Results for “engineering knowledge integration” — Page 33

NASA NTRS2018

A Laser-Induced Fluorescence Diagnostic for HERMeS and High-Power Electric Propulsion

NASA is continuing the development of a 12.5-kW Hall thruster system, which is baselined in a phased exploration concept to expand human presence to cis-lunar space and eventually to Mars. The development team is transitioning knowledge gained from the testing of the government-built Technology Development Unit (TDU) to the contractor-built Engineering Development Unit (EDU). A new laser-induced fluorescence diagnostic that is compatible with the testing of engineering hardware was developed to obtain data for thruster model validation in the lowest background pressure achievable. Prior to performing the test on the EDU, the team performed a functional checkout test of this new diagnostic using the TDU. In addition to providing a checkout of the diagnostic, this test provided data that can be correlated to electron mobility for comparison to the EDU at a later date. A number of technical challenges related to large test facilities and interfacing with engineering hardware were overcome while implementing the new laser diagnostic system. The initial data set was in good agreement with prior optical and non-optical diagnostics data collected on the TDU thrusters. This data set also revealed the spatial origin of high angle ions that have been of concern for spacecraft integration.

Huang, Wensheng↗

NASA NTRS2018

Ion Velocity in the Discharge Channel and Near-Field of the HERMeS Hall Thruster

NASA is continuing the development of a 12.5-kW Hall thruster system, which is baselined in a phased exploration concept to expand human presence to cis-lunar space and eventually to Mars. The development team is transitioning knowledge gained from the testing of the government-built Technology Development Unit (TDU) to the contractor-built Engineering Development Unit (EDU). A new laser-induced fluorescence diagnostic that is compatible with the testing of engineering hardware was developed to obtain data for thruster model validation in the lowest background pressure achievable. Prior to performing the test on the EDU, the team performed a functional checkout test of this new diagnostic using the TDU. In addition to providing a checkout of the diagnostic, this test provided data that can be correlated to electron mobility for comparison to the EDU at a later date. A number of technical challenges related to large test facilities and interfacing with engineering hardware were overcome while implementing the new laser diagnostic system. The initial data set was in good agreement with prior optical and non-optical diagnostics data collected on the TDU thrusters. This data set also revealed the spatial origin of high angle ions that have been of concern for spacecraft integration.

Huang, Wensheng↗

NASA NTRS2018

A Laser-Induced Fluorescence Diagnostic for HERMeS and High-Power Electric Propulsion

NASA is continuing the development of a 12.5-kW Hall thruster system, which is baselined in a phased exploration concept to expand human presence to cis-lunar space and eventually to Mars. The development team is transitioning knowledge gained from the testing of the government-built Technology Development Unit (TDU) to the contractor-built Engineering Development Unit (EDU). A new laser-induced fluorescence diagnostic that is compatible with the testing of engineering hardware was developed to obtain data for thruster model validation in the lowest background pressure achievable. Prior to performing the test on the EDU, the team performed a functional checkout test of this new diagnostic using the TDU. In addition to providing a checkout of the diagnostic, this test provided data that can be correlated to electron mobility for comparison to the EDU at a later date. A number of technical challenges related to large test facilities and interfacing with engineering hardware were overcome while implementing the new laser diagnostic system. The initial data set was in good agreement with prior optical and non-optical diagnostics data collected on the TDU thrusters. This data set also revealed the spatial origin of high angle ions that have been of concern for spacecraft integration.

