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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Coupled Experimental and High-Temperature Discrete-Element Method Modeling Studies of Aluminosilicate Particle Handling in Concentrated Solar Power Environments

Chemically inert, aluminosilicate based particles have been investigated as both a thermal transport and sensible energy storage medium for concentrated solar power facilities. These particles will experience a wide range of operating temperatures (300-1000 K) and handling conditions (dense to dilute falling particle curtains, dense granular flows, or dense structures), requiring specially-designed and optimized infrastructures. The relative influence of collisional and frictional interactions between particles varies based on temperature-dependent particulate properties and greatly impacts the bulk, granular flow behavior. These underlying physics are captured using discrete element method modeling tools. However, this modeling method is computationally expensive as each particle position and interaction is tracked during the simulation. These modeling methods are further complicated by introducing temperature-dependent particle properties, high-temperature radiative exchange, and directional irradiation sources experienced by granular flows in concentrated solar power environments. Coupled experimental and numerical studies of aluminosilicate particles in rotary kilns and dense particle curtains were performed for bulk temperatures up to 1073 K. The three particle types investigated included Carbobead HSP 30 /60, Carbobead CP 30/60, and Granusil 4030. Temperature, spatial, and velocity profile data were extracted from experimental runs using embedded K-type thermocouple probes and particle image velocimetry techniques. Experimental and numerical studies were compared using spatial temperature profiles, velocity fields, and shape profiles of the bulk, granular flows. Numerical models were developed using commercially available discrete element method modeling software, Aspherix®. Existing Aspherix® functionality was expanded by introducing coupled radiative exchange modeling tools. The laboratory-scale rotary kiln was developed to investigate the steady-state heat and mass transfer performance of aluminosilicate particles based on particle type, bulk handling temperature, and wall roughness. The rotational speed of the rotary kiln was varied to control the relative impact of collisional and frictional effects upon the granular flow behavior. Heat and mass transfer performance was categorized based on the Froude number and the observed flow regimes of slipping, rolling, cascading, and centrifuging. Coupled discrete element method modeling studies were used to evaluate the effects of temperature-dependent, particulate mechanical properties upon bulk flow behavior and upon the relative effects of radiative, advective, and/or conductive heat transfer. A high-temperature (< 1073 K) falling particle curtain was similarly fabricated to investigate the heat and mass transfer performance of aluminosilicate particles in particle handling situations dominated by inter-particle collisions. The impact of particle type, flow preheat temperatures (< 1073K), and bulk mass flow rates were investigated upon the particle curtain shape, temperature, and velocity profiles. Coupled discrete element method modeling studies were performed to evaluate the varying impact of temperature-dependent, particulate mechanical properties on the bulk flow behavior and the temperature profile of the particle curtain.

14 SOLAR ENERGY↗
NASA NTRS2001

Plasma Liner Development for MTF Applications: A Status Report

An experimental plasma gun for Magnetic Target Fusion (MTF) applications under development at the NASA Marshall Space Flight Center is described. This gun has been tested experimentally and plasma jet velocities of approximately 50 km/sec have been obtained. The plasma jet structure has been photographed with 10 ns exposure times to reveal a stable and repeatable plasma structure. Data for velocity profile information has been obtained using light pipes embedded in the gun walls to record the plasma transit at various barrel locations. A high speed triggering system has been developed and tested as a means of reducing the gun "jitter". This jitter has been characterized and future work for second generation "ultra-low jitter" gun development is identified.

Eskridge, R. E.↗
NASA NTRS1984

Plasma and energetic particle structure upstream of a quasi-parallel interplanetary shock

ISEE 1, 2 and 3 data from 1978 on interplanetary magnetic fields, shock waves and particle energetics are examined to characterize a quasi-parallel shock. The intense shock studied exhibited a 640 km/sec velocity. The data covered 1-147 keV protons and electrons and ions with energies exceeding 30 keV in regions both upstream and downstream of the shock, and also the magnitudes of ion-acoustic and MHD waves. The energetic particles and MHD waves began being detected 5 hr before the shock. Intense halo electron fluxes appeared ahead of the shock. A closed magnetic field structure was produced with a front end 700 earth radii from the shock. The energetic protons were cut off from the interior of the magnetic bubble, which contained a markedly increased density of 2-6 keV protons as well as the shock itself.

Kennel, C. F.↗
NASA NTRS1983

Stability experiments in rotating-disk flow

An experimental study of the transitional flow on a flat disk, rotating in still air, has been conducted. Using digitized hot-wire data, the axes of the stationary spiral vortices, which are the primary instability mechanism for the disk flow, have been mapped-out in terms of both spatial coordinates and velocity fluctuations. Data are presented for a clean disk and for a disk with a single, isolated roughness element. The data show that the disk vortices are generated at discrete roughness disturbance sites on the disk and that they propagate and grow as wave packets. The familiar vortex pattern of 30 or so vortices results only when these wave packets have merged and filled the entire circumference. The appearance of stationary, secondary vortices prior to turbulent breakdown has also been observed. Comparisons with linear stability theory show reasonable agreement with measured growth rate data.

