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At least 1,711 records · Page 95
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

SEXTANT - Station Explorer for X-ray Timing and Navigation Technology

The Station Explorer for X-ray Timing and Navigation Technology (SEXTANT) is a technology demonstration enhancement to the Neutron-star Interior Composition Explorer (NICER) mission, which is scheduled to launch in late 2016 and will be hosted as an externally attached payload on the International Space Station (ISS) via the ExPRESS Logistics Carrier (ELC). During NICER's 18-month baseline science mission to understand ultra-dense matter though observations of neutron stars in the soft X-ray band, SEXTANT will, for the first-time, demonstrate real-time, on-board X-ray pulsar navigation, which is a significant milestone in the quest to establish a GPS-like navigation capability that will be available throughout our Solar System and beyond. Along with NICER, SEXTANT has proceeded through Phase B, Mission Definition, and received numerous refinements in concept of operation, algorithms, flight software, ground system, and ground test capability. NICER/SEXTANT's Phase B work culminated in NASA's confirmation of NICER to Phase C, Design and Development, in March 2014. Recently, NICER/SEXTANT successfully passed its Critical Design Review and SEXTANT received continuation approval in September 2014. In this paper, we describe the X-ray pulsar navigation concept and provide a brief history of previous work, and then summarize the SEXTANT technology demonstration objective, hardware and software components, and development to date.

X-rays↗
NASA NTRSDate not supplied

NASA and Blue Origin’s Flight Assessment of Precision Landing Algorithms Computing Performance

NASA’s Safe and Precise Landing - Integrated Capabilities Evolution (SPLICE) project continues NASA’s work in the development and testing of technologies for Precision Landing and Hazard Avoidance (PL&HA). This paper presents results characterizing how SPLICE flight software utilizes the shared computing resources of the Descent Landing Computer (DLC), one of the PL&HA technologies under development. The SPLICE technologies are being tested as an integrated payload on Blue Origin’s New Shephard suborbital vehicle. The results presented in this paper are measured by applications running in and with the flight software both in flight, and in a high-fidelity Hardware-in-the-Loop (HWIL) simulation environment. Linux utilities to measure performance are also executed from the command line in the HWIL configuration. Performance measurements of the SPLICE workloads executing on the DLC provide insight on how efficiently the software is utilizing the DLC resources. Examples of how these measurements have guided improvements in the flight code are presented. In addition, the DLC uses a commercial processor as a surrogate for NASA’s High Performance Spaceflight Computing (HPSC) processor. This work provides insight on how an HPSC system may perform delivering PL&HA capabilities on a future mission. The measurements also can be used to infer architectural requirements for PL&HA capabilities, informing the HPSC project and other flight computer development efforts. Examples of the measurements collected include processor utilization, I/O bandwidth, cache and branch misses, and application profiles.

Precision Landing and Hazard Avoidance↗
NASA NTRSDate not supplied

NASA and Blue Origin’s Flight Assessment of Precision Landing Algorithms Computing Performance

NASA’s Safe and Precise Landing - Integrated Capabilities Evolution (SPLICE) project continues NASA’s work in the development and testing of technologies for Precision Landing and Hazard Avoidance (PL&HA). This paper presents results characterizing how SPLICE flight software utilizes the shared computing resources of the Descent Landing Computer (DLC), one of the PL&HA technologies under development. The SPLICE technologies are being tested as an integrated payload on Blue Origin’s New Shepard suborbital vehicle. The results presented in this paper are measured by applications running in and with the flight software both in flight, and in a high-fidelity Hardware-in-the-Loop (HWIL) simulation environment. Linux utilities to measure performance are also executed from the command line in the HWIL configuration. Performance measurements of the SPLICE workloads executing on the DLC provide insight on how efficiently the software is utilizing the DLC resources. Examples of how these measurements have guided improvements in the flight code are presented. In addition, the DLC uses a commercial processor as a surrogate for NASA’s High-Performance Spaceflight Computing (HPSC) processor. This work provides insight on how an HPSC system may perform delivering PL&HA capabilities on a future mission. The measurements also can be used to infer architectural requirements for PL&HA capabilities, informing the HPSC project and other flight computer development efforts. Examples of the measurements collected include processor utilization, I/O bandwidth, cache and branch misses, and application profiles.