Huang, Wensheng↗

NASA NTRS2018

Ion Velocity in the Discharge Channel and Near-Field of the HERMeS Hall Thruster

NASA is continuing the development of a 12.5-kW Hall thruster system, which is baselined in a phased exploration concept to expand human presence to cis-lunar space and eventually to Mars. The development team is transitioning knowledge gained from the testing of the government-built Technology Development Unit (TDU) to the contractor-built Engineering Development Unit (EDU). A new laser-induced fluorescence diagnostic that is compatible with the testing of engineering hardware was developed to obtain data for thruster model validation in the lowest background pressure achievable. Prior to performing the test on the EDU, the team performed a functional checkout test of this new diagnostic using the TDU. In addition to providing a checkout of the diagnostic, this test provided data that can be correlated to electron mobility for comparison to the EDU at a later date. A number of technical challenges related to large test facilities and interfacing with engineering hardware were overcome while implementing the new laser diagnostic system. The initial data set was in good agreement with prior optical and non-optical diagnostics data collected on the TDU thrusters. This data set also revealed the spatial origin of high angle ions that have been of concern for spacecraft integration.

Huang, Wensheng↗

NASA NTRS2018

Ion Velocity in the Discharge Channel and Near-Field of the HERMeS Hall Thruster

NASA is continuing the development of a 12.5-kW Hall thruster system, which is baselined in a phased exploration concept to expand human presence to cis-lunar space and eventually to Mars. The development team is transitioning knowledge gained from the testing of the government-built Technology Development Unit (TDU) to the contractor-built Engineering Development Unit (EDU). A new laser-induced fluorescence diagnostic that is compatible with the testing of engineering hardware was developed to obtain data for thruster model validation in the lowest background pressure achievable. Prior to performing the test on the EDU, the team performed a functional checkout test of this new diagnostic using the TDU. In addition to providing a checkout of the diagnostic, this test provided data that can be correlated to electron mobility for comparison to the EDU at a later date. A number of technical challenges related to large test facilities and interfacing with engineering hardware were overcome while implementing the new laser diagnostic system. The initial data set was in good agreement with prior optical and non-optical diagnostics data collected on the TDU thrusters. This data set also revealed the spatial origin of high angle ions that have been of concern for spacecraft integration.

Huang, Wensheng↗

DOE OSTI2023

A Review of Physics-Informed Machine Learning in Fluid Mechanics

Physics-informed machine-learning (PIML) enables the integration of domain knowledge with machine learning (ML) algorithms, which results in higher data efficiency and more stable predictions. This provides opportunities for augmenting—and even replacing—high-fidelity numerical simulations of complex turbulent flows, which are often expensive due to the requirement of high temporal and spatial resolution. In this review, we (i) provide an introduction and historical perspective of ML methods, in particular neural networks (NN), (ii) examine existing PIML applications to fluid mechanics problems, especially in complex high Reynolds number flows, (iii) demonstrate the utility of PIML techniques through a case study, and (iv) discuss the challenges and opportunities of developing PIML for fluid mechanics.

42 ENGINEERING↗

NASA NTRS2013

Towards a Decision Support System for Space Flight Operations

The Mission Operations Directorate (MOD) at the Johnson Space Center (JSC) has put in place a Model Based Systems Engineering (MBSE) technological framework for the development and execution of the Flight Production Process (FPP). This framework has provided much added value and return on investment to date. This paper describes a vision for a model based Decision Support System (DSS) for the development and execution of the FPP and its design and development process. The envisioned system extends the existing MBSE methodology and technological framework which is currently in use. The MBSE technological framework currently in place enables the systematic collection and integration of data required for building an FPP model for a diverse set of missions. This framework includes the technology, people and processes required for rapid development of architectural artifacts. It is used to build a feasible FPP model for the first flight of spacecraft and for recurrent flights throughout the life of the program. This model greatly enhances our ability to effectively engage with a new customer. It provides a preliminary work breakdown structure, data flow information and a master schedule based on its existing knowledge base. These artifacts are then refined and iterated upon with the customer for the development of a robust end-to-end, high-level integrated master schedule and its associated dependencies. The vision is to enhance this framework to enable its application for uncertainty management, decision support and optimization of the design and execution of the FPP by the program. Furthermore, this enhanced framework will enable the agile response and redesign of the FPP based on observed system behavior. The discrepancy of the anticipated system behavior and the observed behavior may be due to the processing of tasks internally, or due to external factors such as changes in program requirements or conditions associated with other organizations that are outside of MOD. The paper provides a roadmap for the three increments of this vision. These increments include (1) hardware and software system components and interfaces with the NASA ground system, (2) uncertainty management and (3) re-planning and automated execution. Each of these increments provide value independently; but some may also enable building of a subsequent increment.

