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At least 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Advanced Data Acquisition Systems

Current and future requirements of the aerospace sensors and transducers field make it necessary for the design and development of new data acquisition devices and instrumentation systems. New designs are sought to incorporate self-health, self-calibrating, self-repair capabilities, allowing greater measurement reliability and extended calibration cycles. With the addition of power management schemes, state-of-the-art data acquisition systems allow data to be processed and presented to the users with increased efficiency and accuracy. The design architecture presented in this paper displays an innovative approach to data acquisition systems. The design incorporates: electronic health self-check, device/system self-calibration, electronics and function self-repair, failure detection and prediction, and power management (reduced power consumption). These requirements are driven by the aerospace industry need to reduce operations and maintenance costs, to accelerate processing time and to provide reliable hardware with minimum costs. The project's design architecture incorporates some commercially available components identified during the market research investigation like: Field Programmable Gate Arrays (FPGA) Programmable Analog Integrated Circuits (PAC IC) and Field Programmable Analog Arrays (FPAA); Digital Signal Processing (DSP) electronic/system control and investigation of specific characteristics found in technologies like: Electronic Component Mean Time Between Failure (MTBF); and Radiation Hardened Component Availability. There are three main sections discussed in the design architecture presented in this document. They are the following: (a) Analog Signal Module Section, (b) Digital Signal/Control Module Section and (c) Power Management Module Section. These sections are discussed in detail in the following pages. This approach to data acquisition systems has resulted in the assignment of patent rights to Kennedy Space Center under U.S. patent # 6,462,684. Furthermore, NASA KSC commercialization office has issued licensing rights to Circuit Avenue Netrepreneurs, LLC , a minority-owned business founded in 1999 located in Camden, NJ.

Perotti, J.↗
DOE OSTI2019

GMLS-Nets: A Framework for Learning from Unstructured Data

Data fields sampled on irregularly spaced points arise in many applications in the sciences and engineering. For regular grids, Convolutional Neural Networks (CNNs) have been successfully used to gaining benefits from weight sharing and invariances. We generalize CNNs by introducing methods for data on unstructured point clouds based on Generalized Moving Least Squares (GMLS). GMLS is a nonparametric technique for estimating linear bounded functionals from scattered data, and has recently been used in the literature for solving partial differential equations. By parameterizing the GMLS estimator, we obtain learning methods for operators with unstructured stencils. In GMLS-Nets the necessary calculations are local, readily parallelizable, and the estimator is supported by a rigorous approximation theory. We show how the framework may be used for unstructured physical data sets to perform functional regression to identify associated differential operators and to regress quantities of interest. The results suggest the architectures to be an attractive foundation for data-driven model development in scientific machine learning applications.

42 ENGINEERING↗
NASA NTRS2013

A Multi-Purpose Modular Electronics Integration Node for Exploration Extravehicular Activity

As NASA works to develop an effective integrated portable life support system design for exploration Extravehicular activity (EVA), alternatives to the current system s electrical power and control architecture are needed to support new requirements for flexibility, maintainability, reliability, and reduced mass and volume. Experience with the current Extravehicular Mobility Unit (EMU) has demonstrated that the current architecture, based in a central power supply, monitoring and control unit, with dedicated analog wiring harness connections to active components in the system has a significant impact on system packaging and seriously constrains design flexibility in adapting to component obsolescence and changing system needs over time. An alternative architecture based in the use of a digital data bus offers possible wiring harness and system power savings, but risks significant penalties in component complexity and cost. A hybrid architecture that relies on a set of electronic and power interface nodes serving functional models within the Portable Life Support System (PLSS) is proposed to minimize both packaging and component level penalties. A common interface node hardware design can further reduce penalties by reducing the nonrecurring development costs, making miniaturization more practical, maximizing opportunities for maturation and reliability growth, providing enhanced fault tolerance, and providing stable design interfaces for system components and a central control. Adaptation to varying specific module requirements can be achieved with modest changes in firmware code within the module. A preliminary design effort has developed a common set of hardware interface requirements and functional capabilities for such a node based on anticipated modules comprising an exploration PLSS, and a prototype node has been designed assembled, programmed, and tested. One instance of such a node has been adapted to support testing the swingbed carbon dioxide and humidity control element in NASA s advanced PLSS 2.0 test article. This paper will describe the common interface node design concept, results of the prototype development and test effort, and plans for use in NASA PLSS 2.0 integrated tests.

