, and goals. The assets that a community installs early in their grid-modernization initiative can constrain the options that are economical at later stages, which may lead to sub-optimal solutions. This is where technical and economic analysis of potential scenarios (i.e., distinct mixes of energy assets) can be useful in identifying the most promising pathways so that a community can make informed and strategic decisions about the assets they install. These analyses are most accurate and informative when they are based on actual technology performance and cost data. As the RivGen project continues to operate and generate this data, we will be better prepared to evaluate the technology's long-term viability and to identify research areas that would improve it. Igiugig's energy projects are at the cutting edge of two intersecting technology areas: 1) deploying an operational river hydrokinetic turbine, and 2) integrating renewable energy sources to achieve very high percent renewables. If these projects are successful, the lessons learned and technologies developed could be valuable for other microgrids around the world. Igiugig's unique resource mix make it an ideal location for this work, and the community's organizational strength make it an ideal partner in this ground-breaking work. Ongoing support for technical assistance to manage and address technical challenges along the way will maximize the probability of project success.

13 HYDRO ENERGY↗
DOE OSTI2022

Earned Value Management Systems for Operations Activities

An earned value management system (EVMS), which monitors contractor performance, is a requirement for program and project management for all major acquisitions by the United States Federal Government with development effort: (i.e., an asset requiring management attention because of its importance to an agency’s mission; high development, operating, or maintenance costs; high risk and/or high return). As an area less explored in earned value management (EVM) practices, this paper will survey the application of an EVMS for operations activities defined as: (1) Non-capital asset activities that are projects (or project - like) with definable start and end dates, with discrete scopes of work, and measurable accomplishments; as well as (2) Routine or recurring facility or environmental operations. This paper will examine the use of an EVMS to evaluate performance of operations and maintenance activities required once construction of a capital asset is complete and being used as intended. Such activities include upgrades and maintenance in order for capital assets to meet their mission function over a life-cycle (through repair, replacement, etc.). This paper will provide background on this topic from the perspective of the Department of Energy (DOE) Environmental Management (EM) Program. In addition, it will provide material from a panel discussion provided by a group of experts from the October 2019 Office of Environmental Management Project Management Workshop, as well as material from subsequent research on this topic.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Fragility Functions Resource Report: Documented Sources for Electricity and Water Resilience Valuation

Fragility curves provide the vulnerability between hazard intensity and an asset. Federal installations may include many different electricity and water infrastructure types (or assets) such as generators, wind turbines, solar PV, switch yards, substations and power lines as well as water distribution systems that could be affected by different hazards. The vulnerability of each asset is a function of its age, type of materials and maintenance. In addition, the vulnerability changes with the hazards intensity and has a probability distribution function associated with it. The fragility functions are used in conjunction with hazard probability and consequence valuations to determine the values at risk for examination of investment grade analyses of alternative mitigation strategies. This document provides examples of fragility functions and links to their sources for different electricity and water infrastructure assets by hazard type.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Domestic Extremism (Executive Summary)

Domestic extremism (DE) has been a growing concern in the U.S. in recent months, as illustrated in multiple bulletins from the Department of Homeland Security (DHS) warning law enforcement partners of the heightened threat. As concerns about these actors grows, it is important that facilities in the U.S. and internationally that protect critical assets, such as sensitive information, hazardous materials, or critical infrastructure, have effective methods in place to secure those assets. DE has challenged security systems through the danger of insider attack and violence, creating a new threat to be countered. In this effort, therefore, we used a literature review and focus group discussions with experts in critical asset security and extremism to understand the nature of the domestic extremist threat, to identify best practices in securing assets, recognize potential gaps in security measures to be corrected, and recommend actions for the Office of Radiological Security (ORS) to address DE with its partners.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

An Enhanced Computational Framework for Energy Storage Participation in Transmission Planning with Electricity Market Participation: Dynamic Market Participation Restriction Estimation

