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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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At least 1,783 records · Page 99
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Design-to-Deployment Continuum Platform for Microscopes and Computing Ecosystems

Science ecosystems with networked computing systems and physical instruments are increasingly being deployed with a goal to achieve the productivity promised by AI-supported remote automation. In support of these efforts, the virtual infrastructure twins (VITs) have been successfully utilized to develop the orchestration codes for these ecosystems without requiring physical access to expensive instruments, such as electron microscopes. Currently, the utility of such a VIT is severely limited by the computing capacity and capability of the computing system used as its host. Furthermore, codes developed on the VIT typically need to be transferred and refactored for production use, particularly, on high-performance systems with accelerators. In response, we develop a design-to-deployment continuum platform wherein a VIT runs natively on the ecosystem's own computing system, and thereby facilitates the continual in-situ testing and transition of codes for production use. Here, we describe the development and testing of software for remote microscope steering and GPU-based image reconstruction using this platform on a multi-GPU computing system networked to Nion microscopes. We demonstrate a continual transition of steering and reconstruction codes developed under VIT platform to production ecosystem deployment.

Al-Najjar, Anees [Oak Ridge National Laboratory (O↗
DOE OSTI2019

Contrasting advantages of learning with random weights and backpropagation in non-volatile memory neural networks

Recently, a Cambrian explosion of novel, non-volatile memory (NVM) devices known as memristive devices have inspired effort in building hardware neural networks that learn like the brain. Early experimental prototypes built simple perceptrons from nanosynapses, and recently, fully-connected multi-layer perceptron (MLP) learning systems have been realized. However, while backpropagating learning systems pair well with high-precision computer memories and achieve state-of-the-art performances, this typically comes with a massive energy budget. For future Internet of Things/peripheral use cases, system energy footprint will be a major constraint, and emerging NVM devices may fill the gap by sacrificing high bit precision for lower energy. In this work, we contrast the well known MLP approach with the Extreme Learning Machine (ELM) or NoProp approach, which uses a large layer of random weights to improve the separability of high-dimensional tasks, and is usually considered inferior in a software context. However, we find that when taking device non-linearity into account, NoProp manages to equal hardware MLP system in terms of accuracy. While also using a sign-based adaptation of the delta rule for energy-savings, we find that NoProp can learn effectively with four to six ’bits’ of device analog capacity, while MLP requires eight bit capacity with the same rule. This may allow the requirements for memristive devices to be relaxed in the context of online learning. By comparing the energy footprint of these systems for several candidate nanosynapses, as well as realistic peripherals, we confirm that memristive NoProp systems save energy compared to MLP systems. Lastly, we show that ELM/NoProp systems can achieve better generalization abilities than nanosynaptic MLP systems when paired with pre-processing layers (which do not require backpropagated error). Collectively, these advantages make such systems worthy of consideration in future accelerators or embedded hardware.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Effect of Microstructure and Properties of Ni-WC Composite Coatings on Their Solid Particle Erosion Behavior

Metal matrix composites (MMCs) of WC in Co or Ni matrices are widely used due to their high wear resistance. Previous studies showed that tailoring the mechanical properties through optimum WC content can significantly extend the life cycle of MMCs. In this study, composite coatings of Ni with various WC content ranging from 10 to 55 vol.% were cold sprayed using agglomerated WCNi powders and two different sizes of Ni powders, − 10 + 4 (d{sub 50} = 7 µm) referred to as Ni(7) and − 35 + 15 (d{sub 50} = 25 µm) to as Ni(25). Microstructural characterization, including the deformed structure of Ni splats and retention and distribution of WC, was performed by scanning electron microscopy (SEM). Composite coatings were subjected to solid particle erosion following ASTM G76-13 standard. At low WC content coatings, erosion occurred primarily by plastic deformation of Ni matrices and knocking out of WC particles. At high WC concentrations, network distribution of compacted WCNi particles in the matrices led to a significant drop in erosion rates of coatings, due to a change in erosion mechanism from severe cutting and plowing of Ni and WC dislodging to WCNi splats spalling. Composite coatings sprayed using finer size Ni powder were usually more erosion resistant.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Machine Learning of All Mycobacterium tuberculosis H37Rv RNA-seq Data Reveals a Structured Interplay between Metabolism, Stress Response, and Infection

