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At least 1,459 records · Page 81

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NASA NTRS2000

Perl Tools for Automating Satellite Ground Systems

The freeware scripting language Pert offers many opportunities for automating satellite ground systems for new satellites as well as older, in situ systems. This paper describes a toolkit that has evolved from of the experiences gained by using Pert to automate the ground system for the Compton Gamma Ray Observatory (CGRO) and for automating some of the elements in the Earth Observing System Data and Operations System (EDOS) ground system at Goddard Space Flight Center (GSFC). CGRO is an older ground system that was forced to automate because of fund cuts. Three 8 hour shifts were cut back to one 8 hour shift, 7 days per week. EDOS supports a new mission called Terra, launched December 1999 that requires distribution and tracking of mission-critical reports throughout the world. Both of these ground systems use Pert scripts to process data and display it on the Internet as well as scripts to coordinate many of the other systems that make these ground systems work as a coherent whole. Another task called Automated Multimodal Trend Analysis System (AMTAS) is looking at technology for isolation and recovery of spacecraft problems. This effort has led to prototypes that seek to evaluate various tools and technology that meet at least some of the AMTAS goals. The tools, experiences, and lessons learned by implementing these systems are described here.

McLean, David↗

DOE OSTI2022

Projecting the Thermal Response in a HTGR-Type System during Conduction Cooldown Using Graph-Laplacian Based Machine Learning

Accurate prediction of an off-normal event in a nuclear reactor is dependent upon the availability of sensory data, reactor core physical condition, and understanding of the underlying phenomenon. This work presents a method to project the data from some discrete sensory locations to the overall reactor domain during conduction cooldown scenarios similar to High Temperature Gas-cooled Reactors (HTGRs). The existing models for conductive cooldown in a heterogeneous multi-body system, such as an assembly of prismatic blocks or pebble beds relies on knowledge of the thermal contact conductance, requiring significant knowledge of local thermal contacts and heat transport possibilities across those contacts. With a priori knowledge of bulk geometry features and some discrete sensors, a machine learning approach was devised. The presented work uses an experimental facility to mimic conduction cooldown with an assembly of 68 cylindrical rods initially heated to 1200 K. High-fidelity temperature data were collected using an infrared (IR) camera to provide training data to the model and validate the predicted temperature data. The machine learning approach used here first converts the macroscopic bulk geometry information into Graph-Laplacian, and then uses the eigenvectors of the Graph-Laplacian to develop Kernel functions. Support vector regression (SVR) was implemented on the obtained Kernels and used to predict the thermal response in a packed rod assembly during a conduction cooldown experiment. The usage of SVR modeling differs from most models today because of its representation of thermal coupling between rods in the core. When trained with thermographic data, the average normalized error is less than 2% over 400 s, during which temperatures of the assembly have dropped by more than 500 K. The rod temperature prediction performance was significantly better for rods in the interior of the assembly compared to those near the exterior, likely due to the model simplification of the surroundings.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Assurance by Design for Cyber Physical Data-Driven Systems

Currently, Cyber Physical Data-Driven Systems (CPDDS) employ machine learning for the classification, data fusion, and control of our nation’s infrastructure, such as the power grid, transportation networks (e.g., fuel distribution, air traffic control), and DoD long-duration collaborative autonomous platforms including unmanned underwater, ground, surface, space, and aerial systems. Many CPDDSs are system-of-systems that should be designed to communicate over disadvantaged networks. It is important to assure that the CPDDSs are resilient against physical and cyber threats by design. Additionally, their design should tolerate misclassification errors resulting from natural and/or adversarial distribution shifts within their data driven components. The all-domain nature of the problem of assuring the design of CPDDSs requires a multi-disciplinary perspective as outlined in this chapter.

Chikkagoudar, Satish↗

DOE OSTI2018

Quantum-accurate spectral neighbor analysis potential models for Ni-Mo binary alloys and fcc metals

In recent years, efficient interatomic potentials approaching the accuracy of density functional theory (DFT) calculations have been developed using rigorous atomic descriptors satisfying strict invariances, for example, for translation, rotation, permutation of homonuclear atoms, among others. In this work, we generalize the spectral neighbor analysis potential (SNAP) model to bcc-fcc binary alloy systems. We demonstrate that machine-learned SNAP models can yield significant improvements even over the well-established high-performing embedded atom method (EAM) and modified EAM potentials for fcc Cu and Ni. We also report on the development of a SNAP model for the fcc Ni-bcc Mo binary system by machine learning a carefully constructed large computed data set of elemental and intermetallic compounds. We demonstrate that this binary Ni-Mo SNAP model can achieve excellent agreement with experiments in the prediction of a Ni-Mo phase diagram as well as near-DFT accuracy in the prediction of many key properties, such as elastic constants, formation energies, melting points, etc., across the entire binary composition range. In contrast, the existing Ni-Mo EAM has significant errors in the prediction of the phase diagram and completely fails in binary compounds. Lastly, this paper provides a systematic model development process for multicomponent alloy systems, including an efficient procedure to optimize the hyperparameters in the model fitting, and paves the way for long-time large-scale simulations of such systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2026