Precision Landing↗
DOE OSTI2020

TURBOMOLE: Modular program suite for ab initio quantum-chemical and condensed-matter simulations

TURBOMOLE is a collaborative, multi-national software development project aiming to provide highly efficient and stable computational tools for quantum chemical simulations of molecules, clusters, periodic systems, and solutions. The TURBOMOLE software suite is optimized for widely available, inexpensive, and resource-efficient hardware such as multi-core workstations and small computer clusters. TURBOMOLE specializes in electronic structure methods with outstanding accuracy–cost ratio, such as density functional theory including local hybrids and the random phase approximation (RPA), GW-Bethe–Salpeter methods, second-order Møller–Plesset theory, and explicitly correlated coupled-cluster methods. TURBOMOLE is based on Gaussian basis sets and has been pivotal for the development of many fast and low-scaling algorithms in the past three decades, such as integral-direct methods, fast multipole methods, the resolution-of-the-identity approximation, imaginary frequency integration, Laplace transform, and pair natural orbital methods. This review focuses on recent additions to TURBOMOLE’s functionality, including excited-state methods, RPA and Green’s function methods, relativistic approaches, high-order molecular properties, solvation effects, and periodic systems. A variety of illustrative applications along with accuracy and timing data are discussed. Moreover, available interfaces to users as well as other software are summarized. TURBOMOLE’s current licensing, distribution, and support model are discussed, and an overview of TURBOMOLE’s development workflow is provided. Challenges such as communication and outreach, software infrastructure, and funding are highlighted.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1977

Attitude determination system for Seasat-A mission

The attitude determination (AD) system hardware of Seasat-A consists of two sun aspect sensors, two infrared (IR) horizon scanners, related electronics, telemetry, and science sensors. The earth horizon pulse signal sensing is accomplished by a variable thresholding method intended to self-adjust for earth radiance variations. The use of sun sensors rather than star trackers for yaw determination calls for estimation rather than measurement of yaw attitude about 50% of mission time. Features of the yaw interpolation algorithm include adaptability and automatic incorporation of time-varying confidence levels of various data sources. The performance accuracy of the AD system was assessed, where it is found that the most important error source is the variation in earth atmospheric radiance. Analysis of science instrument sensitivity to AD uncertainties was performed, and the most sensitive instrument, the scatterometer system, shows only gradual degradation of performance in the vicinity of the AD accuracy requirement.

Ohtakay, H.↗
NASA NTRS1992

Failure detection and fault management techniques for flush airdata sensing systems

Methods based on chi-squared analysis are presented for detecting system and individual-port failures in the high-angle-of-attack flush airdata sensing system on the NASA F-18 High Alpha Research Vehicle. The HI-FADS hardware is introduced, and the aerodynamic model describes measured pressure in terms of dynamic pressure, angle of attack, angle of sideslip, and static pressure. Chi-squared analysis is described in the presentation of the concept for failure detection and fault management which includes nominal, iteration, and fault-management modes. A matrix of pressure orifices arranged in concentric circles on the nose of the aircraft indicate the parameters which are applied to the regression algorithms. The sensing techniques are applied to the F-18 flight data, and two examples are given of the computed angle-of-attack time histories. The failure-detection and fault-management techniques permit the matrix to be multiply redundant, and the chi-squared analysis is shown to be useful in the detection of failures.