Hodgson, Edward↗
DOE OSTI2019

GMLS-Nets: Scientific Machine Learning Methods for Unstructured Data

Data fields sampled on irregularly spaced points arise in many applications in the sciences and engineering. For regular grids, Convolutional Neural Networks (CNNs) have been successfully used to gaining benefits from weight sharing and invariances. We generalize CNNs by introducing methods for data on unstructured point clouds based on Generalized Moving Least Squares (GMLS). GMLS is a nonparametric technique for estimating linear bounded functionals from scattered data, and has recently been used in the literature for solving partial differential equations. By parameterizing the GMLS estimator, we obtain learning methods for operators with unstructured stencils. In GMLS-Nets the necessary calculations are local, readily parallelizable, and the estimator is supported by a rigorous approximation theory. We show how the framework may be used for unstructured physical data sets to perform functional regression to identify associated differential operators and to regress quantities of interest. The results suggest the architectures to be an attractive foundation for data-driven model development in scientific machine learning applications

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

GMLS-Nets: A Framework for Learning from Unstructured Data

Data fields sampled on irregularly spaced points arise in many applications in the sciences and engineering. For regular grids, Convolutional Neural Networks (CNNs) have been successfully used to gaining benefits from weight sharing and invariances. We generalize CNNs by introducing methods for data on unstructured point clouds based on Generalized Moving Least Squares (GMLS). GMLS is a non-parametric technique for estimating linear bounded functionals from scattered data, and has recently been used in the literature for solving partial differential equations. By parameterizing the GMLS estimator, we obtain learning methods for operators with unstructured stencils. In GMLS-Nets the necessary calculations are local, readily parallelizable, and the estimator is supported by a rigorous approximation theory. We show how the framework may be used for unstructured physical data sets to perform functional regression to identify associated differential operators and to regress quantities of interest. The results suggest the architectures to be an attractive foundation for data-driven model development in scientific machine learning applications

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Data efficiency and extrapolation trends in neural network interatomic potentials

Abstract Recently, key architectural advances have been proposed for neural network interatomic potentials (NNIPs), such as incorporating message-passing networks, equivariance, or many-body expansion terms. Although modern NNIP models exhibit small differences in test accuracy, this metric is still considered the main target when developing new NNIP architectures. In this work, we show how architectural and optimization choices influence the generalization of NNIPs, revealing trends in molecular dynamics (MD) stability, data efficiency, and loss landscapes. Using the 3BPA dataset, we uncover trends in NNIP errors and robustness to noise, showing these metrics are insufficient to predict MD stability in the high-accuracy regime. With a large-scale study on NequIP, MACE, and their optimizers, we show that our metric of loss entropy predicts out-of-distribution error and data efficiency despite being computed only on the training set. This work provides a deep learning justification for probing extrapolation and can inform the development of next-generation NNIPs.

36 MATERIALS SCIENCE↗
NASA NTRS2003

Interactive, Secure Web-enabled Aircraft Engine Simulation Using XML Databinding Integration

This paper discusses the detailed design of an XML databinding framework for aircraft engine simulation. The framework provides an object interface to access and use engine data. while at the same time preserving the meaning of the original data. The Language independent representation of engine component data enables users to move around XML data using HTTP through disparate networks. The application of this framework is demonstrated via a web-based turbofan propulsion system simulation using the World Wide Web (WWW). A Java Servlet based web component architecture is used for rendering XML engine data into HTML format and dealing with input events from the user, which allows users to interact with simulation data from a web browser. The simulation data can also be saved to a local disk for archiving or to restart the simulation at a later time.

Lin, Risheng↗
DOE OSTI2017

Multi-static Serial LiDAR for Surveillance and Identification of Marine Life at MHK Installations