Meshkat, Leila↗

NASA NTRS2013

Towards a decision support system for space flight operations

The Mission Operations Directorate (MOD) at the Johnson Space Center (JSC) has put in place a Model Based Systems Engineering (MBSE) technological framework for the development and execution of the Flight Production Process (FPP). This framework has provided much added value and return on investment to date. This paper describes a vision for a model based Decision Support System (DSS) for the development and execution of the FPP and its design and development process. The envisioned system extends the existing MBSE methodology and technological framework which is currently in use. The MBSE technological framework currently in place enables the systematic collection and integration of data required for building an FPP model for a diverse set of missions. This framework includes the technology, people and processes required for rapid development of architectural artifacts. It is used to build a feasible FPP model for the first flight of spacecraft and for recurrent flights throughout the life of the program. This model greatly enhances our ability to effectively engage with a new customer. It provides a preliminary work breakdown structure, data flow information and a master schedule based on its existing knowledge base. These artifacts are then refined and iterated upon with the customer for the development of a robust end-to-end, high-level integrated master schedule and its associated dependencies. The vision is to enhance this framework to enable its application for uncertainty management, decision support and optimization of the design and execution of the FPP by the program. Furthermore, this enhanced framework will enable the agile response and redesign of the FPP based on observed system behavior. The differences between the anticipated system behavior and the observed behavior may be due to the processing of tasks internally, or due to external factors such as changes in program requirements or conditions associated with other organizations that are outside of MOD. The paper provides a roadmap for the four increments of this vision. These increments include (1) the existing capabilities (2) hardware and software system components and interfaces with the NASA ground system, (3) uncertainty management and (4) re-planning and automated execution. Each of these increments provides value independently; but some may also enable building of a subsequent increment.

Ruszkowski, James↗

DOE OSTI2020

Fast-Spectrum Critical Assemblies with a Pb-HEU Core Surrounded by a Copper Reflector

The Department of Energy invests tens of millions of dollars each year to develop the next generation of nuclear engineering modeling & simulation (M&S) tools. These M&S tools are used to analyze advanced reactor designs and the safety of current nuclear operations. As computers become more powerful, we are able to enhance resolution in our calculations. This improved resolution is taking us to a point where the limitations of simulation capability are in the quality of data, including our ability to quantify the uncertainty and sensitivity of the data. In order to accurately model systems of interest, the industry must improve key nuclear data measurements and our confidence of how well we understand the data. Thus, M&S tools need evaluated and quality-assured experimental data for validation purposes. The International Criticality Safety Benchmark Evaluation Project (ICSBEP) compiles benchmark experiment data in a handbook that can be used by criticality safety engineers to validate computer codes and cross-section libraries at nuclear facilities. Both critical and subcritical experiments are included in the handbook. Figure 1 organizes all the benchmark evaluations that have been performed by the isotope of interest, in this case Pb, and the neutron energy within the system. Compared to other isotopes of interest for nuclear applications, there are few benchmark evaluations for Pb systems. This has caused the latest nuclear cross-section libraries to over/underestimate changes in the neutron population compared to experimental results. Therefore, this evaluation fills an important knowledge gap in benchmark evaluations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1994