Whitmore, Stephen A.↗
NASA NTRS2017

Wearable Biosensor Monitor to Support Autonomous Crew Health and Readiness to Perform

For future human exploration missions, NASA needs a health monitoring system composed of hardware that is compact, fully interoperable with an integrated data management system, and requires minimal consumables. Such a system will be achieved through the integration of small, easy to use biomedical sensors that will have the ability to measure, store and transmit physiological parameters during operational and ambulatory activity. Since 2012, the Canadian Space Agency (CSA) has been active in funding the development of wearable biomonitoring sensors. The Astroskin is the first prototype and consists of a shirt-based garment and headband with embedded sensors, and associated software and technology that measure vital signs, sleep quality and activity level of the wearer. NASA and CSA have been collaborating since 2014 to test and validate this system in a lab environment at Ames Research Center and more recently in the Human Exploration Research Analog (HERA) located at Johnson Spaceflight Center. Specific objectives of the HERA study were: 1) to assess the performance of the Astroskin biosensor system for long-term health monitoring (24-hours) capabilities and during exercise as a measure of crew fitness; 2) to obtain crew feedback on comfort and usability of the Astroskin system; 3) to demonstrate performance of Bluetooth communication during real-time transmission and for verification of data in this environment; and 4) to obtain baseline data for further development of algorithms and tools that facilitate decision support for diagnosing and monitoring of a sick or injured crewmember. HERA Campaign 3 included four missions (each 30-days in duration) with four crewmembers assigned to each mission. A total of 9 men and 7 women participated in the Astroskin evaluation that included continuous physiological monitoring (24-hours) on mission days MD-11, MD1 (high workload), MD15 (low workload), MD19, MD29, and MD+7. Mission days 19 and 29 also included 30 minutes of sub-maximal exercise on a cycle ergometer. Following each 24-hour monitoring session crew physiological data were downloaded to laptops and each crewmember completed a 28 question survey on their experiences with the Astroskin hardware and software. This presentation will focus on lessons learned from the HERA missions. Specifically it will address Astroskin system performance in terms of data loss and data quality (no comparison to lab standard devices), wireless communication with the onboard mobile device, crew usability and comfort, and future development of a next generation biomonitoring system.

crew fitness↗
DOE OSTI2021

Real-time Simulation Framework for Hardware-in-the-Loop Testing of Multi-port Autonomous Reconfigurable Solar Power Plant (MARS)

Multiport autonomous reconfigurable solar power plant (MARS) has been proposed for integrated development of photovoltaic (PV) and energy storage system (ESS) that can connect to high-voltage direct current (HVdc) and alternating current (ac) transmission grid. To de-risk the development of this complex integrated system that consists of hundreds to thousands of power electronics modules, a controller hardware-in-the-loop (cHIL) test setup will be extremely beneficial. The cHIL testing can be used for evaluation of modules as well as the hierarchical control system in MARS. With the unique configuration of power electronics modules in MARS, it becomes necessary to develop custom-designed real-time simulation models in the cHIL setup in absence of off-the-shelf models. In this paper, high-fidelity dynamic model of MARS, control algorithms at the lower level, and required communication algorithms are developed and optimized for real-time performance in the cHIL setup. Real-time experimental results from the cHIL are provided.

Dong, Zerui↗
DOE OSTI2025

SWARM: Reimagining scientific workflow management systems in a distributed world

Modern scientific workflows process massive amounts of data from diverse instruments and sensors, leveraging geographically distributed, heterogeneous compute and storage resources—from leadership-class systems to edge devices—connected by high-performance networks. The diversity of resources introduces challenges in harnessing their full potential, with resilience issues arising across applications, system software, networks, storage, and hardware. Today, workflow management systems (WMS) coordinate the execution of computation and data management tasks across target resources. However, WMS’s centralized nature makes them vulnerable to faults and scalability issues that may result in failures of entire computational campaigns. In conclusion, this paper introduces a novel agentic framework for workflow management, fully distributing and decentralizing the WMS functions and modeling them as swarm intelligence agents infused with advanced artificial intelligence solutions and traditional distributed computing algorithms that can make coordinated decisions in the presence of failures of the underlying cyberinfrastructure.

