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NASA NTRS2012

The International Space Station: New Capabilities for Disaster Response and Humanitarian Aid

The International Space Station (ISS) has been acquiring Earth imagery since 2000, primarily in the form of astronaut photography using hand-held film and digital cameras. Recent additions of more sophisticated multispectral and hyperspectral sensor systems have expanded both the capabilities and relevance of the ISS to basic research, applied Earth science, and development of new sensor technologies. Funding opportunities established within NASA, the US National Laboratories and the international partner organizations have generated instrument proposals that will further enhance these capabilities. With both internal and external sensor location options, and the availability of both automated and human-tended operational environments, the ISS is a unique platform within the constellation of Earth-observing satellites currently in orbit. Current progress and challenges associated with development of ISS terrestrial remote sensing capabilities in the area of disaster response and support of relief efforts will be presented. The ISS orbit allows for imaging of the Earth's surface at varying times of day and night, providing opportunities for data collection over approximately 95% of the populated regions. These opportunities are distinct from--yet augment--the data collection windows for the majority of sensors on polar-orbiting satellites. In addition to this potential for "being in the right place at the right time" to collect critical information on an evolving disaster, the presence of a human crew also allows for immediate recognition of an event from orbit, notification of relevant organizations on the ground, and re-tasking of available remote sensing resources to support humanitarian response and relief efforts. Challenges to establishing an integrated response capability are both technical (coordination of sensor targeting and data collection, rapid downlink and posting of data to a central accessible hub, timely generation and distribution of relevant data products) and operational (notification and engagement of sensor support teams, international partner agency sanction of astronaut support activities). To better collaborate on common issues and strengthen applications, including using the data to support disaster response, we established an ISS Program Science Forum Working Group for Earth Observations comprised of representatives from the international partner agencies. This international forum welcomes input and support from relevant United Nations task groups regarding our disaster response and humanitarian aid to enable development of the ISS capabilities in this area for greatest value to the international community.

Stefanov, William↗

NASA NTRS1995

The luminosity structure and objective classification of galaxies

The luminosity structure of spiral galaxies is studied using the technique of principal component analysis. It is found that approximately 94% of the variation in the luminosity distribution of galaxies can be accounted for by just two principal components. The principal luminosity components may contain valuable information about star formation history or whatever luminosity-regulating process occurs in galaxies. Practically, these principal components provide a new approach for the investigation of the luminosity structures of galaxies and their dependence on other properties. They also serve as an excellent objective classification system for galaxies. We introduce in this paper such a classification scheme and explore its various properties. The new system shows a number of very impressive characteristics. Most important, it can well segregate virtually all the important galactic properties we tested and does so much better than the conventional morphological classification systems. Of particular interest is that some distance-dependent parameters can also be determined to a surprisingly good accuracy; for example, absolute magnitude may be determined to an accuracy of approximately 0.6 mag (yet further improvement is believed to be highly possible). Second, the system is objective, and the classification procedure can be automated to a large degree; also the new system can apply to much smaller and fainter images than do eye-based clasification systems. These properties make the new system suitable for practical application, especially on very large (and deeper) digital image catalogs. Third, the classification is expressed in dimensionless numbers, yet the simple notation bears significant and easily understandable meaning, making it easy and convenient to use. Finally, the new system has another extremely useful feature: it provides a very powerful and convenient platform not only for classification, but also for easily recording, examining, and studying the variations and correlations of galaxy properties-all these may be carried out graphically by using the C-vectors and the C-diagrams introduced in the paper. We wil also give an example to demonstrate the use of the classification system for the study of the internal extinction problem in spiral galaxies.