Wilkinson, S. P.↗
NASA NTRS1976

Vertical wind velocities from superpressure balloons - A case study using Eole data

A method for determining vertical wind velocities using the pressure- and temperature-change data from superpressure (constant-density) balloons was proposed in an earlier paper (1975). In this paper, we present a case study using actual balloon data to test the method. Data from the Eole balloon experiment were used to estimate the sign of vertical wind velocities, as well as the horizontal velocity field, near 200 mb over Australia. The resulting patterns of vertical velocity compare favorably with satellite pictures and with current models of flow near jet-stream maxima. Additionally, this study provides evidence that areas of vertical motion in the troposphere extend upward into the lower stratosphere without changing sign.

Banta, R. M.↗
NASA NTRS2013

RUSHMAPS: Real-Time Uploadable Spherical Harmonic Moment Analysis for Particle Spectrometers

RUSHMAPS is a new onboard data reduction scheme that gives real-time access to key science parameters (e.g. moments) of a class of heliophysics science and/or solar system exploration investigation that includes plasma particle spectrometers (PPS), but requires moments reporting (density, bulk-velocity, temperature, pressure, etc.) of higher-level quality, and tolerates a lowpass (variable quality) spectral representation of the corresponding particle velocity distributions, such that telemetry use is minimized. The proposed methodology trades access to the full-resolution velocity distribution data, saving on telemetry, for real-time access to both the moments and an adjustable-quality (increasing quality increases volume) spectral representation of distribution functions. Traditional onboard data storage and downlink bandwidth constraints severely limit PPS system functionality and drive cost, which, as a consequence, drives a limited data collection and lower angular energy and time resolution. This prototypical system exploit, using high-performance processing technology at GSFC (Goddard Space Flight Center), uses a SpaceCube and/or Maestro-type platform for processing. These processing platforms are currently being used on the International Space Station as a technology demonstration, and work is currently ongoing in a new onboard computation system for the Earth Science missions, but they have never been implemented in heliospheric science or solar system exploration missions. Preliminary analysis confirms that the targeted processor platforms possess the processing resources required for realtime application of these algorithms to the spectrometer data. SpaceCube platforms demonstrate that the target architecture possesses the sort of compact, low-mass/power, radiation-tolerant characteristics needed for flight. These high-performing hybrid systems embed unprecedented amounts of onboard processing power in the CPU (central processing unit), FPGAs (field programmable gate arrays), and DSP (digital signal processing) elements. The fundamental computational algorithm de constructs 3D velocity distributions in terms of spherical harmonic spectral coefficients (which are analogous to a Fourier sine-cosine decomposition), but uses instead spherical harmonics Legendre polynomial orthogonal functions as a basis for the expansion, portraying each 2D angular distribution at every energy or, geometrically, spherical speed-shell swept by the particle spectrometer. Optionally, these spherical harmonic spectral coefficients may be telemetered to the ground. These will provide a smoothed description of the velocity distribution function whose quality will depend on the number of coefficients determined. Successfully implemented on the GSFC-developed processor, the capability to integrate the proposed methodology with both heritage and anticipated future plasma particle spectrometer designs is demonstrated (with sufficiently detailed design analysis to advance TRL) to show specific science relevancy with future HSD (Heliophysics Science Division) solar-interplanetary, planetary missions, sounding rockets and/or CubeSat missions.

Figueroa-Vinas, Adolfo↗
DOE OSTI2024

Efficient Anomaly Detection Driven By Different Machine Learning Architectures And Models

The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.

Marfo, William↗
NASA NTRS1976

Jet noise characteristics of unsuppressed duct burning turbofan exhaust system

Recent aero-acoustic tests of model coannular nozzles have shown that less noise is generated if the higher-velocity jet is exhausted from the outer annular passage rather than from the primary nozzle. These findings are of particular significance to a duct-burning turbofan (DBTF) engine being studied for application to an advanced supersonic transport. Unlike conventional turbofan engines that have peak velocities from the primary nozzle, it is possible to design a DBTF engine to have a fan velocity higher than that of the primary flow. Results are presented for a NASA-sponsored model test program that covers a range of fan to primary-area ratios from 0.75 to 1.2, and a range of fan to primary-velocity ratios from 0.4 to 2.8. Correlations are given that relate radiated sound power to fan velocity, fan to primary-velocity ratio, and fan to primary-area ratio. Corresponding exhaust-plume velocity-traverse data are presented which suggest that the observed noise benefits may be due to the more rapid decay of the annular flow because of shear stresses on the inner surface that result from the lower-velocity primary flow.

