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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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The processor which drives for the RANGE software allows the user to analyze LANDSAT data containing pasture and rangeland. Analysis includes mapping, generating statistics, calculating vegetative indexes, and plotting vegetative indexes. Routines for using the processor are given. A flow diagram is included.
During the Summer 2020 session, I worked with intern Destani S. Van Arsdalen of EGS Software. Together, we co-created a tool to aid the dynamic investigation, updated over time,of the security compliance of LCS COTS and open source software. We originally planned touse spreadsheet software for management and analysis, but through this exploratoryproject, chose to use Python and JSON after receiving feedback on our project’s current anddesired capabilities at that time.At first, the project was solely designed to help on-board new COTS software, based on aquestionnaire that could be filled out for each software package. This, combined with usingthe spreadsheet application’s web-query capabilities to fetch information from the NVD,allowed presentation and analytics cells to automatically populate as elements of themanually-filled questionnaire changed. While this system was promising, we decided tochange technologies for a few reasons. In the spreadsheet, single cells could not hold complexdata like arrays and objects. The automatic population of cells and dynamic updates made itdifficult to manage and add new features. And finally, it had limited extensibility sinceadding new software required significant understanding of how both the spreadsheet wasconstructed, and the more obscure, proprietary scripting languages packaged with it.The pivot to a standard computer science database language of JSON, aided by thescripting capabilities of Python, greatly helped to improve the project’s functionality. First,and most importantly, the script’s import and analysis of database data is easilyreproducible. Additional data analysis can be modularly added without requiringmodification of the script and is capable of routine scheduling. The revised process can besplit into three parts. First, the conversion of LCS asset and software documentation into theJSON hierarchical database format. Second, the merging of this database with the NVD,forming a new data structure, using CPEs of the CVE object as a linking element betweenthem. And third, the automatically performed analytics and analysis of the combined data,in a modular and extensible format, to produce better informed business decisions. The outputted graphs, for example, are automatically generated by the Python script inconnection with the combined database. This allows updated graphs and any analytics to be re-rendered automatically following updates to the LCS’s initial asset documentation. Afinal report can then be programmatically and easily constructed from these sources to allow fully reproducible metrics for heavily evidenced risk management decisions.
A document consisting mostly of lecture slides presents overviews of artificial-intelligence-based control methods now under development for application to robotic aircraft [called Unmanned Aerial Vehicles (UAVs) in the paper] and spacecraft and to the next generation of flight controllers for piloted aircraft. Following brief introductory remarks, the paper presents background information on intelligent control, including basic characteristics defining intelligent systems and intelligent control and the concept of levels of intelligent control. Next, the paper addresses several concepts in intelligent flight control. The document ends with some concluding remarks, including statements to the effect that (1) intelligent control architectures can guarantee stability of inner control loops and (2) for UAVs, intelligent control provides a robust way to accommodate an outer-loop control architecture for planning and/or related purposes.
This report documents the calculation of the General Aviation (GA) non-airport crash density for Department of Energy (DOE) sites and power generating facilities licensed by the Nuclear Regulatory Commission (NRC) using the methodology outlined in DOE-STD-3014-2006, Accident Analysis for Aircraft Crash Into Hazardous Facilities. The results of this report will be used by the American Nuclear Society (ANS) for the development of ANS standard 2.36, Accident Analysis for Aircraft Crash into Reactor and Nonreactor Nuclear Facilities.
The Miniature Infrared Data Acquisition and Telemetry (MIRDAT) Phase 1 study was performed to determine the technical and commercial feasibility of producing a miniaturized electro-optical telemetry system. This system acquires and transmits experimental data from aircraft scale models for realtime monitoring in wind tunnels. During the Phase 1 study, miniature prototype MIRDAT telemetry devices were constructed, successfully tested in the laboratory and delivered to the user for wind tunnel testing. A search was conducted for commercially available components and advanced hybrid techniques to further miniaturize the system during Phase 2 development. A design specification was generated from laboratory testing, user requirements and discussions with component manufacturers. Finally, a preliminary design of the proposed MIRDAT system was documented for Phase 2 development.