Han, Mingshen↗

DOE OSTI2024

Machine learning at the edge to improve in-field safeguards inspections

Artificial intelligence (AI) and machine learning (ML) are near-ubiquitous in day-to-day life; from cars with automated driver-assistance, recommender systems, generative content platforms, and large language chatbots. Implementing AI as a tool for international safeguards could significantly decrease the burden on safeguards inspectors and nuclear facility operators. The use of AI would allow inspectors to complete their in-field activities quicker, while identifying patterns and anomalies and freeing inspectors to focus on the uniquely human component of inspections. Sandia National Laboratories has spent the past two and a half years developing on-device machine learning to develop both a digital and robotic assistant. This combined platform, which we term inspecta, has numerous on-device machine learning capabilities that have been demonstrated at the laboratory scale. Here this work describes early successes implementing AI/ML capabilities to reduce the burden of tedious inspector tasks such as seal examination, information recall, note taking, and more.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2023

Skewering the silos: using Brick to enable portable analytics, modeling and controls in buildings

Nearly all large commercial buildings have heating, ventilation and air conditioning (HVAC) systems, lighting systems, safety and other systems controlled by a computer—a dedicated server with a building energy management system (BMS). However, these BMSs are proprietary with each building’s assets (that is, fans, valves, pumps, and their setpoints) named and coded uniquely by the BMS vendor or engineer; building analytics and control algorithms are written specific to the assets and the building. Thus, any control updates or analytics to improve building performance—especially critical to reduce greenhouse emissions or improve load flexibility—are labor intensive and costly. The Brick schema was developed so the same analysis or control algorithms can work on a variety of buildings if each is digitally represented in a Brick data model. The goal of this project was to further the development of Brick to extend it beyond an academic project with demonstrated success in a small field study, to a practical choice for industrial and commercial stakeholders seeking to realize value from building data. To do this, we executed four objectives: (1) expand the Brick schema including its modeling capabilities and vocabulary, (2) develop tools for integrating Brick with existing digital technologies and representations in buildings, (3) develop an open-source analytics platform to facilitate use of Brick in delivering data value, and (4) demonstrate Brick-driven analytics and controls in real settings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Roadmap to Automated Mobility Systems: Informing the Planning of a Sustainable, Resilient Transportation Ecosystem for Dallas/Fort Worth International Airport

The National Renewable Energy Laboratory (NREL) developed this report to provide Dallas/Fort Worth International Airport (DFW) assistance toward a vision that considers the maturation and proliferation of mobility automation, electrification, and infrastructure integrated with Internet of Things (IoT) technologies as they present themselves on the path to 2035. In planning ongoing infrastructure investments, DFW has goals to accommodate and leverage these enabling technologies toward greater sustainability and enhanced traveler and employee experiences, ensuring that infrastructure investments are fully utilized into the future. The objective of this document is to assist DFW to anticipate and envision future airport access by travelers, employees, and goods by examining existing needs and exploring opportunities enabled by technology that informs longer-term infrastructure planning. The vast infrastructure of DFW, which includes buildings, roadways, and other physical structures, as well as growing digital and energy network infrastructures, requires long-term planning and strategy to fully leverage technology advancement and avoid abandoning assets due to functional obsolescence.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Myna: Connecting powder bed fusion build data to simulation tools for digital twin applications

Additive manufacturing (AM), as a digital process, can generate a detailed digital thread linking a part’s design and manufacturing to its operational performance. As AM systems advance, an increasing amount of process data is stored in manufacturing databases. In principle, this data can be utilized by simulation-based digital twin approaches, such as real-time process control and asynchronous post-processing guidance. However, few tools currently exist for systematically integrating digital thread data with computational tools. Here, in this study, we propose a software package, called Myna, for connecting data from powder bed fusion processes to simulation tools. The utility of such a platform is demonstrated using build data from the Oak Ridge National Laboratory Manufacturing Demonstration Facility “Peregrine v2023-10” public dataset to automatically configure and run 54 semi-analytical 3DThesis melt pool simulations, 78 numerical Additive FOAM melt pool simulations, and 3 ExaCA microstructure simulations. The simulated, spatially registered microstructures are then compared directly with electron backscatter diffraction characterization of the corresponding as-built part locations. The resulting simulated microstructure showed variation as a function of process parameters, particularly stripe width; however, the experimental data had little variation between the microstructure texture and grain size resulting from different processing conditions. Analysis of the discrepancies suggest that it is possible a two-phase ferritic-austenitic solidification model is needed to accurately predict grain size and texture for certain stainless steel 316L feedstock compositions under powder bed fusion conditions, providing direction for future research. As illustrated here, due to the number and complexity of the simulations involved in AM process-structure–property predictions, automated methods to connect process data and simulations will remain necessary tools for testing hypotheses and implementing digital twin applications.