Technology Benefit Estimator (T/BEST): User's Manual

The Technology Benefit Estimator (T/BEST) system is a formal method to assess advanced technologies and quantify the benefit contributions for prioritization. T/BEST may be used to provide guidelines to identify and prioritize high payoff research areas, help manage research and limited resources, show the link between advanced concepts and the bottom line, i.e., accrued benefit and value, and to communicate credibly the benefits of research. The T/BEST software computer program is specifically designed to estimating benefits, and benefit sensitivities, of introducing new technologies into existing propulsion systems. Key engine cycle, structural, fluid, mission and cost analysis modules are used to provide a framework for interfacing with advanced technologies. An open-ended, modular approach is used to allow for modification and addition of both key and advanced technology modules. T/BEST has a hierarchical framework that yields varying levels of benefit estimation accuracy that are dependent on the degree of input detail available. This hierarchical feature permits rapid estimation of technology benefits even when the technology is at the conceptual stage. As knowledge of the technology details increases the accuracy of the benefit analysis increases. Included in T/BEST's framework are correlations developed from a statistical data base that is relied upon if there is insufficient information given in a particular area, e.g., fuel capacity or aircraft landing weight. Statistical predictions are not required if these data are specified in the mission requirements. The engine cycle, structural fluid, cost, noise, and emissions analyses interact with the default or user material and component libraries to yield estimates of specific global benefits: range, speed, thrust, capacity, component life, noise, emissions, specific fuel consumption, component and engine weights, pre-certification test, mission performance engine cost, direct operating cost, life cycle cost, manufacturing cost, development cost, risk, and development time. Currently, T/BEST operates on stand-alone or networked workstations, and uses a UNIX shell or script to control the operation of interfaced FORTRAN based analyses. T/BEST's interface structure works equally well with non-FORTRAN or mixed software analysis. This interface structure is designed to maintain the integrity of the expert's analyses by interfacing with expert's existing input and output files. Parameter input and output data (e.g., number of blades, hub diameters, etc.) are passed via T/BEST's neutral file, while copious data (e.g., finite element models, profiles, etc.) are passed via file pointers that point to the expert's analyses output files. In order to make the communications between the T/BEST's neutral file and attached analyses codes simple, only two software commands, PUT and GET, are required. This simplicity permits easy access to all input and output variables contained within the neutral file. Both public domain and proprietary analyses codes may be attached with a minimal amount of effort, while maintaining full data and analysis integrity, and security. T/BESt's sotware framework, status, beginner-to-expert operation, interface architecture, analysis module addition, and key analysis modules are discussed. Representative examples of T/BEST benefit analyses are shown.

Generazio, Edward R.↗

NASA NTRS2012

Advanced Aero-Propulsive Mid-Lift-to-Drag Ratio Entry Vehicle for Future Exploration Missions

NASA is currently looking well into the future toward realizing Exploration mission possibilities to destinations including the Earth-Moon Lagrange points, Near-Earth Asteroids (NEAs) and the Moon. These are stepping stones to our ultimate destination Mars. New ideas will be required to conquer the significant challenges that await us, some just conceptions and others beginning to be realized. Bringing these ideas to fruition and enabling further expansion into space will require varying degrees of change, from engineering and integration approaches used in spacecraft design and operations, to high-level architectural capabilities bounded only by the limits of our ideas. The most profound change will be realized by paradigm change, thus enabling our ultimate goals to be achieved. Inherent to achieving these goals, higher entry, descent, and landing (EDL) performance has been identified as a high priority. Increased EDL performance will be enabled by highly-capable thermal protection systems (TPS), the ability to deliver larger and heavier payloads, increased surface access, and tighter landing footprints to accommodate multiple asset, single-site staging. In addition, realizing reduced cost access to space will demand more efficient approaches and reusable launch vehicle systems. Current operational spacecraft and launch vehicles do not incorporate the technologies required for these far-reaching missions and goals, nor what is needed to achieve the desired launch vehicle cost savings. To facilitate these missions and provide for safe and more reliable capabilities, NASA and its partners will need to make ideas reality by gaining knowledge through the design, development, manufacturing, implementation and flight testing of robotic and human spacecraft. To accomplish these goals, an approach is recommended for integrated development and implementation of three paradigm-shifting capabilities into an advanced entry vehicle system with additional application to launch vehicle stage return, thus making ideas reality. These paradigm shifts include the technology maturation of advanced flexible thermal protection materials onto mid lift-to-drag ratio entry vehicles, the development of integrated supersonic aero-propulsive maneuvering, and the implementation of advanced asymmetric launch shrouds. These paradigms have significant overlap with launch vehicle stage return already being developed by the Air Force and several commercial space efforts. Completing the realization of these combined paradigms holds the key to a high-performing entry vehicle system capability that fully leverages multiple technology benefits to accomplish NASA's Exploration missions to atmospheric planetary destinations.