The Ion Beam Textured and Coated Surfaces Experiment (IBEX) was designated S1003 on the Long Duration Exposure Facility (LDEF) at a location of 98 degrees relative to the ram direction. Thirty-six diverse materials were exposed to the micrometeoroid (and some debris) environment for 5.8 years. Optical property measurements indicated no changes for almost all of the materials except S-13G, Kapton, and Kapton-coated surfaces, and these changes can be explained by other environmental effects. From the predicted micrometeoroid flux of NASA SP-8013, no changes in optical properties of the surfaces due to micrometeoroids were expected. However, there were hypervelocity impacts on the various diverse materials flown on IBEX. The characteristics of these craters were documented using scanning electron microscopy (SEM) and are presented. Interest in placing large solar concentrator/solar dynamic systems in space for power generation has again brought up a concern for maintaining the integrity of the optical properties of highly specular reflecting surfaces in the near-Earth space environment. It has been shown that highly reflective polished metals and thin film coatings degrade when exposed to simulated micrometeoroids in the laboratory. At LeRC, a shock tube was used to simulate the phenomenon of micrometeoroid optical properties of surfaces exposed to this impact were then evaluated. A calibrated sensor, 2000 A Al/stainless steel, was developed to not only detect the small size micrometeoroid environment, but also to evaluate the degradation of the optical properties of thin aluminum films in space. This sensor was flown on LDEF experiment S1003 and also on the OSO 3 and SERT 2 satellites that were launched in 1967 and 1970, respectively. No changes in the optical properties of the highly reflective surface sensor on SERT 2 were measured during 20 years in space. The results, as determined by the accuracy of the sensor, indicate that a highly reflective surface should lose less than 1 percent of its specular reflectance in near-Earth orbit during 20 years.
Here, the focus is on the design and configuration feasibility of the short spacer for the Space Station Program in its launch configuration. The product of this study is being used by Rockwell International (Rocketdyne Division) as they continue their design concept of the current short spacer configuration. It is anticipated that the launch loads will dominate the on-orbit loads and dictate the design configuration of the short spacer. At the present time, the on-orbit loads have not been generated. The structural analysis discussed herein is based on the transient events derived from the Space Transportation System (STS) Interface Control Document (ICD). The transient loading events consist of liftoff loads, landing loads, and emergency landing loads. The quasi-static loading events have been neglected, since the magnitude of the acceleration factors are lower than the transient acceleration factors. The normal mode analyses presented herein are based on the most feasible configurations with acceptable stress ranges.
The JPL Supervisory Telerobotics (STELER) Laboratory has developed and demonstrated a unique local-remote robot control architecture which enables management of intermittent communication bus latencies and delays such as those expected for ground-remote operation of Space Station robotic systems via the Tracking and Data Relay Satellite System (TDRSS) communication platform. The current work at JPL in this area has focused on enhancing the technologies and transferring the control architecture to hardware and software environments which are more compatible with projected ground and space operational environments. At the local site, the operator updates the remote worksite model using stereo video and a model overlay/fitting algorithm which outputs the location and orientation of the object in free space. That information is relayed to the robot User Macro Interface (UMI) to enable programming of the robot control macros. This capability runs on a single Silicon Graphics Inc. machine. The operator can employ either manual teleoperation, shared control, or supervised autonomous control to manipulate the intended object. The remote site controller, called the Modular Telerobot Task Execution System (MOTES), runs in a multi-processor VME environment and performs the task sequencing, task execution, trajectory generation, closed loop force/torque control, task parameter monitoring, and reflex action. This paper describes the new STELER architecture implementation, and also documents the results of the recent autonomous docking task execution using the local site and MOTES.
The intense heat from forest fires can generate explosive deep convective cloud systems that inject pollutants to high altitudes. Both satellite and high-altitude aircraft measurements have documented cases in which these pyrocumulonimbus clouds inject large amounts of smoke well into the stratosphere (Fromm and Servranckx 2003; Jost et al. 2004). This smoke can remain in the stratosphere, be transported large distances, and affect lower stratospheric chemistry. In addition recent in situ measurements in pyrocumulus updrafts have shown that the high concentrations of smoke particles have significant impacts on cloud microphysical properties. Very high droplet number densities result in delayed precipitation and may enhance lightning (Andrew et al. 2004). Presumably, the smoke particles will also lead to changes in the properties of anvil cirrus produces by the deep convection, with resulting influences on cloud radiative forcing. In situ sampling near the tops of mature pyrocumulonimbus is difficult due to the high altitude and violence of the storms. In this study, we use large eddy simulations (LES) with size-resolved microphysics to elucidate physical processes in pyrocumulonimbus clouds.