Swarm intelligence↗
NASA NTRS2012

Variable Sampling Mapping

The performance of an optical system (for example, a telescope) is limited by the misalignments and manufacturing imperfections of the optical elements in the system. The impact of these misalignments and imperfections can be quantified by the phase variations imparted on light traveling through the system. Phase retrieval is a methodology for determining these variations. Phase retrieval uses images taken with the optical system and using a light source of known shape and characteristics. Unlike interferometric methods, which require an optical reference for comparison, and unlike Shack-Hartmann wavefront sensors that require special optical hardware at the optical system's exit pupil, phase retrieval is an in situ, image-based method for determining the phase variations of light at the system s exit pupil. Phase retrieval can be used both as an optical metrology tool (during fabrication of optical surfaces and assembly of optical systems) and as a sensor used in active, closed-loop control of an optical system, to optimize performance. One class of phase-retrieval algorithms is the iterative transform algorithm (ITA). ITAs estimate the phase variations by iteratively enforcing known constraints in the exit pupil and at the detector, determined from modeled or measured data. The Variable Sampling Mapping (VSM) technique is a new method for enforcing these constraints in ITAs. VSM is an open framework for addressing a wide range of issues that have previously been considered detrimental to high-accuracy phase retrieval, including undersampled images, broadband illumination, images taken at or near best focus, chromatic aberrations, jitter or vibration of the optical system or detector, and dead or noisy detector pixels. The VSM is a model-to-data mapping procedure. In VSM, fully sampled electric fields at multiple wavelengths are modeled inside the phase-retrieval algorithm, and then these fields are mapped to intensities on the light detector, using the properties of the detector and optical system, for comparison with measured data. Ultimately, this model-to-data mapping procedure enables a more robust and accurate way of incorporating the exit-pupil and image detector constraints, which are fundamental to the general class of ITA phase retrieval algorithms.

Smith, Jeffrey, S.↗
NASA NTRS2018

C++ Resource Intelligent Compilation for GPU Enabled Applications

We are nearing the limits of Moore's Law with current computing technology. As industries push for more performance from smaller systems, alternate methods of computation such as Graphics Processing Units (GPUs) should be considered. Many of these systems utilize the Compute Unified Device Architecture (CUDA) to give programmers access to individual compute elements of the GPU for general purpose computing tasks. Direct access to the GPU's parallel multi-core architecture enables highly efficient computation and can drastically reduce the time required for complex algorithms or data analysis. Of course not all systems have a CUDA-enabled device to leverage, and so applications must consider optional support for users with these devices. Resource Intelligent Compilation (RIC) addresses this situation by enabling GPU-based acceleration of existing applications without affecting users without GPUs. Resource Intelligent Compilation (RIC) creates C/C++ modules that can be compiled to create a standard CPU version or GPU accelerated version of a program, depending on hardware availability. This is accomplished through a toolbox of programming strategies based on features of the CUDA API. Using this toolbox, existing applications can be modified with ease to support GPU acceleration, and new applications can be generated with just a few simple modifications. All of this culminates in an accelerated application for users with the appropriate hardware, with no performance impact to standard systems. This memorandum presents all the important features involved in supporting and implementing RIC and an example of using RIC to accelerate an existing mathematical model, without removing support for standard users. Through this memorandum, NASA engineers can acquire a set of guidelines to follow for RIC-compliant development, seamlessly accelerating C/C++ applications.