Mycobacterium tuberculosis is one of the most consequential human bacterial pathogens, posing a serious challenge to 21st century medicine. A key feature of its pathogenicity is its ability to adapt its transcriptional response to environmental stresses through its transcriptional regulatory network (TRN). While many studies have sought to characterize specific portions of the M. tuberculosis TRN, and some studies have performed system-level analysis, few have been able to provide a network-based model of the TRN that also provides the relative shifts in transcriptional regulator activity triggered by changing environments. Here, we compiled a compendium of nearly 650 publicly available, high quality M. tuberculosis RNA-sequencing data sets and applied an unsupervised machine learning method to obtain a quantitative, top-down TRN. It consists of 80 independently modulated gene sets known as “iModulons,” 41 of which correspond to known regulons. These iModulons explain 61% of the variance in the organism’s transcriptional response. We show that iModulons (i) reveal the function of poorly characterized regulons, (ii) describe the transcriptional shifts that occur during environmental changes such as shifting carbon sources, oxidative stress, and infection events, and (iii) identify intrinsic clusters of regulons that link several important metabolic systems, including lipid, cholesterol, and sulfur metabolism. This transcriptome-wide analysis of the M. tuberculosis TRN informs future research on effective ways to study and manipulate its transcriptional regulation and presents a knowledge-enhanced database of all published high-quality RNA-seq data for this organism to date.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Flavor Classification in ICARUS Using Convolutional Visual Networks

In this work, we adapt the Convolutional Visual Network (CVN) approach [1] to the ICARUS detector by incorporating TPC stitching methods inspired by NuGraph[8]. The stitching technique used here is unique to ICARUS, designed specifically to handle its distinct detector segmentations. We then retrain the network using ICARUS-specific data. This study underscores the flexibility of deep learning models in high-energy physics and the importance of accounting for detector-specific features when transferring machine learning techniques between experiments. This poster presents the methods, classification performance, and insights gained from applying CVN to ICARUS data.

Wieler, Felipe [Tech. Fed. Parana U.]↗
NASA NTRS1984

New features of different frequency generating systems due to the use of electrodeless rigidly mounted VBA quartz crystal resonator

The BVA 5 MHz crystal equipped frequency sources exhibit a new blend of performances such as 10 to 11 daily stability, 5x10-13 short term stability (1 to 30 s time intervals) and close to the carrier low phase noise (1 Hz : -120 dBc, 10 Hz : -140 dBc), whereby retaining the customary crystal oscillator benefits of small volume, high reliability and low price, as opposed to more sophisticated frequency generators which would be required to achieve comparable performances. Examples illustrating the impact of the Oscilloquartz BVA oven-controlled crystal oscillator in different frequency generating systems are presented: cesium frequency standards; hydrogen frequency standard; a precision distribution sub-system for satellite ground stations; and high hierarchy exchanges of digital networks, synchronized by the master-slave method are discussed.

Jendly, A.↗
NASA NTRS1987

Telemetry handling on the Space Station data management system

This paper examines the impact of telemetry handling on the design of the onboard networks that are part of the Space Station Data Management System (DMS). An architectural approach to satisfying the DMS requirement for support of the high throughput needed for telemetry transport and for servicing distributed computer systems is discussed. Several of the functionality vs. performance tradeoffs that must be made in developing an optimized mechanism for handling telemetry data in the DMS are considered.