Across all fields of human-system interface design it is vital to posses a sound methodology dictating the constraints on the system based on the capabilities of the human user. These limitations may be based on strength, mobility, dexterity, cognitive ability, etc. and combinations thereof. Data collected in an isolated environment to determine, for example, maximal strength or maximal range of motion would indeed be adequate for establishing not-to-exceed type design limitations, however these restraints on the system may be excessive over what is basally needed. Resources may potentially be saved by having a technique to determine the minimum measurements a system must accommodate. This paper specifically deals with the creation of a novel methodology for establishing mobility requirements for a new generation of space suit design concepts. Historically, the Space Shuttle and the International Space Station vehicle and space hardware design requirements documents such as the Man-Systems Integration Standards and International Space Station Flight Crew Integration Standard explicitly stated that the designers should strive to provide the maximum joint range of motion capabilities exhibited by a minimally clothed human subject. In the course of developing the Human-Systems Integration Requirements (HSIR) for the new space exploration initiative (Constellation), an effort was made to redefine the mobility requirements in the interest of safety and cost. Systems designed for manned space exploration can receive compounded gains from simplified designs that are both initially less expensive to produce and lighter, thereby, cheaper to launch.
The NASA Glenn Research Center s (NASA Glenn) Advanced Noise Control Fan (ANCF) was developed in the early 1990s to provide a convenient test bed to measure and understand fan-generated acoustics, duct propagation, and radiation to the farfield. As part of a complete upgrade, current baseline and acoustic measurements were documented. Extensive in-duct, farfield acoustic, and flow field measurements are reported. This is a follow-on paper to documenting the operating description of the ANCF.
As interest in long duration effects of space habitation increases, understanding the behavior of model organisms living within the habitats engineered to fly them is vital for designing, validating, and interpreting future spaceflight studies. We previously conducted a detailed phenotypic analysis of mouse behavior during long duration (33-day) spaceflight in the NASA Rodent Habitat (Rodent Research-1; RR1 mission). Notably, we documented the emergence within the 8-10 days of launch of spontaneous ambulatory behavior in the form of circling or ‘race-tracking’ behavior in spaceflight but not in an identical ground control condition. Circling is unique to the NASA RH not having been reported to occur in other mouse habitats that provide limited opportunity for grasping, and/or are characterized by smaller habitable volumes viz., Italian Mice Drawer System (MDS) flown on ISS; Russian Block Obespecheniya Soderzhaniya (BOS) flown on Bion M-1, or JAXA Habitat Cage Unit (HCU) flown in the ISS Kibo module. Over time, circling became the primary dark cycle activity of FLT mice, occurring in individuals then as a coordinated group activity. Here we discuss possible interpretations of circling behavior including: (1) Stereotypic or abnormal repetitive behaviors (ARBs) that are unvarying, and apparently functionless behavior patterns documented numerous species in laboratory and zoo settings, possibility related to insufficient environmental enrichment and stress, (2) Rewarding effects of physical activity that are well-documented in terrestrial studies of rodents given the opportunity to run in wheels, and (3) Vestibular self-stimulation, i.e., the generation biologically-relevant amounts of vestibular sensory input to reduce the effects of microgravity. Affording mice the opportunity to grab and run in the RH resembles physical activities that the crew participate in routinely. Our approach is yielding an interesting analogue for better understanding human responses to spaceflight, and providing the opportunity to begin to address how physical movement influences responses to microgravity.
The Phase change material-based actively-tunable filter (P-ACTIVE, Dr. Hyun Jung Kim, the Principal Investigator / PI) project is a NASA Langley Research Center (LaRC)-led research endeavor that began as a NASA CIF (Center Innovation Fund) project in 2019. The goal of P-ACTIVE is to develop all-solid-state tunable optical filters that can serve as low-Size, Weight, and Power (SWaP) replacements to the ubiquitous filter wheel technology current in use, and serve as a starting point for completely new optical component technologies. By using materials commonly used in electronic memory devices, namely chalcogenide phase-change materials (PCMs), in conjunction with nanostructured surfaces with exotic, tailored optical responses (“metasurfaces”), the P-ACTIVE filters are able to tune their optical transmission/reflection behavior in real-time. Because of this new functionality, P-ACTIVE filters are highlighted for NASA science and space programs such as next generation Lidar, landing system (SLS) thermal monitoring, thermal imaging, imaging spectroscopy, and disaster monitoring (e.g., volcanic eruption observations from SmallSats). This document intends to serve as a compendium of the P-ACTIVE project. We begin by describing the project from background science and assume no prior knowledge of the subject. We then address experimental and theoretical results of P-ACTIVE device performance, as well as recent results obtained from the MISSE-14 mission related to space qualification of P-ACTIVE devices and constituent materials. We conclude with a prospective view of the P-ACTIVE technology, and discuss the potential for these filters and other similar metasurface devices to serve NASA’s mission.