Campbell, C. H.↗

NASA NTRS2008

Engineering America's Current and Future Space Transportation Systems: 50 Years of Systems Engineering Innovation for Sustainable Exploration

Over the past 50 years, the National Aeronautics and Space Administration (NASA) has delivered space transportation solutions for America's complex missions, ranging from scientific payloads that expand knowledge, such as the Hubble Space Telescope, to astronauts and lunar rovers destined for voyages to the Moon. Currently, the venerable Space Shuttle, which has been in service since 1981, provides the United States' (U.S.) capability for both crew and heavy cargo to low-Earth orbit to' construct the International Space Station, before the Shuttle is retired in 2010. In the next decade, NASA will replace this system with a duo of launch vehicles: the Ares I Crew Launch Vehicle and the Ares V Cargo Launch Vehicle (Figure 1). The goals for this new system include increased safety and reliability coupled with lower operations costs that promote sustainable space exploration for decades to come. The Ares I will loft the Orion Crew Exploration Vehicle, while the heavy-lift Ares V will carry the Altair Lunar Lander and the equipment and supplies needed to construct a lunar outpost for a new generation of human and robotic space pioneers. This paper will provide details of the in-house systems engineering and vehicle integration work now being performed for the Ares I and planned for the Ares V. It will give an overview of the Ares I system-level test activities, such as the ground vibration testing that will be conducted in the Marshall Center's Dynamic Test Stand to verify the integrated vehicle stack's structural integrity and to validate computer modeling and simulation (Figure 2), as well as the main propulsion test article analysis to be conducted in the Static Test Stand. These activities also will help prove and refine mission concepts of operation, while supporting the spectrum of design and development work being performed by Marshall's Engineering Directorate, ranging from launch vehicles and lunar rovers to scientific spacecraft and associated experiments. Ultimately, fielding a robust space transportation solution that will carry international explorers and essential payloads will pave the way for a new century of scientific discovery beyond planet Earth.

Dmbacher, Daniel L.↗

DOE OSTI2025

A Software/Hardware Framework for Efficient and Safe Emergency Response in Post-Crash Scenarios of Battery Electric Vehicles

The adoption rate of battery electric vehicles (EVs) is rapidly increasing. Electric vehicles differ significantly from conventional internal combustion engine vehicles and vary widely across different manufacturers. Emergency responders (ERs) and recovery personnel may have less experience with EVs and lack timely access to critical information such as the extent of the stranded energy present, high-voltage safety hazards, and post-crash handling procedures in a user-friendly manner. This paper presents a software/hardware interactive tool named Electric Vehicle Information for Incident Response Solutions (EVIRS) to aid in the quick access to emergency response and recovery information. The current prototype of EVIRS identifies EVs using the VIN or Make, Model, and Year, and offers several useful features for ERs and recovery personnel. These features include integration and easy access to emergency response procedures tailored to an identified EV, vehicle structural schematics, the quick identification of battery pack specifications, and more. For EVs that are not severely damaged, EVIRS can perform calculations to estimate stranded energy in the EV’s battery and discharge time for various power loads using either EV dashboard information or operational data accessed through the CAN interface. Knowledge of this information may be helpful in the post-crash handling, management, and storage of an EV. The functionality and accuracy of EVIRS were demonstrated through laboratory tests using a 2021 Ford Mach-E and associated data acquisition system. The results indicated that when the remaining driving range was used as an input, EVIRS was able to estimate the pack voltage with an error of less than 3 V. Conversely, when pack voltage was used as an input, the estimated state of charge (SOC) error was less than 5% within the range of 30–90% SOC. Additionally, other features, such as retrieving emergency response guides for identified EVs and accessing lessons learned from archived incidents, have been successfully demonstrated through EVIRS for quick access. EVIRS can be a valuable tool for emergency responders and recovery personnel, both in action and during offline training, by providing crucial information related to assessing EV/battery safety risks, appropriate handling, de-energizing, transport, and storage in an integrated and user-friendly manner.