Final Report for project DE-EE0006787: Multi-static Serial LiDAR for Surveillance and Identification of Marine Life at MHK Installations. This project developed and tested an optical monitoring system prototype that will be suitable for marine and hydrokinetic (MHK) full project lifecycle observation (baseline, commissioning, and decommissioning), with automated real-time classification of marine animals. This system can be deployed to collect pre-installation baseline species observations at a proposed deployment site with minimal post-processing overhead. To satisfy deployed MHK project species of concern (e.g. Endangered Species Act-listed) monitoring requirements, the system provides automated tracking and notification of the presence of managed animals within established perimeters of MHK equipment and provides high resolution imagery of their behavior through a wide range of conditions. During a project’s decommissioning stage, the system can remain installed to provide resource managers with post-installation data. Our technology, known as an Unobtrusive Multi-static Serial LiDAR Imager (UMSLI), is a technology transfer of underwater distributed LiDAR imaging technology that preserves the advantages of traditional optical and acoustic solutions while overcoming associated disadvantages for MHK environmental monitoring applications. This new approach is a purposefully-designed, reconfigurable adaptation of an existing technology that can be easily mounted on or around different classes of MHK equipment. The system uses low average power red (638nm) laser illumination to be invisible and eye-safe to marine animals and is compact and cost effective. The equipment is designed for long term, maintenance-free operations, to inherently generate a sparse primary dataset that only includes detected anomalies (animal presence information), and to allow robust real-time automated animal classification/identification with a low data bandwidth requirement. Advantages of the technology over others currently being used or being considered for MHK monitoring include: Unlike a conventional camera, the depth of field is near-infinite and limited by attenuation (approximately 5-8 m) rather than focal properties of a lens; Operation in an adaptive mode which can project a sparse grid of pulses with higher peak power for longer range detection (>10 meters) and track animals within a zone of interest with high resolution imagery for identification of marine life at closer range (<5m); System detection limit and Signal-to-Noise-Ratio is superior to a camera, due to rejection of both backscattering component and ambient solar background; Multiple wide-angle pulsed laser illuminators and bucket detectors can be flexibly configured to cover a 4pi steradian (i.e. omnidirectional) scene volume, while also retrieving 3D features of animal targets from timing information; Process and classification framework centered around a novel active learning and incremental classification classifier that enables accurate identification of a variety of marine animals automatically; A two-tiered monitoring architecture and invisible watermarking-based data archiving and retrieving approach ensures significant data reduction while preserving high fidelity monitoring. A methodology to train and optimize the classifier for target species of concern to optimize site monitoring effectiveness. This technological innovation addresses a high priority regulatory requirement to observe marine life interaction near MHK projects. Our solution improves resource manager confidence that any interactions between marine animals and equipment are observed in a cost-effective and automated manner. Without EERE funding, this novel application of multi-static LiDAR would not have been available to the MHK community for environmental monitoring.

16 TIDAL AND WAVE POWER↗
NASA NTRS2011

Plug-and-Play Environmental Monitoring Spacecraft Subsystem

A Space Environment Monitor (SEM) subsystem architecture has been developed and demonstrated that can benefit future spacecraft by providing (1) real-time knowledge of the spacecraft state in terms of exposure to the environment; (2) critical, instantaneous information for anomaly resolution; and (3) invaluable environmental data for designing future missions. The SEM architecture consists of a network of plug-and- play (PnP) Sensor Interface Units (SIUs), each servicing one or more environmental sensors. The SEM architecture is influenced by the IEEE Smart Transducer Interface Bus standard (IEEE Std 1451) for its PnP functionality. A network of PnP Spacecraft SIUs is enabling technology for gathering continuous real-time information critical to validating spacecraft health in harsh space environments. The demonstrated system that provided a proof-of-concept of the SEM architecture consisted of three SIUs for measurement of total ionizing dose (TID) and single event upset (SEU) radiation effects, electromagnetic interference (EMI), and deep dielectric charging through use of a prototype Internal Electro-Static Discharge Monitor (IESDM). Each SIU consists of two stacked 2X2 in. (approximately 5X5 cm) circuit boards: a Bus Interface Unit (BIU) board that provides data conversion, processing and connection to the SEM power-and-data bus, and a Sensor Interface Electronics (SIE) board that provides sensor interface needs and data path connection to the BIU.