In the previous study and report, we developed a computational framework to explore the technical and economic viability of the dual use of pumped storage hydropower (PSH) for both transmission assets and market participation. The rationale for the dual-use application is to improve the resource utilization rate and reduce the cost recovery burden. A PSH-based transmission solution generally has a large capacity, which corresponds to a higher investment cost compared to a traditional line solution. Market participation may allow the PSH asset to offset part of its high investment cost. A critical question is whether it is economically and technically viable to allow dual use of a PSH-based transmission project without jeopardizing transmission system reliability. And a more specific follow-up question is how to improve market participation performance while maintaining transmission reliability. In this study, we extend the computational framework by updating the method to determine transmission service obligation requirements dynamically based on the possibility of the need for transmission service in operation. Specifically, we correlate the need for transmission service with the probability of thermal overloading on a monitored transmission line, which is essentially based on the probability distribution of power flow and its probability of exceeding the line rating based on system dispatch and operational uncertainties. In addition, the need depends on the level of risk that a system operator is willing to take. The higher the risk tolerance, the lower the requirement that the system operator would apply to the PSH project as a transmission asset. The requirements are updated every day before the PSH project determines its market participation strategy. With the updated transmission service requirements, it is expected that a PSH project can serve as a transmission asset when the transmission system is at risk and really needs it, while it can participate in a market with sufficient information and certain restrictions. In this way, the transmission service quality and market participation performance can both be improved.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Wireless Sensor Modalities at a Nuclear Plant Site to Collect Vibration Data

One of the major contributors to the total operating costs of domestic nuclear fleet of reactors today is the operation and maintenance (O&M) costs. These include labor-intense preventive maintenance programs involving manually-performed inspection, calibration, testing, and maintenance of plant assets at periodic frequency and time-based replacement of assets at periodic frequency, irrespective of their conditions. This has resulted in a labor-centric business model to achieve high capacity factors. To build an optimal maintenance program, it’s time to transition from this labor-centric business model to a technology-centric business model. Fortunately, there are technologies (advanced sensor, data analytics, and risk assessment methodologies) that will support this transition. The technology-centric business model will result in significant plant life extension and reduction of time-based maintenance activities. This will drive down O&M costs as labor is a rising cost and technology is a declining cost. This approach will lay the foundation for real-time condition assessment of plant assets, allowing condition-based maintenance to enhance plant safety, reliability, and economics of operation. The goal of this project is to address challenges in the area of digital monitoring, i.e., the application of advanced sensor technologies (particularly wireless sensor technologies) and science-based data analytic capabilities to advance online monitoring and predictive maintenance in nuclear plants to improve plant performance (efficiency gain and economic competitiveness). To achieve the project goal, in partnership with Exelon Generating Company (Exelon), researchers from Idaho National Laboratory (INL) and Oak Ridge National Laboratory (ORNL) are performing research and development (R&D) to demonstrate application of wireless sensors using the distributed antenna system and advanced data analytics to achieve predictive maintenance. In the report, wireless vibration sensors, vibration data and its indicator are described. The wireless vibration sensors presented in this report support three types of wireless communication, namely, Wi-Fi, cellular, and 900 MHz. These wireless vibration sensors are considered by partner plant site for installation on plant asset to enable online vibration monitoring to replace periodic measurements. These vibration data along with other plant process data will be utilized to develop diagnostic and prognostic models.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗
DOE OSTI2025

HydroBio: Hydropower Capacity and Freshwater Biodiversity in Conterminous United States Sub-basins

This dataset summarizes existing and potential hydropower capacity and freshwater biodiversity at the sub-basin level throughout the conterminous United States (CONUS). It contains descriptive information regarding each sub-basin (e.g., 8-digit hydrologic unit code identifier, name, states, and size) along with sub-basin-level summaries of: 1) existing hydropower capacity (MW), 2) potential nominal non-powered dam (NPD) capacity (MW), 3) potential capacity of new stream reach development (NSD) (MW), and 4) freshwater biodiversity, including the total richness of fish, crayfish, and mussels and metrics that account for how rare and threatened those species tend to be. Hydropower data were obtained from Oak Ridge National Laboratory data resources (Existing Hydropower Assets, Non-Powered Dam Technical Potential, and New Stream Reach Development). Freshwater biodiversity data were obtained from NatureServe. Sub-basin characteristic information was obtained from the United States Geological Survey. Additionally, long data that provide lists of unique elements within each sub-basin for each constituent data resource (e.g., NatureServe, Existing Hydropower Assets) are provided to enhance dataset utility for users. The dataset provides, for the first time, a national-level assessment of existing and potential hydropower capacity in the context of freshwater biodiversity and is a valuable resource for stakeholders tasked with providing affordable, reliable energy to the American public while maintaining or enhancing invaluable freshwater resources. The dataset contains six data files in comma separated (*.csv) format that are within a zipped file.