GPU↗
NASA NTRSDate not supplied

TechEdSat-13: The First Flight of an AI/ML Neuromorphic Processor

Neuromorphic processors, inspired by the wiring of the brain, permit certain classes of Artificial Intelligence/Machine Learning (AI/ML) algorithms to run far more efficiently. Ultimately, such systems will make the small- and nano-satellite platforms even more useful in terms of greatly improved power, communication and internal data management. In addition, on-board processing of images and data will help to not only rapidly interpret the information, but also reduce the amount of data that needs to be transmitted to ground stations. This initial flight experiment uses the Intel/ Loihi processor combined with a custom interface board and three communication channels to run the AI/ML scripts. These will vary with increasing length and complexity during the course of the mission. The algorithms will use the (at first) limited sensor data to ‘learn’ - with comparisons to similar architecture in comparable ground experiments. Some of the applications for successor flights in the TES-n flight series include Cognitive Communications, whereby the overall communication system is optimized per overflight – by optimizing timing and data transmission functions. Lastly, the performance of the Loihi 14nm process technology will be monitored for performance in the LEO radiation environment, thus looking for induced hardware and software errors. This information will help guide future radiation protection techniques to extend the lifetime and overall utility. The TES-13 is a 3U nanosat successfully launched by the Virgin Orbit Launcher-1 on January 13, 2022, and will presage more flights and AI/ML applications to come.

TechEdSat↗
NASA NTRS2022

TechEdSat-13: The First Flight of a Neuromorphic Processor

Neuromorphic processors, inspired by the wiring of the brain, permit certain classes of Artificial Intelligence/Machine Learning (AI/ML) algorithms to run far more efficiently. Ultimately, such systems will make the small- and nano-satellite platforms even more useful in terms of greatly improved power, communication and internal data management. In addition, on-board processing of images and data will help to not only rapidly interpret the information, but also reduce the amount of data that needs to be transmitted to ground stations. This initial flight experiment uses the Intel/Loihi processor combined with a custom interface board and three communication channels to run the AI/ML scripts. These will vary with increasing length and complexity during the course of the mission. The algorithms will use the (at first) limited sensor data to ‘learn’ - with comparisons to similar architecture in comparable ground experiments. Some of the applications for successor flights in the TES-n flight series include Cognitive Communications, whereby the overall communication system is optimized per overflight – by optimizing timing and data transmission functions. Lastly, the performance of the Loihi 14nm process technology will be monitored for performance in the LEO radiation environment, thus looking for induced hardware and software errors. This information will help guide future radiation protection techniques to extend the lifetime and overall utility. The TES-13 is a 3U nanosat successfully launched by the Virgin Orbit Launcher-1 on January 13, 2022, and will presage more flights and AI/ML applications to come.

TechEdSat↗
NASA NTRS2014

Fuzzy Neuron: Method and Hardware Realization

This innovation represents a method by which single-to-multi-input, single-to-many-output system transfer functions can be estimated from input/output data sets. This innovation can be run in the background while a system is operating under other means (e.g., through human operator effort), or may be utilized offline using data sets created from observations of the estimated system. It utilizes a set of fuzzy membership functions spanning the input space for each input variable. Linear combiners associated with combinations of input membership functions are used to create the output(s) of the estimator. Coefficients are adjusted online through the use of learning algorithms.

Krasowski, Michael J.↗
DOE OSTI2021

Real-time nuclear activation detectors for measuring neutron angular distributions at the National Ignition Facility (invited)

The Real Time Nuclear Activation Detector (RTNAD) array at NIF measures the distribution of 14 MeV neutrons emitted by deuterium-tritium (DT) fueled inertial confinement fusion implosions. The uniformity of the neutron distribution is an important indication of implosion symmetry and DT shell integrity. The array consists of 48 LaBr 3 (Ce) crystal gamma-ray spectrometers mounted outside the NIF target chamber, which continuously monitor the slow decay of the 909 keV gamma-ray line from activated 89 Zr located in Zr cups surrounding each crystal. The measured decay rate dramatically increases during a DT implosion in proportion to the number of 14 MeV neutrons striking each Zr cup. The neutrons produce activated 89 Zr through an (n, 2n) reaction on 90 Zr, which is insensitive to low energy neutrons. The neutron flux along the detector line-of-sight at shot time is determined by extrapolating the fitted 909 keV decay curve back to shot time. Automatic analysis algorithms were developed to handle the non-stop data stream. The large number of detectors and the high statistical accuracy of the array enable the spherical harmonic modes of the neutron angular distribution to be measured up to L ≤ 4 to provide a better understanding of implosion dynamics. In addition, these data combined with measurements of the down-scattered neutrons can be used to derive fuel areal density distributions. This paper will describe the RTNAD hardware and analysis procedures.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Real-time Electromagnetic Transient Simulation of Multi-Terminal HVDC-AC Grids based on GPU