Variational Optical Phase Learning on a Continuous-Variable Quantum Compiler

Quantum process learning is a fundamental primitive that draws inspiration from machine learning with the goal of better studying the dynamics of quantum systems. One approach to quantum process learning is quantum compilation, whereby an analog quantum operation is digitized by compiling it into a series of basic gates. While there has been significant focus on quantum compiling for discrete-variable systems, the continuous-variable (CV) framework has received comparatively less attention. We present an experimental implementation of a CV quantum compiler that uses two-mode squeezed light to learn a Gaussian unitary operation. We demonstrate the compiler by learning a parameterized linear phase unitary through the use of target and control phase unitaries to demonstrate a factor of 5.4 increase in the precision of the phase estimation and a 3.6-fold acceleration in the time-to-solution metric when leveraging quantum resources. We further show how our approach can be extended to higher-dimensional compilation tasks. Our results are enabled by the tunable control of our cost landscape via variable squeezing, thus providing a critical framework to simultaneously increase precision and reduce time-to-solution.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Spatio-Temporal Deep Graph Network for Event Detection, Localization, and Classification in Cyber-Physical Electric Distribution System

This work proposes a deep graph learning framework to identify, locate, and classify power, cyber, and cyber power events at the distribution system level. The proposed algorithm jointly exploits spatial, temporal, and node-level cyber and physical data features. The developed graph neural network, together with a deep autoencoder, utilizes physical measurements from distribution level phasor measurement units and cyber data from communication network logs. The spatial structure of the synchrophasor measurements and network is incorporated through a weighted adjacency matrix. The temporal structure is incorporated by defining a spatial operation in the gated recurrent unit. This spatio-temporal learning element resides inside a power event detection, localization, and classification module that provides the degree of confidence for an event label. To accurately pinpoint the location of an event to the nearest bus equipped with a measurement unit, a combination of squared error and proximity score is utilized. Also included is a cyber event detection module that employs heteroskedasticity to analyze the significance of various cyber features during different types of attacks. Finally, a dual-bit cyber-power decision table determines the nature of the event. The proposed method is validated on two distribution systems modeled in OPAL-RT/Hypersim with limited phasor measurement units for different possible physical and cyber events. Further analyses include comparison with other state-of-the-art methods and validation in the presence of measurement noise. As a result, our method outperforms existing approaches and achieves an average detection accuracy of 97.97%, F1-score of 96.88%, precision of 96.53%, and recall of 98.57%.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2015

International Space Station (ISS) Environmental Control and Life Support (ECLS) System Overview of Events: 2010-2014

Nov 2, 2014 marked the completion of the 14th year of continuous human presence in space on board the International Space Station (ISS). After 42 expedition crews, over 115 assembly & utilization flights, over 180 combined Shuttle/Station, US & Russian Extravehicular Activities (EVAs), the post-Assembly-Complete ISS continues to fly and the engineering teams continue to learn from operating its systems, particularly the life support equipment. Problems with initial launch, assembly and activation of ISS elements have given way to more long term system operating trends. New issues have emerged, some with gestation periods measured in years. Major events and challenges for each U.S. Environmental Control and Life Support (ECLS) subsystem occurring during calendar years 2010 through 2014 are summarily discussed in this paper, along with look-aheads for what might be coming in the future for each U.S. ECLS subsystem.

Gentry, Gregory J.↗

DOE OSTI2024

Learning From User Behavior: A Survey-Assist Algorithm for Longitudinal Mobility Data Collection

GPS-based travel surveys are widely used in mobility studies to gather crucial qualitative data, like purpose, transportation mode and replaced mode. However, survey response still poses a burden to users, especially in long-term mobility studies, leading to response fatigue. We explore a survey-assist strategy to ease this burden by a novel, user-level modeling approach that leverages past responses from each user to predict responses for new trips, without relying on external data sources like GIS data. We investigate three main algorithms for predicting responses: (i) clustering trips and extrapolating responses for similar trips, (ii) using random forest classification, and (iii) clustering that uses a hybrid algorithm to determine spatial structure, which is then fed as input to a classic random forest classifier. The clustering approach can flexibly predict responses for even complex qualitative survey questions; it achieved F-scores of 65%. The random forest pipeline uses architecture that restricts it to predicting three predetermined survey questions: trip purpose, mode, and replaced mode. However, it achieved F-scores of 78%. While the survey-assist approach has been implemented by several proprietary systems, to our knowledge, this is the first exploration in the academic literature. It follows that this is also the first rigorous evaluation of multiple algorithms that can implement the approach. The evaluation uses a large scale, publicly available, longitudinal dataset consisting of ~ 92k trips from 235 users over a period of roughly one and a half years. With this approach, travel surveys can be pre-filled with the predicted responses for each trip, thus streamlining the survey process for users. Combined with an active learning system that requests user input on low-confidence predictions, models can be updated and improved over time to better support the long-term collection of longitudinal qualitative data.