Determining the adverse aerodynamic effects due to ice accretion often relies on dry-air wind-tunnel testing of artificial, or simulated, ice shapes. Recent developments in ice accretion documentation methods have yielded a laser-scanning capability that can measure highly three-dimensional features of ice accreted in icing wind tunnels. The objective of this paper was to evaluate the aerodynamic accuracy of ice-accretion simulations generated from laser-scan data. Ice-accretion tests were conducted in the NASA Icing Research Tunnel using an 18-inch chord, 2-D straight wing with NACA 23012 airfoil section. For six ice accretion cases, a 3-D laser scan was performed to document the ice geometry prior to the molding process. Aerodynamic performance testing was conducted at the University of Illinois low-speed wind tunnel at a Reynolds number of 1.8 x 10(exp 6) and a Mach number of 0.18 with an 18-inch chord NACA 23012 airfoil model that was designed to accommodate the artificial ice shapes. The ice-accretion molds were used to fabricate one set of artificial ice shapes from polyurethane castings. The laser-scan data were used to fabricate another set of artificial ice shapes using rapid prototype manufacturing such as stereolithography. The iced-airfoil results with both sets of artificial ice shapes were compared to evaluate the aerodynamic simulation accuracy of the laser-scan data. For four of the six ice-accretion cases, there was excellent agreement in the iced-airfoil aerodynamic performance between the casting and laser-scan based simulations. For example, typical differences in iced-airfoil maximum lift coefficient were less than 3% with corresponding differences in stall angle of approximately one degree or less. The aerodynamic simulation accuracy reported in this paper has demonstrated the combined accuracy of the laser-scan and rapid-prototype manufacturing approach to simulating ice accretion for a NACA 23012 airfoil. For several of the ice-accretion cases tested, the aerodynamics is known to depend upon the small, three dimensional features of the ice. These data show that the laser-scan and rapid-prototype manufacturing approach is capable of replicating these ice features within the reported accuracies of the laser-scan measurement and rapid-prototyping method; thus providing a new capability for high-fidelity ice-accretion documentation and artificial ice-shape fabrication for icing research.
The Space Environment Test Division Post-Test Data Reduction Program processes data from test history tapes generated on the Flexible Data System in the Space Environment Simulation Laboratory at the National Aeronautics and Space Administration/Lyndon B. Johnson Space Center. The program reads the tape's data base records to retrieve the item directory conversion file, the item capture file and the process link file to determine the active parameters. The desired parameter names are read in by lead cards after which the periodic data records are read to determine parameter data level changes. The data is considered to be compressed rather than full sample rate. Tabulations and/or a tape for generating plots may be output.
The Fast Modular Reactor (FMR) is a 100-MW(thermal) gas-cooled fast reactor being developed by General Atomics Electromagnetic System with the goal of developing a FMR for flexible and dispatchable power to the U.S. electricity market in the mid-2030s. The conceptual design aims to develop and verify simplified design features. These include an inert helium gas coolant, pellet-loaded fuel rods, installations with air cooling as ultimate heat sink, and small and passive heat removal systems. The goal is to ensure the development of a safe, maintainable, cost-effective, and distributed nuclear energy-generating station. The baseline technologies selected to achieve this goal are a helium coolant that is an inert gas with no chemical reaction with structural components, not activated, single phase, enabling high-temperature operation and a high thermal efficiency Brayton cycle; conventional uranium dioxide (UO 2 ) fuel, which is the most widely used and well-known fuel material, capable of high burnup (100 MWd/kg) and a long fuel life; and silicon carbide composite (SiGA®) cladding and internal structures that are chemically inert in the helium environment, exceptionally radiation tolerant, and being derisked by accident tolerant fuel technology development. Further, the reactor was specifically designed with passive safety features, including high-temperature in-core materials and a reactor vessel cooling system consisting of cooling panels of naturally circulating water. The passive safety of the core was confirmed for the depressurized loss-of–forced cooling accident, which showed the peak cladding temperature at ~1600°C during the transient, which is below the current design limit of 1800°C. The conceptual design of the FMR has been conducted for the reactor system, vessel system, generator and turbomachine, instrumentation and control, residual heat removal system, plant service system, and containment, as well as pre-application licensing documents.