Whitelaw, Virginia A.↗
NASA NTRS1991

Engineering Test Satellite VI (ETS-VI)

The Engineering Test Satellite-VI (ETS-VI) is being developed as the third Japanese three-axis stabilized engineering test satellite to establish the 2-ton geostationary operational satellite bus system and to demonstrate the high performance satellite communication technology for future operational satellites. The satellite is expected to be stationed at 154 deg east latitude. It will be launched from the Tanegashima Space Center in Japan by a type H-II launch vehicle. The Deep Space Network (DSN) will support the prelaunch compatibility test, data interface verification testing, and launch rehersals. The DSN primary support period is from launch through the final AEF plus 1 hour. Contingency support is from final AEF plus 1 hour until launch plus 1 month. The coverage will consist of all the 26-m antennas as prime and the 34-m antennas at Madrid and Canberra as backup. Maximum support will consist of two 8-hour tracks per station for a 7-day period, plus the contingency support, if required. Information is given in tabular form for DSN support, telemetry, command, and tracking support responsibility.

Horii, M.↗
NASA NTRS1993

Neural Network Would Estimate Fatigue Life

Processor learns mappings between sequences of loads and fatigue damage. Data processor built around neural network provides real-time estimates of fatigue life of simple mechanical components. Intended to be forerunner of more-advanced processors that estimates remaining lives of components stressed near their mechanical and thermal limits in turbine engines and other complicated, high-performance machinery. Real-time estimates of accumulated fatigue damage and remaining life before mechanical failure useful in scheduling maintenance and controlling operation to extend overall life of machinery.

Merrill, W.↗
NASA NTRS2005

NASA Tech Briefs, August 2005

Topics include: Hidden Identification on Parts: Magnetic Machine-Readable Matrix Symbols; System for Processing Coded OFDM Under Doppler and Fading; Multipurpose Hyperspectral Imaging System; Magnetic-Flux-Compensated Voltage Divider; High-Performance Satellite/Terrestrial-Network Gateway; Internet-Based System for Voice Communication With the ISS; Stripline/Microstrip Transition in Multilayer Circuit Board; Dual-Band Feed for a Microwave Reflector Antenna; Quadratic Programming for Allocating Control Effort; Range Process Simulation Tool; Simulator of Space Communication Networks; Computing Q-D Relationships for Storage of Rocket Fuels; Contour Error Map Algorithm; Portfolio Analysis Tool; Glass Frit Filters for Collecting Metal Oxide Nanoparticles; Anhydrous Proton-Conducting Membranes for Fuel Cells; Portable Electron-Beam Free-Form Fabrication System; Miniature Laboratory for Detecting Sparse Biomolecules; Multicompartment Liquid-Cooling/Warming Protective Garments; Laser Metrology for an Optical-Path-Length Modulator; PCM Passive Cooling System Containing Active Subsystems; Automated Electrostatics Environmental Chamber; Estimating Aeroheating of a 3D Body Using a 2D Flow Solver; Artificial Immune System for Recognizing Patterns; Computing the Thermodynamic State of a Cryogenic Fluid; Safety and Mission Assurance Performance Metric; Magnetic Control of Concentration Gradient in Microgravity; Avionics for a Small Robotic Inspection Spacecraft; and Simulation of Dynamics of a Flexible Miniature Airplane.

Source record↗
NASA NTRS2004

Nanofluidic Size-Exclusion Chromatograph

Efforts are under way to develop a nanofluidic size-exclusion chromatograph (SEC), which would be a compact, robust, lightweight instrument for separating molecules of interest according to their sizes and measuring their relative abundances in small samples. About as large as a deck of playing cards, the nanofluidic SEC would serve, in effect, as a laboratory on a chip that would perform the functions of a much larger, conventional, bench-top SEC and ancillary equipment, while consuming much less power and much smaller quantities of reagent and sample materials. Its compactness and low power demand would render it attractive for field applications in which, typically, it would be used to identify and quantitate a broad range of polar and nonpolar organic compounds in soil, ice, and water samples. Size-exclusion chromatography is a special case of high-performance liquid chromatography. In a conventional SEC, a sample plug is driven by pressure along a column packed with silica or polymer beads that contain uniform nanopores. The interstices between, and the pores in, the beads collectively constitute a size-exclusion network. Molecules follow different paths through the size-exclusion network, such that characteristic elution times can be related to sizes of molecules: basically, smaller molecules reach the downstream end of the column after the larger ones do because the smaller ones enter minor pores and stay there for a while, whereas the larger ones do not enter the pores. The volume accessible to molecules gradually diminishes as their size increases. All molecules bigger than a pore size elute together. For most substances, the elution times and sizes of molecules can be correlated directly with molecular weights. Hence, by measuring the flux of molecules arriving at the downstream end as a function of time, one can obtain a liquid mass spectrum for the molecules present in a sample over a broad range of molecular weights.