Bozeman, Bryan [Oak Ridge National Laboratory (ORN↗
DOE OSTI2024

Technical Language Processing of Nuclear Power Plants Equipment Reliability Data

Operating nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) element data that contain information about the status of components, assets, and systems. Some of this information is in textual form where the occurrence of abnormal events or maintenance activities are described. Analyses of NPP textual data via natural language processing (NLP) methods have expanded in the last decade, and only recently the true potential of such analyses has emerged. So far, applications of NLP methods have been mostly limited to classification and prediction in order to identify the nature of the given textual element (e.g., safety or non-safety relevant). In this paper, we target a more complex problem: the automatic generation of knowledge based on a textual element in order to assist system engineers in assessing an asset’s historical health performance. The goal is to assist system engineers in the identification of anomalous behaviors, cause–effect relations between events, and their potential consequences, and to support decision-making such as the planning and scheduling of maintenance activities. “Knowledge extraction” is a very broad concept whose definition may vary depending on the application context. In our particular context, it refers to the process of examining an ER textual element to identify the systems or assets it mentions and the type of event it describes (e.g., component failure or maintenance activity). In addition, we wish to identify details such as measured quantities and temporal or cause–effect relations between events. This paper describes how ER textual data elements are first preprocessed to handle typos, acronyms, and abbreviations, then machine learning (ML) and rule-based algorithms are employed to identify physical entities (e.g., systems, assets, and components) and specific phenomena (e.g., failure or degradation). A few applications relevant from an NPP ER point of view are presented as well.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Blockchain Research and Development Activities Sponsored by the U.S. Department of Energy and Utility Sector

This article provides an in-depth analysis of blockchain research in the energy sector, focusing on projects funded by the U.S. Department of Energy (DOE) and comparing them with industry-funded initiatives. A total of 110 funded activities within the U.S. power industry were successfully tracked and mapped into a newly developed categorization framework. This framework is designed to help research agencies to systematically understand their funded portfolio. Such characterization is expected to help them make effective investments, identify research gaps, measure impact, and advance technological progress to meet national goals. In line with this need, the proposed framework proposes a 2-D categorization matrix to systematically classify blockchain efforts within the energy sector.Under the proposed framework, the Energy System Domain serves as the primary classification dimension, categorizing use cases into 30 distinct applications. The second dimension, Blockchain Properties, captures the specific needs and functionalities provided by Blockchain technology. The aim was to capture blockchain’s applicability and functionality: where and why blockchain? Principles behind the selection of the viewpoint dimensions were carefully defined based on consensus obtained through the Blockchain for Optimized Security and Energy Management (BLOSEM) project. The mapped results show that activities within the Grid Automation, Coordination, and Control (31.8%), Marketplaces and Trading (25.5%), Foundational Blockchain Research (19.1%), and Supply Chain Management (17.3%) domains have been actively pursued to date. The three leading specific use case applications were identified as Transactive Energy Management for Marketplaces and Trading, Asset Management for Supply Chain Management, and Fundamental Blockchain for Foundational Blockchain Research. The Marketplaces and Trading and Retail Services Enablement domains stood out as being favored by industry by a factor greater than 2 (2.3 and 2.6, respectively), yet there seemed to be little to zero investment from DOE. Approximately 76% of the total projects prioritized Immutability, Identity Management, and Decentralization and/or Disintermediation compared to Asset Digitization and/or Tokenization, Automation, and Privacy and/or Anonymity. The greatest discrepancies between DOE and industry were in Asset Digitization and/or Tokenization and Automation. The industry efforts (36% in Asset Digitization/Tokenization and 22% in Automation) was 14 times and 2.4 times, respectively, more intensive than the DOE-sponsored efforts, indicating a significant discrepancy in industry versus government priorities. Overall, quantifying DOE-sponsored projects and industry activities through mapping provides clarity on portfolio investments and opportunities for future research.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2005

Sensor Webs: Autonomous Rapid Response to Monitor Transient Science Events

To better understand how physical phenomena, such as volcanic eruptions, evolve over time, multiple sensor observations over the duration of the event are required. Using sensor web approaches that integrate original detections by in-situ sensors and global-coverage, lower-resolution, on-orbit assets with automated rapid response observations from high resolution sensors, more observations of significant events can be made with increased temporal, spatial, and spectral resolution. This paper describes experiments using Earth Observing 1 (EO-1) along with other space and ground assets to implement progressive mission autonomy to identify, locate and image with high resolution instruments phenomena such as wildfires, volcanoes, floods and ice breakup. The software that plans, schedules and controls the various satellite assets are used to form ad hoc constellations which enable collaborative autonomous image collections triggered by transient phenomena. This software is both flight and ground based and works in concert to run all of the required assets cohesively and includes software that is model-based, artificial intelligence software.