Patel, Jagdish↗
DOE OSTI2024

DICER: Data Intensive Computing Environment and Runtime for Evaluating Unprecedented Scale of Geospatial-Temporal Human Mobility Data

With the significant increase in sources and volume of human mobility data through commercial data vendors as well as microsimulation of cities, the scale of geospatial-temporal data to analyze and assess for mobility characterization has grown to the level of Big Data. There are mobility related commercial organizations deploying scalable computing, but often the system architecture, workflow, and intermediate processing components are not fully disclosed in relevant scope. Current research literature has a notable lack of studies demonstrating architectures and workflows for human mobility analytics that are implemented on a TeraByte scale of geospatial-temporal data. In this context, this paper presents a hyperscale-level system solution named DICER (Data Intensive Computing Environment and Runtime) for processing and analytics of geospatial-temporal data at big data scale. Although the cluster computing architecture of DICER with Apache Spark job running on Kubernetes cluster is not new, there are innovations in the workflow, hierarchical processing logic, and a wide range of intermediate preprocessing and mobility metrics calculation. We have performed case studies to validate the effectiveness of DICER system solution by performing detailed analytics and assessment of human mobility microsimulation output at three different scopes and scale, including a usecase with 16.97 TeraByte and 259.2 Billion rows of data. In addition, we have presented another case study of utilizing DICER to perform the same mobility processing and comparative analytics on large-scale commercially available geospatial-temporal data. All these case studies validate the efficiency and usefulness of DICER in computing population mobility characteristics from geospatial-temporal trajectory data at an unprecedented scale (not only just data volume, but also combination of: number of user entities, temporal frequency, spatial resolution, data duration).

De, Debraj↗
DOE OSTI2022

Deep learning-based spatio-temporal estimate of greenhouse gas emissions using satellite data

Accurate estimation of greenhouse gases (GHGs) emissions is very important for developing mitigation strategies to climate change by controlling and reducing GHG emissions. This project aims to develop multiple deep learning approaches to estimate anthropogenic greenhouse gas emissions using multiple types of satellite data. NO2 concentration is chosen as an example of GHGs to evaluate the proposed approach. Two sentinel satellites (sentinel-2 and sentinel-5P) provide multiscale observations of GHGs from 10-60m resolution (sentinel-2) to ~kilometer scale resolution (sentinel-5P). Among multiple deep learning (DL) architectures evaluated, two best DL models demonstrate that key features of spatio-temporal satellite data and additional information (e.g., observation times and/or coordinates of ground stations) can be extracted using convolutional neural networks and feed forward neural networks, respectively. In particular, irregular time series data from different NO 2 observation stations limit the flexibility of long short-term memory architecture, requiring zero-padding to fill in missing data. However, deep neural operator (DNO) architecture can stack time-series data as input, providing the flexibility of input structure without zero-padding. As a result, the DNO outperformed other deep learning architectures to account for time-varying features. Overall, temporal patterns with smooth seasonal variations were predicted very well, while frequent fluctuation patterns were not predicted well. In addition, uncertainty quantification using conformal inference method is performed to account for prediction ranges. Overall, this research will lead to a new groundwork for estimating greenhouse gas concentrations using multiple satellite data to enhance our capability of tracking the cause of climate change and developing mitigation strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Exploring Hardware Support For Scaling Irregular Applications on Multi-node Multi-core Architectures

With the recent emergence of large-scale knowledge dis- covery, data mining and social network analysis, irregular applications have gained renewed interest. Classic cache-based high-performance architectures do not provide optimal performances with such kind of workloads, mainly due to the very low spatial and temporal locality of the irregular control and memory access patterns. In this paper, we present a multi-node, multi-core, fine-grained multi-threaded shared-memory system architecture specifically designed for the execution of large-scale irregular applications, and built on top of three pillars, that we believe are fundamental to support these workloads. First, we offer transparent hardware support for Partitioned Global Address Space (PGAS) to provide a large globally-shared address space with no software library overhead. Second, we employ multi-threaded multi-core processing nodes to achieve the necessary latency tolerance required by accessing global memory, which potentially resides in a remote node. Finally, we devise hardware support for inter-thread synchronization on the whole global address space. We first model the performances by using an analytical model that takes into account the main architecture and application characteristics. We describe the hardware design of the proposed cus- tom architectural building blocks that provide support for the above- mentioned three pillars. Finally, we present a limited-scale evaluation of the system on a multi-board FPGA prototype with typical irregular kernels and benchmarks. The experimental evaluation demonstrates the architecture performance scalability for different configurations of the whole system.