25 ENERGY STORAGE↗

DOE OSTI2022

Cyber-Resilient Distributed Autonomous Energy Grid

The aim of Cyber-Resilient Distributed Autonomous Grid research initiative is to advance fundamental science and engineering approaches for cyber-resilient design, control, and operation of a distributed, highly interconnected, and autonomous energy grid of the future. From increasing penetration of DERs, smart homes and building with highly controllable loads at the distribution layer, to the interconnection of bulk renewables at the transmission layer the fundamental nature of the energy grid is changing, and these changes are enabled by rapid increase in dependence on the communication infrastructure and independent third parties for control and operation of the grid. NREL's Integrated Energy Pathways critical objective correctly identifies that there are fundamental cyber-resilience challenges inherent in the grid's evolution, and this effort is establishing strong and novel integrated cyber-resilience framework and approaches to address these new and fundamental challenges.

controls↗

NASA NTRS2010

Modeling Flight: The Role of Dynamically Scaled Free-Flight Models in Support of NASA's Aerospace Programs

The state of the art in aeronautical engineering has been continually accelerated by the development of advanced analysis and design tools. Used in the early design stages for aircraft and spacecraft, these methods have provided a fundamental understanding of physical phenomena and enabled designers to predict and analyze critical characteristics of new vehicles, including the capability to control or modify unsatisfactory behavior. For example, the relatively recent emergence and routine use of extremely powerful digital computer hardware and software has had a major impact on design capabilities and procedures. Sophisticated new airflow measurement and visualization systems permit the analyst to conduct micro- and macro-studies of properties within flow fields on and off the surfaces of models in advanced wind tunnels. Trade studies of the most efficient geometrical shapes for aircraft can be conducted with blazing speed within a broad scope of integrated technical disciplines, and the use of sophisticated piloted simulators in the vehicle development process permits the most important segment of operations the human pilot to make early assessments of the acceptability of the vehicle for its intended mission. Knowledgeable applications of these tools of the trade dramatically reduce risk and redesign, and increase the marketability and safety of new aerospace vehicles. Arguably, one of the more viable and valuable design tools since the advent of flight has been testing of subscale models. As used herein, the term "model" refers to a physical article used in experimental analyses of a larger full-scale vehicle. The reader is probably aware that many other forms of mathematical and computer-based models are also used in aerospace design; however, such topics are beyond the intended scope of this document. Model aircraft have always been a source of fascination, inspiration, and recreation for humans since the earliest days of flight. Within the scientific community, Leonardo da Vinci, George Cayley, and the Wright brothers are examples of early aviation pioneers who frequently used models during their scientific efforts to understand and develop flying machines. Progress in the technology associated with model testing in worldwide applications has firmly established model aircraft as a key element in new aerospace research and development programs. Models are now routinely used in many applications and roles, including aerodynamic data gathering in wind tunnel investigations for the analysis of full-scale aircraft designs, proof-of-concept demonstrators for radical aeronautical concepts, and problem-solving exercises for vehicles already in production. The most critical contributions of aerospace models are to provide confidence and risk reduction for new designs and to enhance the safety and efficiency of existing configurations.