Mandl, Dan↗
NASA NTRS2009

Monitoring Disasters by Use of Instrumented Robotic Aircraft

Efforts are under way to develop data-acquisition, data-processing, and data-communication systems for monitoring disasters over large geographic areas by use of uninhabited aerial systems (UAS) robotic aircraft that are typically piloted by remote control. As integral parts of advanced, comprehensive disaster- management programs, these systems would provide (1) real-time data that would be used to coordinate responses to current disasters and (2) recorded data that would be used to model disasters for the purpose of mitigating the effects of future disasters and planning responses to them. The basic idea is to equip UAS with sensors (e.g., conventional video cameras and/or multispectral imaging instruments) and to fly them over disaster areas, where they could transmit data by radio to command centers. Transmission could occur along direct line-of-sight paths and/or along over-the-horizon paths by relay via spacecraft in orbit around the Earth. The initial focus is on demonstrating systems for monitoring wildfires; other disasters to which these developments are expected to be applicable include floods, hurricanes, tornadoes, earthquakes, volcanic eruptions, leaks of toxic chemicals, and military attacks. The figure depicts a typical system for monitoring a wildfire. In this case, instruments aboard a UAS would generate calibrated thermal-infrared digital image data of terrain affected by a wildfire. The data would be sent by radio via satellite to a data-archive server and image-processing computers. In the image-processing computers, the data would be rapidly geo-rectified for processing by one or more of a large variety of geographic-information- system (GIS) and/or image-analysis software packages. After processing by this software, the data would be both stored in the archive and distributed through standard Internet connections to a disaster-mitigation center, an investigator, and/or command center at the scene of the fire. Ground assets (in this case, firefighters and/or firefighting equipment) would also be monitored in real time by use of Global Positioning System (GPS) units and radio communication links between the assets and the UAS. In this scenario, the UAS would serve as a data-relay station in the sky, sending packets of information concerning the locations of assets to the image-processing computer, wherein this information would be incorporated into the geo-rectified images and maps. Hence, the images and maps would enable command-center personnel to monitor locations of assets in real time and in relation to locations affected by the disaster. Optionally, in case of a disaster that disrupted communications, the UAS could be used as an airborne communication relay station to partly restore communications to the affected area. A prototype of a system of this type was demonstrated in a project denoted the First Response Experiment (Project FiRE). In this project, a controlled outdoor fire was observed by use of a thermal multispectral scanning imager on a UAS that delivered image data to a ground station via a satellite uplink/ downlink telemetry system. At the ground station, the image data were geo-rectified in nearly real time for distribution via the Internet to firefighting managers. Project FiRE was deemed a success in demonstrating several advances essential to the eventual success of the continuing development effort.

Wegener, Steven S.↗
NASA NTRS2012

Mars Express Forward Link Capabilities for the Mars Relay Operations Service (MaROS)

This software provides a new capability for landed Mars assets to perform forward link relay through the Mars Express (MEX) European Union orbital spacecraft. It solves the problem of standardizing the relay interface between lander missions and MEX. The Mars Operations Relay Service (MaROS) is intended as a central point for relay planning and post-pass analysis for all Mars landed and orbital assets. Through the first two phases of implementation, MaROS supports relay coordination through the Odyssey orbiter and the Mars Reconnaissance Orbiter (MRO). With this new software, MaROS now fully integrates the Mars Express spacecraft into the relay picture. This new software generates and manages a new set of file formats that allows for relay request to MEX for forward and return link relay, including the parameters specific to MEX. Existing MEX relay planning interactions were performed via email exchanges and point-to-point file transfers. By integrating MEX into MaROS, all transactions are managed by a centralized service for tracking and analysis. Additionally, all lander missions have a single, shared interface with MEX and do not have to integrate on a mission-by mission basis. Relay is a critical element of Mars lander data management. Landed assets depend largely upon orbital relay for data delivery, which can be impacted by the availability and health of each orbiter in the network. At any time, an issue may occur to prevent relay. For this reason, it is imperative that all possible orbital assets be integrated into the overall relay picture.

Allard, Daniel A.↗