High-fidelity electromagnetic transient (EMT) simulation plays a critical role in understanding the dynamic behavior and fast transients involved in operation, control, and protection of Multi-Terminal dc (MTdc) grids. Here, this paper proposes a cost-effective high-performance real-time EMT simulation platform for large-scale cross-continental MTdc grids based on graphics processing unit (GPU). The proposed simulation platform: i) assembles detailed EMT models of all components within an MTdc-ac grid into a single platform. This setup provides a complete simulation solution to capture fast transient signals required for high-bandwidth controller design and protection studies without any compromise; ii) implements the first GPU-based simulation architecture and corresponding algorithms for MTdc-ac grids with real-time performance at scales of 1s; iii) is highly-efficient and balances the high utilization of GPU resources and low latency required for the simulation; and iv) outperforms the existing central processing unit (CPU)- or digital signal processor (DSP)/field-programmable gate array (FPGA)-based simulators in terms of its higher scalability on large-scale MTdc-ac grids and superior price-performance ratio on the hardware. Accuracy and performance of the proposed platform are evaluated with respect to the reference results from PSCAD/EMTDC environment.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Hierarchical Control of Megawatt-Scale Charging Stations for Electric Trucks with Distributed Energy Resources

Electrifying medium- and heavy-duty trucks is critical to decarbonizing the transportation sector. Energy needs of electric trucks will likely require megawatt-scale charging stations, which could significantly stress the electric distribution grid. Distributed energy resources (DER) can alleviate this stress and reduce charging costs with proper management. To that end, this work develops a hierarchical predictive control algorithm for future multi-port megawatt-scale charging stations that can provide real-time energy management for stations, decide charging rates, dispatch energy storage system (ESS), and provide grid voltage support. We integrate three algorithmic components: (i) an energy management optimization (EMO) that provides supervisory control to DER assets and charging loads at minute scale, (ii) a real-time energy management system (RT-EMS) that heuristically compensates for fast disturbances at sub-second scale, and (iii) a model predictive control (MPC)-based battery management system (BMS) that communicates future charging demands to the EMO, to manage the overall megawatt-scale site. Additionally, validation in a controller hardware-in-the-loop (CHIL) environment shows that the hierarchical controller can reduce the total energy consumption from the grid by approximately 28% compared to an uncontrolled case for the station configuration in this paper, without impacting charging time.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

ATHENA: Analytical Tool for Heterogeneous Neuromorphic Architectures

The ASC program seeks to use machine learning to improve efficiencies in its stockpile stewardship mission. Moreover, there is a growing market for technologies dedicated to accelerating AI workloads. Many of these emerging architectures promise to provide savings in energy efficiency, area, and latency when compared to traditional CPUs for these types of applications — neuromorphic analog and digital technologies provide both low-power and configurable acceleration of challenging artificial intelligence (AI) algorithms. If designed into a heterogeneous system with other accelerators and conventional compute nodes, these technologies have the potential to augment the capabilities of traditional High Performance Computing (HPC) platforms [5]. This expanded computation space requires not only a new approach to physics simulation, but the ability to evaluate and analyze next-generation architectures specialized for AI/ML workloads in both traditional HPC and embedded ND applications. Developing this capability will enable ASC to understand how this hardware performs in both HPC and ND environments, improve our ability to port our applications, guide the development of computing hardware, and inform vendor interactions, leading them toward solutions that address ASC’s unique requirements.

97 MATHEMATICS AND COMPUTING↗