Feldman, Sabrina↗
DOE OSTI2026

Neutrino Flavor Classification in ICARUS Experiment Using Convolutional Visual Networks

In this work, I adapt the Convolutional Visual Network (CVN) approach to the ICARUS detector by incorporating TPC stitching methods inspired by NuGraph. The stitching technique used here is unique to ICARUS, designed specifically to handle its distinct detector segmentation s. I then retrain the network using ICARUS-specific data. This study underscores the flexibility of deep learning models in high-energy physics and the importance of accounting for detector-specific features when transferring machine learning techniques between experiments. This dissertation presents the methods, classification performance, and insights gained from applying CVN to ICARUS data.

Wieler, Felipe Andre [Parana Tech. Fed. U., Toledo↗
DOE OSTI2023

Deep learning for morphological identification of extended radio galaxies using weak labels

Abstract The present work discusses the use of a weakly-supervised deep learning algorithm that reduces the cost of labelling pixel-level masks for complex radio galaxies with multiple components. The algorithm is trained on weak class-level labels of radio galaxies to get class activation maps (CAMs). The CAMs are further refined using an inter-pixel relations network (IRNet) to get instance segmentation masks over radio galaxies and the positions of their infrared hosts. We use data from the Australian Square Kilometre Array Pathfinder (ASKAP) telescope, specifically the Evolutionary Map of the Universe (EMU) Pilot Survey, which covered a sky area of 270 square degrees with an RMS sensitivity of 25–35 $\mu$ Jy beam $^{-1}$ . We demonstrate that weakly-supervised deep learning algorithms can achieve high accuracy in predicting pixel-level information, including masks for the extended radio emission encapsulating all galaxy components and the positions of the infrared host galaxies. We evaluate the performance of our method using mean Average Precision (mAP) across multiple classes at a standard intersection over union (IoU) threshold of 0.5. We show that the model achieves a mAP $_{50}$ of 67.5% and 76.8% for radio masks and infrared host positions, respectively. The network architecture can be found at the following link: https://github.com/Nikhel1/Gal-CAM

Astronomy & Astrophysics↗
NASA NTRS1990

Multiprocessor architecture: Synthesis and evaluation

Multiprocessor computed architecture evaluation for structural computations is the focus of the research effort described. Results obtained are expected to lead to more efficient use of existing architectures and to suggest designs for new, application specific, architectures. The brief descriptions given outline a number of related efforts directed toward this purpose. The difficulty is analyzing an existing architecture or in designing a new computer architecture lies in the fact that the performance of a particular architecture, within the context of a given application, is determined by a number of factors. These include, but are not limited to, the efficiency of the computation algorithm, the programming language and support environment, the quality of the program written in the programming language, the multiplicity of the processing elements, the characteristics of the individual processing elements, the interconnection network connecting processors and non-local memories, and the shared memory organization covering the spectrum from no shared memory (all local memory) to one global access memory. These performance determiners may be loosely classified as being software or hardware related. This distinction is not clear or even appropriate in many cases. The effect of the choice of algorithm is ignored by assuming that the algorithm is specified as given. Effort directed toward the removal of the effect of the programming language and program resulted in the design of a high-level parallel programming language. Two characteristics of the fundamental structure of the architecture (memory organization and interconnection network) are examined.