Knapp, Gerald L. [Oak Ridge National Laboratory (O↗

NASA NTRS1994

A New Generation of Intelligent Trainable Tools for Analyzing Large Scientific Image Databases

In a variety of scientific disciplines two-dimensional digital image data is now relied on as a basic component of routine scientific investigation. The proliferation of image acquisition hardware such as multi-spectral remote-sensing platforms, medical imaging sensors, and high-resolution cameras have led to the widespread use of image data in fields such as atmospheric studies, planetary geology, ecology, agriculture, glacielogy, forestry, astronomy, diagnostic medicine, to name but a few.

machine↗

NASA NTRSDate not supplied

Cloud Computing Option for Modeling the Debris Environment

NASA’s Digital Transformation Initiative aims to promote the agency’s adoption of current and evolving digital technologies. Through agency-wide collaboration with other NASA teams, the Office of Safety and Mission Assurance (OSMA) has directed the Orbital Debris Program Office and the Meteoroid Environment Office to integrate cloud computing technologies in their publicly released software models: the Orbital Debris Engineering Model (ORDEM) and the Meteoroid Engineering Model (MEM). Decoupling the user interface from the backend processor was key for the software packages to run on a cloud computing framework. Benefits to this design include horizontal scaling of computing resources, user authentication and authorization, and automated deployment. Both models are hosted on a cloud computing platform supported by the NASA authorized IT security and compliance framework. This paper focuses on the new ORDEM web application, which includes the current features of the publicly released ORDEM software with an upgraded frontend design. The underlying ORDEM processor is run on a cloud container, allowing the user to run multiple spacecraft and telescope/radar mode simulations. Featuresexclusive to the ORDEM web application, such as importing multiple TLEs, auto-generated plotting, and the ability to check runtime progress are discussed. Comparisons between the current ORDEM software and the web application are summarized.

Andrew Vavrin↗

NASA NTRS2023

Cloud Computing Option for Modeling the Debris Environment

NASA’s Digital Transformation Initiative aims to promote the agency’s adoption of current and evolving digital technologies. Through agency-wide collaboration with other NASA teams, the Office of Safety and Mission Assurance (OSMA) has directed the Orbital Debris Program Office and the Meteoroid Environment Office to integrate cloud computing technologies into their publicly released software models: the Orbital Debris Engineering Model (ORDEM) and the Meteoroid Engineering Model (MEM). Decoupling the user interface from the backend processor was key for the software packages to run on a cloud computing framework. Benefits to this design include horizontal scaling of computing resources, user authentication and authorization, and automated deployment. Both models are hosted on a cloud computing platform supported by the NASA authorized IT security and compliance framework. This paper focuses on the new ORDEM web application, which includes the current features of the publicly released ORDEM software with an upgraded frontend design, although parallels between ORDEM and MEM are also discussed. The underlying ORDEM processor is run on a cloud container, allowing the user to run multiple spacecraft and telescope/radar mode simulations. Features exclusive to the ORDEM web application, such as importing multiple TLEs, auto-generated plotting, and the ability to check runtime progress are discussed. Comparisons between the current ORDEM software and the web application are summarized.