NREL's computational sciences center hosts the largest high performance computing (HPC) capabilities dedicated to energy research while functioning as a living laboratory for energy-efficient computing. NREL's HPC capabilities support the research needs of the Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE). In ten years of operation, HPC use in EERE-sponsored research has grown by a factor of 30, including work in electricity generation, energy efficiency, transportation, and energy system modeling. This paper analyzes this research portfolio, providing examples of individual use cases. The paper documents NREL's history of operating one of the world's most energy-efficient data centers while examining pathways to reduce economic and environmental impact beyond reduction of Power Usage Efficiency (PUE). This paper concludes by examining the unique opportunities created for accelerating improvements in data center efficiency created by combining an HPC system dedicated to energy research and a research program in energy-efficient computing.
RTX Technology Research Center (RTRC), together with Collins Aerospace (Collins) and Oak Ridge National Laboratory (ORNL) has developed an Artificial Intelligence (AI) / Machine Learning (ML) guided solution to advance the manufacturing and assembly of high performance and lightweight thermoplastic composite (TPC) aerospace products. The solution aims to lower risk, cost and lead time for induction heating based welding and consolidation processes for TPC structure. The cost and lead time of part and material specific process development for induction welding (IW) and induction consolidation will be reduced by replacing traditional empirical methods with optimization methods that merge AI/ML and physics-based process simulations and process experiments with sensing and controls. TPC-IW process development is empirical in nature, and uncertainties in material & process behavior exist near & far from the induction coil. Physics-based simulations can be leveraged directly for process optimization but can be too computationally expensive to run in high fidelity and real time to do robust process optimization. The key impact of successful TPC induction consolidation and welding is cost & lead time reduction for part & material specific consolidation and welding recipes. This is an enabler for more rapid deployment of TPC structures via joining assembly, which can reduce energy & cost intensive usage of autoclaves & ovens. The solution aimed to advance the U.S. Department of Energy’s interests in using thermoplastics and automation in composite manufacturing for improvement of products for existing markets via increased production speeds, reduced costs, and lowered use of energy. Welded TPC structures can offer significant weight & energy savings for high-value commercial aerospace & industrial applications compared to metal & thermoset composite structures assembled by mechanical fastening and/or adhesive bonding. The project was organized into two Budget Periods. Budget Period 1 (BP1) was 15 months and its goal was to perform ML process optimization framework development & deployment on lab-coupon aerostructure components. A Go/No-Go Review was performed at the end of BP1 to verify fulfilment of key tasks & milestones to justify a Go Decision to move into the next Budget Period. Budget Period 2 (BP2) was 12 months and its goal was the deployment of the ML framework for ML process optimization of pilot industrial scale aerostructure components. The overall project aim was to develop & demonstrate ML-enhanced modeling framework that learns process-property mapping from multiple data sources at different fidelities. During BP1, the team accomplished key tasks & milestones to demonstrate the concept of multi-source ML for TPC aerostructure consolidation and assembly. First, the team completed documentation of induction based TPC heating requirements including baseline metrics to compare measured results against. Next the team completed demonstration of data generation from physics-based simulations for ML surrogate model generation and demonstrated the integration of physics-based simulation data into multi-source AI/ML algorithms. In parallel, the team established the lab-coupon scale induction welding system and completed a process to label and reduce generated data from physics-based simulation and experiments for ML surrogate models to enable multi-source ML model training & testing. To complete BP1, the team integrated physics-based simulation data and experimental data into multi-source ML algorithms. This was based on the team completing ML deployment of the induction welding on a lab system at RTRC and AI/ML deployment on existing induction welding line at Collins. ORNL visited both Collins and RTRC sites to witness the TPC induction welding process. Then, ORNL designed and constructed a new version of their vision-based sensing system better adapted to acquire process signals of the TPC induction welding process for process anomaly and defect detection. In BP2, the team accomplished key tasks & milestones to scale up multi-source ML for TPC aerostructure consolidation and assembly from the lab-coupon scale to the pilot-industrial scale. In BP2, the team demonstrated real time anomaly & defect detection via experiments performed by ORNL & RTRC. The team completed ML-optimization heating trials for TPC induction consolidation at Collins, and the team confirmed pilot industrial scale experimental data from Collins was compatible with the developed ML pipeline from RTRC. The team completed sub-element scale ML process optimization demonstration at RTRC, where the team leveraged RTRC’s robotic TPC welding setup to de-risk the ML process optimization by performing ML analysis of recorded temperatures to account for complex part features. Then, the team applied its ML-derived control strategies and ML process optimization framework at Collins to the pilot-industrial scale on a demo skin-stiffener part representative of a nacelle aerostructure fan cowl section. The key innovation is the AI/ML framework enabling effective process development of high performance, lightweight, energy efficient TPCs for composite aircraft structures.