Standley, Hilda M.↗
DOE OSTI2023

Multi-module-based CVAE to predict HVCM faults in the SNS accelerator

We present a multi-module framework based on Conditional Variational Autoencoder (CVAE) to detect anomalies in the power signals coming from multiple High Voltage Converter Modulators (HVCMs). We condition the model with the specific modulator type to capture different representations of the $\mathcal{normal}$ waveforms and to improve the sensitivity of the model to identify a specific type of fault when we have limited samples for a given module type. We studied several Artificial Neural Network (ANN) architectures for our CVAE model and evaluated the model performance by looking at their loss landscape for stability and generalization. Our results for the Spallation Neutron Source (SNS) experimental data show that the trained model generalizes well to detecting multiple fault types for several HVCM module types. The results of this study can be used to improve the HVCM reliability and overall SNS uptime.

43 PARTICLE ACCELERATORS↗
NASA NTRSDate not supplied

Gbps High Speed Antenna Arraying for Ground-Based Network

Combining the output signals from two or more ground station antennas can increase the gain of the received signal, providing the critical flexibility to increase the science data rate from space missions. NASA’s Near Space Network (NSN) has developed a gigabits/sec high rate antenna arraying system, based on the coherent combination of signals derived from multiple directive antennas. This arraying system is called the “High Data Rate Signal Combiner (HDRSC).” This arraying design approach/technology has been used previously at very low data rates. This work, however, focuses on gigabits/sec high rate antenna arraying system architecture. When coherently combining just two signals there is ideally a doubling of power, i.e., a 3dB signal-to-noise improvement. Arraying of small antennas can easily outperform a single large aperture antenna not only in radio-frequency performance but also in a substantial reduction of cost. This paper will cover the design approach for Gbps arraying, hardware architecture, test philosophy, and results.

antenna arraying↗
DOE OSTI2021

Physics-informed machine learning of the Lagrangian dynamics of velocity gradient tensor

Reduced models describing the Lagrangian dynamics of the velocity gradient tensor (VGT) in homogeneous isotropic turbulence (HIT) are developed under the physics-informed machine learning (PIML) framework. We consider the VGT at both Kolmogorov scale and coarse-grained scale within the inertial range of HIT. Building reduced models requires resolving the pressure Hessian and subfilter contributions, which is accomplished by constructing them using the integrity bases and invariants of the VGT. The developed models can be expressed using the extended tensor basis neural network (TBNN) introduced by Ling et al. [J. Fluid Mech. 807, 155 (2016)]. Physical constraints, such as Galilean invariance, rotational invariance, and incompressibility condition, are thus embedded in the models explicitly. Our PIML models are trained on the Lagrangian data from a high-Reynolds number direct numerical simulation (DNS). To validate the results, we perform a comprehensive out-of-sample test. We observe that the PIML model provides an improved representation for the magnitude and orientation of the small-scale pressure Hessian contributions. Statistics of the flow, as indicated by the joint PDF of second and third invariants of the VGT, show good agreement with the “ground-truth” DNS data. A number of other important features describing the structure of HIT are reproduced by the model successfully. We have also identified challenges in modeling inertial range dynamics, which indicates that a richer modeling strategy is required. This helps us identify important directions for future research, in particular towards including inertial range geometry into the TBNN.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

A faster and more reliable data acquisition system for the full performance of the SciCRT

The SciBar Cosmic Ray Telescope (SciCRT) is a massive scintillator tracker to observe cosmic rays at a very high-altitude environment in Mexico. The fully active tracker is based on the Scintillator Bar (SciBar) detector developed as a near detector for the KEK-to-Kamioka long-baseline neutrino oscillation experiment (K2K) in Japan. Since the data acquisition (DAQ) system was developed for the accelerator experiment, we determined to develop a new robust DAQ system to optimize it to our cosmic-ray experiment needs at the top of Mt. Sierra Negra (4600 m). One of our special requirements is to achieve a 10 times faster readout rate. We started to develop a new fast readout back-end board (BEB) based on 100 Mbps SiTCP, a hardware network processor developed for DAQ systems for high energy physics experiments. Then we developed the new BEB which has a potential of 20 times faster than the current one in the case of observing neutrons. Lastly, we installed the new DAQ system including the new BEBs to a part of the SciCRT in July 2015. The system has been operating since then. In this article, we describe the development, the basic performance of the new BEB, the status after the installation in the SciCRT, and the future performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