Andrew Vavrin↗

NASA NTRS2015

Earth Observations from the International Space Station: Benefits for Humanity

The International Space Station (ISS) is a unique terrestrial remote sensing platform for observation of the Earth's land surface, oceans, and atmosphere. Unlike automated remote-sensing platforms it has a human crew; is equipped with both internal and externally-mounted active and passive remote sensing instruments; and has an inclined, low-Earth orbit that provides variable views and lighting (day and night) over 95 percent of the inhabited surface of the Earth. As such, it provides a useful complement to autonomous, sun-synchronous sensor systems in higher altitude polar orbits. Beginning in May 2012, NASA ISS sensor systems have been available to respond to requests for data through the International Charter, Space and Major Disasters, also known as the "International Disaster Charter" or IDC. Data from digital handheld cameras, multispectral, and hyperspectral imaging systems has been acquired in response to IDC activations and delivered to requesting agencies through the United States Geological Survey. The characteristics of the ISS for Earth observation will be presented, including past, current, and planned NASA, International Partner, and commercial remote sensing systems. The role and capabilities of the ISS for humanitarian benefit, specifically collection of remotely sensed disaster response data, will be discussed.

Stefanov, William L.↗

NASA NTRS2012

Investigation into Cloud Computing for More Robust Automated Bulk Image Geoprocessing

Geospatial resource assessments frequently require timely geospatial data processing that involves large multivariate remote sensing data sets. In particular, for disasters, response requires rapid access to large data volumes, substantial storage space and high performance processing capability. The processing and distribution of this data into usable information products requires a processing pipeline that can efficiently manage the required storage, computing utilities, and data handling requirements. In recent years, with the availability of cloud computing technology, cloud processing platforms have made available a powerful new computing infrastructure resource that can meet this need. To assess the utility of this resource, this project investigates cloud computing platforms for bulk, automated geoprocessing capabilities with respect to data handling and application development requirements. This presentation is of work being conducted by Applied Sciences Program Office at NASA-Stennis Space Center. A prototypical set of image manipulation and transformation processes that incorporate sample Unmanned Airborne System data were developed to create value-added products and tested for implementation on the "cloud". This project outlines the steps involved in creating and testing of open source software developed process code on a local prototype platform, and then transitioning this code with associated environment requirements into an analogous, but memory and processor enhanced cloud platform. A data processing cloud was used to store both standard digital camera panchromatic and multi-band image data, which were subsequently subjected to standard image processing functions such as NDVI (Normalized Difference Vegetation Index), NDMI (Normalized Difference Moisture Index), band stacking, reprojection, and other similar type data processes. Cloud infrastructure service providers were evaluated by taking these locally tested processing functions, and then applying them to a given cloud-enabled infrastructure to assesses and compare environment setup options and enabled technologies. This project reviews findings that were observed when cloud platforms were evaluated for bulk geoprocessing capabilities based on data handling and application development requirements.

Brown, Richard B.↗

DOE OSTI2022

Modelica-json: Transforming energy models to digitize the control delivery process

Building simulation models are typically not used to generate the documentation required for bidding and project delivery of commercial building systems, or for their semantic modeling and commissioning. This paper presents a software tool that aids in digitizing the control delivery process, spanning simulation during design to implementation and formal verification during commissioning. The tool can generate from Modelica models digital documentation of control sequences. This digital documentation, along with other project drawings and specifications can be used for project bidding. It can also be used for implementation of control sequences through machineto-machine translation to commercial legacy control products, for which we are currently developing the proposed ASHRAE Standard 231P based on the presented work. Moreover, as-installed control sequences can be formally verified against the design specification, and a semantic model in Brick can be exported to aid in configuration of building analytics and fault detection. The paper presents what we believe is the first translation of a Modelica-implemented control sequence to a native implementation on a commercial control platform, using the webCTRL product line from Automated Logic. The paper also shows how a webCTRL implementation can be formally verified against its Modelica specification. These use cases have all been demonstrated with a prototype implementation that is now being further developed.