clustering↗

NASA NTRS1992

National launch system core vehicle

A new launch system is being defined to provide significant improvements in the national launch capability. The most challenging requirements are to reduce operational cost and increase system reliability, dependability, responsiveness while maintaining acceptable mission performance. System architecture studies have established that a family of vehicles is required to launch the required range of payloads. The vehicle design approach implements a design process that identifies the system design criteria and design concepts that enable the vehicle to meet the objectives of cost, reliability, safety and performance. The vehicle design features for the 50 to 100 K payload capability will utilize cost effective elements derived from existing vehicles, emerging technology and lessons learned to enable the system to be developed at a reasonable investment and to deliver payloads in an operationally efficient manner with high reliability.

Worlund, Armis L.↗

NASA NTRS2005

A Proven Ground System Architecture for Promoting Collaboration and Common Solutions at NASA

NASA Goddard Space Flight Center's "GMSEC" ground system architecture was presented at GSAW2003 as a concept being studied. GMSEC would utilize a publish/subscribe middleware framework and standardized interfaces to allow custom and COTS ground system components to plug-and-play. This capability, in turn, would reduce integration costs, allow for technology infusion over time, and encourage the development and sharing of common components across missions and organizations. At GSAW2004, GMSEC was presented at a breakout session as a system working well in the NASA lab and being applied as an integral piece of reengineering efforts for several GSFC missions. Today, GMSEC is supporting five satellites at GSFC and has been selected by several future missions. Over 30 plug-and-play components are now available to missions using the GMSEC approach. Other organizations, including Marshall Space Flight Center, Johns Hopkins University's Applied Physic Lab, and the Institute for Scientific Research are each developing GMSEC-compatible components. Based on the success of GMSEC and efforts at other NASA Centers, the message bus approach is now being evaluated as a NASA Agency-wide approach for many future missions involving multiple NASA Centers as we move towards the goals of NASA s new Exploration Initiative. The presentation will explain the basic technical concepts of using a publish/subscribe framework for mission operations support (and its applicability to flight systems as well). Lessons learned on NASA's GMSEC program will allow the audience to better understand the significant benefits of this architecture approach over the traditional "one-off" solution approach. The point of the presentation is to show the long-term benefits of using a ground system architecture which incorporates many of the successful GMSEC concepts - message bus, mix of COTS and custom software, standard interfaces, plug-and-play, etc. The implications for the development process will also be discussed.

Smith, Danford↗

NASA NTRS1992

Artificial neural network implementation of a near-ideal error prediction controller

A theory has been developed at the University of Virginia which explains the effects of including an ideal predictor in the forward loop of a linear error-sampled system. It has been shown that the presence of this ideal predictor tends to stabilize the class of systems considered. A prediction controller is merely a system which anticipates a signal or part of a signal before it actually occurs. It is understood that an exact prediction controller is physically unrealizable. However, in systems where the input tends to be repetitive or limited, (i.e., not random) near ideal prediction is possible. In order for the controller to act as a stability compensator, the predictor must be designed in a way that allows it to learn the expected error response of the system. In this way, an unstable system will become stable by including the predicted error in the system transfer function. Previous and current prediction controller include pattern recognition developments and fast-time simulation which are applicable to the analysis of linear sampled data type systems. The use of pattern recognition techniques, along with a template matching scheme, has been proposed as one realizable type of near-ideal prediction. Since many, if not most, systems are repeatedly subjected to similar inputs, it was proposed that an adaptive mechanism be used to 'learn' the correct predicted error response. Once the system has learned the response of all the expected inputs, it is necessary only to recognize the type of input with a template matching mechanism and then to use the correct predicted error to drive the system. Suggested here is an alternate approach to the realization of a near-ideal error prediction controller, one designed using Neural Networks. Neural Networks are good at recognizing patterns such as system responses, and the back-propagation architecture makes use of a template matching scheme. In using this type of error prediction, it is assumed that the system error responses be known for a particular input and modeled plant. These responses are used in the error prediction controller. An analysis was done on the general dynamic behavior that results from including a digital error predictor in a control loop and these were compared to those including the near-ideal Neural Network error predictor. This analysis was done for a second and third order system.