Chambers, Joseph↗

DOE OSTI2020

Emerging materials intelligence ecosystems propelled by machine learning

We report that the age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human-machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human-machine partnerships for fast and efficient virtual materials screening, development and discovery.

36 MATERIALS SCIENCE↗

DOE OSTI2020

HIDES: Hybrid Intrusion Detector for Energy Systems

The establishment of a resilient electric grid accompanied by a secure communications network is an ongoing battle as advanced persistent threats continue to exploit existing vulnerabilities in legacy supervisory control and data acquisition system (SCADA) infrastructure. Traditional intrusion detection systems (IDSs) lack consistent performance because of the continuously evolving attack surface of SCADA systems. These shortcomings can be overcome by integrating logical system behavior, protocol-specific knowledge, and data-based learning to develop a comprehensive IDS solution. In this paper, we present a Hybrid Intrusion Detector for Energy Systems by integrating a network-based IDS, state-of-the-art machine learning-based IDS, and model-based IDS to detect unknown and stealthy cyberattacks targeting the SCADA networks. The proposed IDS uses synchrophasor measurements and cyber logs to learn patterns of different scenarios based on spatiotemporal behaviors of power systems. As a proof of concept, we implement and validate the proposed IDS by leveraging resources available at the National Renewable Energy Laboratory's Energy Systems Integration Facility test bed. Experimental results show promising performance in detecting cyberattacks while providing realtime visualization of power system measurements and cyber logs.

machine-learning intrusion detection system↗

NASA NTRS2017

MSRR Rack Materials Science Research Rack

The Materials Science Research Rack (MSRR) is a research facility developed under a cooperative research agreement between NASA and the European Space Agency (ESA) for materials science investigations on the International Space Station (ISS). The MSRR is managed at the Marshall Space Flight Center (MSFC) in Huntsville, AL. The MSRR facility subsystems were manufactured by Teledyne Brown Engineering (TBE) and integrated with the ESA/EADS-Astrium developed Materials Science Laboratory (MSL) at the MSFC Space Station Integration and Test Facility (SSITF) as part of the Systems Development Operations Support (SDOS) contract. MSRR was launched on STS-128 in August 2009, and is currently installed in the U. S. Destiny Laboratory Module on the ISS. Materials science is an integral part of developing new, safer, stronger, more durable materials for use throughout everyday life. The goal of studying materials processing in space is to develop a better understanding of the chemical and physical mechanisms involved, and how they differ in the microgravity environment of space. To that end, the MSRR accommodates advanced investigations in the microgravity environment of the ISS for basic materials science research in areas such as solidification of metals and alloys. MSRR allows for the study of a variety of materials including metals, ceramics, semiconductor crystals, and glasses. Materials science research benefits from the microgravity environment of space, where the researcher can better isolate chemical and thermal properties of materials from the effects of gravity. With this knowledge, reliable predictions can be made about the conditions required on Earth to achieve improved materials. MSRR is a highly automated facility with a modular design capable of supporting multiple types of investigations. Currently the NASA-provided Rack Support Subsystem provides services (power, thermal control, vacuum access, and command and data handling) to the ESA developed Materials Science Laboratory (MSL) which accommodates interchangeable Furnace Inserts (FI). Two ESA-developed FIs are presently available on the ISS: the Low Gradient Furnace (LGF) and the Solidification and Quenching Furnace (SQF). Sample-Cartridge Assemblies (SCAs), each containing one or more material samples, are installed in the FI by the crew and can be processed at temperatures up to 1400 C. Once an SCA is installed, the experiment can be run by automatic command or science conducted via telemetry commands from the ground. This facility is available to support materials science investigations through programs such as the US National Laboratory, Technology Development, NASA Research Announcements, and others. TBE and MSFC are currently developing NASA Sample Cartridge Assemblies (SCA's) with a planned availability for launch in 2017.

Reagan, Shawn↗