Wetter, Michael↗

DOE OSTI2022

Virtual Infrastructure Twins: Software Testing Platforms for Computing-Instrument Ecosystems

Science ecosystems are being built by federating computing systems and instruments located at geographically distributed sites over wide-area networks. These computing-instrument ecosystems are expected to support complex workflows that incorporate remote, automated AI-driven science experiments. Their realization, however, requires various designs to be explored and software components to be developed, in order to support the orchestration of distributed computations and experiments. It is often too expensive, infeasible, or disruptive for the entire ecosystem to be available during the typically long software development and testing periods. We propose a Virtual Infrastructure Twin (VIT) of the ecosystem that emulates its network and computing components, and incorporates its instrument software simulators. It provides a software environment nearly identical to the ecosystem to support early development and testing, and design space exploration. We present a brief overview of previous digital infrastructure twins that culminated in the VIT concept, including (i) the virtual science network environment for developing software-defined networking solutions, and (ii) the virtual federated science instrument environment for testing the federation software stack and remote instrument control software. We briefly describe VITs for Nion microscope steering and access to GPU systems.

Rao, Nageswara↗

DOE OSTI2023

Virtual Infrastructure Twins: Software Testing Platforms for Computing-Instrument Ecosystems

Rao, Nageswara↗

DOE OSTI2023

Creating a Simulation Platform for Research and Development of Advanced Control Methods

Advanced nuclear reactors are essential to meet the changing energy requirements throughout both the United States and the rest of world. In addition to other features, they are designed to enable deployment in remote locations and operate in a fully (or near-fully) autonomous manner, which will require a new control paradigm. To realize autonomously operating reactors, the U.S. Department of Energy’s Nuclear Energy Enabling Technologies Advanced Sensors and Instrumentation (NEET ASI) program conducts research and development into the enabling technologies and methods needed, including digital twins, machine learning, and risk modeling, in addition to various types of control methods. These technologies and methods are the key foundations needed to achieve fully autonomous systems. To develop and evaluate the technologies and methods necessary for achieving autonomous operations, it is critical to identify a software tool capable of integrating all the required elements. In surveying the available solutions, no software platforms were identified that could accomplish what was needed without introducing drawbacks. This challenge was the motivation for the current effort: to develop a software platform that can seamlessly integrate autonomouscontrol-enabling technologies and methods, allowing for accelerated research and development and transfer of ideas. The resulting platform, known as the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND), is Python-based, and leverages open-source tools to provide flexibility and facilitate building upon prior research. It is designed to enable advanced reactor developers to deploy and test advanced control technologies and methods coupled with their own models, solutions, and hardware. Given the substantial undertaking of developing such a platform, the current effort focused on laying down scalable, flexible software foundations and infrastructure, then demonstrating the platform via a use case. These foundations included developing generic modules, which contain the base variable and system blocks (the information and functional building blocks, respectively, that can be used to design a simulation) and the data handling and storage blocks needed to exchange information between the various blocks; as well as enablingtechnology-specific modules. This platform was evaluated via a use case, which was to simulate and control a process for the Microreactor Automated Control System (MACS) test bed. While MACS is not currently directly coupled to any specific microreactor physics, it was initially developed in concert with the Microreactor Applications Research Validation and Evaluation (MARVEL) microreactor, and so the MARVEL physics are used here. As part of this use case, several enabling-technology-specific blocks within COMMAND were integrated, including a proportional integral derivative (PID) control block, a Reactor Excursion and Leak Analysis Program (RELAP5-3D) block, and an anomaly detection block. The COMMAND software platform was successfully demonstrated to achieve the scalability and flexibility objectives of this effort and will be leveraged by the program’s research efforts to advance state of the art control methodologies towards autonomous operations of advanced reactors. As new use cases are created and implemented, it is anticipated that COMMAND will continue to grow and evolve to meet new requirements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Galaxy Zoo: 3D – crowdsourced bar, spiral, and foreground star masks for MaNGA target galaxies