Mcvey, Eugene S.↗

DOE OSTI2020

Neuromorphic Graph Algorithms: Extracting Longest Shortest Paths and Minimum Spanning Trees

Neuromorphic computing is poised to become a promising computing paradigm in the post Moore's law era due to its extremely low power usage and inherent parallelism. Traditionally speaking, a majority of the use cases for neuromorphic systems have been in the field of machine learning. In order to expand their usability, it is imperative that neuromorphic systems be used for non-machine learning tasks as well. The structural aspects of neuromorphic systems (i.e., neurons and synapses) are similar to those of graphs (i.e., nodes and edges), However, it is not obvious how graph algorithms would translate to their neuromorphic counterparts. In this work, we propose a preprocessing technique that introduces fractional offsets on the synaptic delays of neuromorphic graphs in order to break ties. This technique, in turn, enables two graph algorithms: longest shortest path extraction and minimum spanning trees.

Kay, Bill↗

NASA NTRS2014

Visual Image Sensor Organ Replacement

This innovation is a system that augments human vision through a technique called "Sensing Super-position" using a Visual Instrument Sensory Organ Replacement (VISOR) device. The VISOR device translates visual and other sensors (i.e., thermal) into sounds to enable very difficult sensing tasks. Three-dimensional spatial brightness and multi-spectral maps of a sensed image are processed using real-time image processing techniques (e.g. histogram normalization) and transformed into a two-dimensional map of an audio signal as a function of frequency and time. Because the human hearing system is capable of learning to process and interpret extremely complicated and rapidly changing auditory patterns, the translation of images into sounds reduces the risk of accidentally filtering out important clues. The VISOR device was developed to augment the current state-of-the-art head-mounted (helmet) display systems. It provides the ability to sense beyond the human visible light range, to increase human sensing resolution, to use wider angle visual perception, and to improve the ability to sense distances. It also allows compensation for movement by the human or changes in the scene being viewed.

Maluf, David A.↗

NASA NTRS2013

Lessons Learned from the Development of the MSL Descent Stage Propulsion System

Development of the MSL descent stage propulsion system required a number of new propulsion hardware developments incorporating technologies not normally found in spacecraft propulsion subsystems. These developments were driven by the relatively high (25,000 N) maximum thrust level and the requirement for precise throttling of the main engines. This paper presents lessons learned in the course of these developments, including surprises and anomalies discovered at both the component and subsystem levels.

Entry, Descent, and Landing (EDL)↗

DOE OSTI2019

Machine Learning for Solar Technology Portfolio Management

Technology readiness level (TRL) labeling and forecasting today is done entirely manually. A computationally-assisted model of a TRL-like classification for estimation and tracking could enable a research and development (R&D) portfolio manager to more accurately estimate the relative level of development for technologies in that portfolio. This report describes the work to understand readiness level systems, train a machine learning algorithm to recognize textual indicators of particular readiness levels within written material from carefully chosen case studies in renewable technology development, and apply that algorithm to predict the readiness levels of other technology development cases. We account for the results of each step of the work conducted, including the predictive accuracy of the trained model: 54% correct, within a +/-1 readiness level error threshold and a root mean squared error of 1.88. Great potential remains to improve and apply this model for management of individual projects and across portfolios.

14 SOLAR ENERGY↗

DOE OSTI2019

Machine Learning for Solar Technology Portfolio Management

14 SOLAR ENERGY↗

DOE OSTI2020

Machine learning with bond information for local structure optimizations in surface science

Local optimization of adsorption systems inherently involves different scales: within the substrate, within the molecule, and between the molecule and the substrate. In this work, we show how the explicit modeling of different characteristics of the bonds in these systems improves the performance of machine learning methods for optimization. Furthermore, we introduce an anisotropic kernel in the Gaussian process regression framework that guides the search for the local minimum, and we show its overall good performance across different types of atomic systems. The method shows a speed-up of up to a factor of two compared with the fastest standard optimization methods on adsorption systems. Additionally, we show that a limited memory approach is not only beneficial in terms of overall computational resources but can also result in a further reduction of energy and force calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Mutual information prediction for strongly correlated systems

We have trained a new machine-learning (ML) model which predicts mutual information (MI) for strongly correlated systems. This is a complex quantity, which is much more difficult to predict than one-site entropies, but carries important information about the correlation structure inside electronic systems. In this work, we replaced the expensive density matrix renormalization group (DMRG) calculations by newly trained ML model for prediction of the mutual information. We show the performance of the model on two important tasks: (a) to determine the correlation structure and (b) to determine ordering of orbitals for accurate DMRG calculations. In conclusion, the results are compared with the MI obtained from accurate DMRG calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