FPGA↗
DOE OSTI2020

Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models

Recent progress in scientific machine learning (SciML) has opened up the possibility of training novel neural network architectures that solve complex partial differential equations (PDEs). Several (nearly data free) approaches have been recently reported that successfully solve PDEs, with examples including deep feed forward networks, generative networks, and deep encoder-decoder networks. However, practical adoption of these approaches is limited by the difficulty in training these models, especially to make predictions at large output resolutions (≥1024×1024). Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models - training in reasonable time as well as distributing the storage requirements. Our framework provides several out of the box functionality including (a) loss integrity independent of number of processes, (b) synchronized batch normalization, and (c) distributed higher-order optimization methods. We show excellent scalability of this framework on both cloud as well as HPC clusters, and report on the interplay between bandwidth, network topology and bare metal vs cloud. We deploy this approach to train generative models of sizes hitherto not possible, showing that neural PDE solvers can be viably trained for practical applications. We also demonstrate that distributed higher-order optimization methods are 2-3× faster than stochastic gradient-based methods and provide minimal convergence drift with higher batch-size.

PDEs↗
NASA NTRS2002

REACH: Real-Time Data Awareness in Multi-Spacecraft Missions

NASA's Advanced Architectures and Automation Branch at the Goddard Space Flight Center (Code 588) saw the potential to reduce the cost of constellation missions by creating new user interfaces to the ground system health-and-safety data. The goal is to enable a small Flight Operations Team (FOT) to remain aware and responsive to the increased amount of ground system information in a multi-spacecraft environment. Rather than abandon the tried and true, these interfaces were developed to run alongside existing ground system software to provide additional support to the FOT. These new user interfaces have been combined in a tool called REACH. REACH-the Real-time Evaluation and Analysis of Consolidated Health-is a software product that uses advanced visualization techniques to make spacecraft anomalies easy to spot, no matter how many spacecraft are in the constellation. REACH reads numerous real-time streams of data from the ground system(s) and displays synthesized information to the FOT such that anomalies are easy to pick out and investigate.

Maks, Lori↗
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 NTRS2024

Modeling Evolvable Water Recovery Systems for Short and Long-Duration Missions in Partial Gravity

Water recovery technologies on the International Space Station (ISS) are designed and optimized for microgravity environments, thus creating a need for innovative systems optimized for mission operations in the presence of gravity. Alternative water recovery technologies under research by the Life Support Systems division were compiled into a series of Partial Gravity Water Recovery System (PGWRS) architectures. Individual unit processes were modeled via fundamental physical-chemical process equations to simulate their method of treatment, including chemical or biological oxidation, flash evaporation, filtration, or adsorption and ion exchange. Dimension and sizing data were then utilized to scale each architecture. Modeling was completed on an assortment of architectures assuming three scenarios in which treatment methods could take advantage of partial gravity: a short-duration mission with a temporary surface habitat, a longer duration mission with a more permanent habitat, and a long duration mission with minimal resupply. Total system mass was estimated to quantitatively compare architectures within scenarios, but qualitative observations were also made regarding system robustness, flexibility, and capacity to meet more stringent demands. Technologies that traded most favorably between the scenarios were those that included a pre-oxidation stage to minimize mass loading to downstream absorption and ion exchange beds, exploited gravity in liquid-vapor separation processes, and reconstituted wastewater components into recyclable material streams.

Avery L. Carlson↗
NASA NTRSDate not supplied

Modeling Evolvable Water Recovery Systems for Short and Long-Duration Missions in Partial Gravity

Water recovery technologies on the International Space Station (ISS) are designed and optimized for microgravity environments, thus creating a need for innovative systems optimized for mission operations in the presence of gravity. Alternative water recovery technologies under research by the Life Support Systems division were compiled into a series of Partial Gravity Water Recovery System (PGWRS) architectures. Individual unit processes were modeled via fundamental physical-chemical process equations to simulate their method of treatment, including chemical or biological oxidation, flash evaporation, filtration, or adsorption and ion exchange. Dimension and sizing data were then utilized to scale each architecture. Modeling was completed on an assortment of architectures assuming three scenarios in which treatment methods could take advantage of partial gravity: a short-duration mission with a temporary surface habitat, a longer duration mission with a more permanent habitat, and a long duration mission with minimal resupply. Total system mass was estimated to quantitatively compare architectures within scenarios, but qualitative observations were also made regarding system robustness, flexibility, and capacity to meet more stringent demands. Technologies that traded most favorably between the scenarios were those that included a pre-oxidation stage to minimize mass loading to downstream absorption and ion exchange beds, exploited gravity in liquid-vapor separation processes, and reconstituted wastewater components into recyclable material streams.

Avery Carlson↗