ABSTRACT The challenge of consistent identification of internal structure in galaxies – in particular disc galaxy components like spiral arms, bars, and bulges – has hindered our ability to study the physical impact of such structure across large samples. In this paper we present Galaxy Zoo: 3D (GZ:3D) a crowdsourcing project built on the Zooniverse platform that we used to create spatial pixel (spaxel) maps that identify galaxy centres, foreground stars, galactic bars, and spiral arms for 29 831 galaxies that were potential targets of the MaNGA survey (Mapping Nearby Galaxies at Apache Point Observatory, part of the fourth phase of the Sloan Digital Sky Surveys or SDSS-IV), including nearly all of the 10 010 galaxies ultimately observed. Our crowdsourced visual identification of asymmetric internal structures provides valuable insight on the evolutionary role of non-axisymmetric processes that is otherwise lost when MaNGA data cubes are azimuthally averaged. We present the publicly available GZ:3D catalogue alongside validation tests and example use cases. These data may in the future provide a useful training set for automated identification of spiral arm features. As an illustration, we use the spiral masks in a sample of 825 galaxies to measure the enhancement of star formation spatially linked to spiral arms, which we measure to be a factor of three over the background disc, and how this enhancement increases with radius.

Masters, Karen L. (ORCID:0000000308469578)↗

NASA NTRS2015

International Space Station Data Collection for Disaster Response

Remotely sensed data acquired by orbital sensor systems has emerged as a vital tool to identify the extent of damage resulting from a natural disaster, as well as providing near-real time mapping support to response efforts on the ground and humanitarian aid efforts. The International Space Station (ISS) is a unique terrestrial remote sensing platform for acquiring disaster response imagery. Unlike automated remote-sensing platforms it has a human crew; is equipped with both internal and externally-mounted remote sensing instruments; and has an inclined, low-Earth orbit that provides variable views and lighting (day and night) over 95 percent of the inhabited surface of the Earth. As such, it provides a useful complement to autonomous sensor systems in higher altitude polar orbits. NASA remote sensing assets on the station began collecting International Disaster Charter (IDC) response data in May 2012. The initial NASA ISS sensor systems responding to IDC activations included the ISS Agricultural Camera (ISSAC), mounted in the Window Observational Research Facility (WORF); the Crew Earth Observations (CEO) Facility, where the crew collects imagery using off-the-shelf handheld digital cameras; and the Hyperspectral Imager for the Coastal Ocean (HICO), a visible to near-infrared system mounted externally on the Japan Experiment Module Exposed Facility. The ISSAC completed its primary mission in January 2013. It was replaced by the very high resolution ISS SERVIR Environmental Research and Visualization System (ISERV) Pathfinder, a visible-wavelength digital camera, telescope, and pointing system. Since the start of IDC response in 2012 there have been 108 IDC activations; NASA sensor systems have collected data for thirty-two of these events. Of the successful data collections, eight involved two or more ISS sensor systems responding to the same event. Data has also been collected by International Partners in response to natural disasters, most notably JAXA and Roscosmos/Energia through the Urugan program.

Stefanov, William L.↗

DOE OSTI2025

Microreactor Automated Control System Test Bed Digital Architecture for Real-Time, Hardware-in-the-Loop Simulation

This work describes progress made towards the development of a real-time hardware-in-the-loop (HIL) test bed for non-nuclear testing of microreactor control schemes and failure modes. Non-nuclear testing is a crucial step in developing robust control algorithms for managing microreactor dynamics. The creation of an HIL simulation harnesses the realistic dynamics of physical analogue systems while additionally considering the challenges of variable communication delay. This collaborative effort between Oak Ridge National Laboratory and Idaho National Laboratory has resulted in a LabVIEW-based gRPC communication protocol which couples a TRANSFORM Modelica simulation of nuclear components to the ViBRANT physical hardware for realistic feedback and visual representation of control action in real time. A modular python client structure is developed to manage FMU-based Modelica simulation and real-time gRPC communication. HIL testing suggests that the modeled reactor with natural convection molten salt loop coolant configuration responds well to PID control of drum positioning for modulation of reactor core power, however, future efforts will be made to explore the added thermal inertial delay of system level control and downstream demand changes. Development of this platform with a generalized methodology provides a foundation for exploring a variety of reactor configurations and failure modes in rapid order to provide insight into the most effective avenues of study for further research and development.

McConnell, Jono [ORNL] (ORCID:0000000238984741)↗