Packman, A. B.↗
DOE OSTI2021

Bayesian calibration of a physics-based crystal plasticity and damage model

In this work, we present a model parameter calibration procedure for a physics-based crystal plasticity model. The calibration process utilizes a powerful statistics-based Bayesian calibration method. Calibration of the crystal plasticity parameters makes use of experimentally-measured data, i.e. compressive stress–strain response, from $\langle$100$\rangle$ and $\langle$123$\rangle$ single crystal copper dynamically loaded via Kolsky bar tests. The calibration of damage parameters is achieved using experimentally-measured free-surface velocity history data from plate impact test on $\langle$100$\rangle$ and $\langle$110$\rangle$ single crystal copper, which generates shock compression followed by dynamic tensile failure. A validation assessment is then carried out by comparing the calibrated model predictions and experimental measurements of the dynamic tensile damage generated in an impacted bicrystal copper plate. Lastly, a model-informed rationale for the experimentally-observed dependence of the spatial distribution of ductile damage (porosity) on crystallography is provided.

42 ENGINEERING↗
NASA NTRS1976

Turbulence measurements in a transonic boundary layer and free-shear flow using laser velocimetry and hot-wire anemometry techniques

Quantitative measurements of the turbulence fluctuations in velocity and mass flux have been obtained in Mach 0.6 and 0.8 turbulent boundary layer and free-shear layer flows by laser velocimetry and hot-wire anemometry techniques. To evaluate the effects of compressibility, these transonic data are compared to available incompressible and supersonic results. Based on some simplifying assumptions, estimates of the rms density fluctuations are made for which error bounds are given. In addition to these fluctuation data, the compressible mean velocity data obtained with the laser velocimeter are presented and compared to pitot tube results. The investigation was conducted in the Ames 6- by 6-Foot Supersonic Wind Tunnel at free-stream Mach numbers of 0.6 and 0.8 for a unit Reynolds number of about 10,000,000 per meter.

Johnson, D. A.↗
DOE OSTI2020

Data Federation Challenges in Remote Near-Real-Time Fusion Experiment Data Processing

Fusion energy experiments and simulations provide critical information needed to plan future fusion reactors. As next-generation devices like ITER move toward long-pulse experiments, analyses, including AI and ML, should be performed in a wide range of time and computing constraints, from near-real-time constraints, between-shot analysis, and to campaign-wide long-term analysis. However, the data volume, velocity, and variety make it extremely challenging for analyses using only local computational resources. Researchers need the ability to compose and execute workflows spanning edge resources to large-scale high-performance computing facilities.We present Delta, a system to address data analysis challenges, including AI/ML, in fusion science, by leveraging the ADIOS I/O library and middleware, to support executing science workflows over the wide area network for near-real-time streaming. We discuss the data federation challenges in performing remote workflows, focusing on on-going research work in (1) managing, reducing, and streaming data to minimize I/O and data movement overheads, (2) decompressing and reorganizing data for analysis, and (3) executing workflows for automated data analysis. We introduce examples for deep-learning based data analysis for the fusion domain and demonstrate how we use Delta to construct end-to-end workflows for a fusion device in Korea, connecting a remote DOE facility in the USA. The capability demonstrated by this project is the basis for improving the state of the art for near-real-time data federation amongst remote facilities.

Choi, Jong Youl↗
DOE OSTI2020

The Manufacturing Data and Machine Learning Platform: Enabling Real-time Monitoring and Control of Scientific Experiments via IoT

IoT devices and sensor networks present new opportunities for measuring, monitoring, and guiding scientific experiments. Sensors, cameras, and instruments can be combined to provide previously unachievable insights into the state of ongoing experiments. However, IoT devices can vary greatly in the type, volume, and velocity of data they generate, making it challenging to fully realize this potential. Indeed, synergizing diverse IoT data streams in near-real time can require the use of machine learning (ML). In addition, new tools and technologies are required to facilitate the collection, aggregation, and manipulation of sensor data in order to simplify the application of ML models and in turn, fully realize the utility of IoT devices in laboratories. Here we will demonstrate how the use of the Argonne-developed Manufacturing Data and Machine Learning (MDML) platform can analyze and use IoT devices in a manufacturing experiment. MDML is designed to standardize the research and operational environment for advanced data analytics and AI-enabled automated process optimization by providing the infrastructure to integrate AI in cyber-physical systems for in situ analysis. We will show that MDML is capable of processing diverse IoT data streams, using multiple computing resources, and integrating ML models to guide an experiment.

Machine Learning↗
NASA NTRS1990

Measurements in a separation bubble on an airfoil using laser velocimetry

An experimental investigation was conducted to measure the reverse flow within the transitional separation bubble that forms on an airfoil at low Reynolds numbers. Measurements were used to determine the effect of the reverse flow on integrated boundary-layer parameters often used to model the bubble. Velocity profile data were obtained on an NACA 663-018 airfoil at angle of attack of 12 deg and a chord Reynolds number of 140,000 using laser Doppler and single-sensor hot-wire anemometry. A new correlation is proposed based on zero velocity position, since the Schmidt (1986) correlations fail in the turbulent portion of the bubble.

Fitzgerald, Edward J.↗
NASA NTRS1986

Ignition of metals and alloys in gaseous oxygen by frictional heating

The ignition of metals and alloys have been investigated by rotating the end of a hollow cylinder against an identical stationary cylinder on a common axis (frictional heating) in gaseous oxygen. A ranking criterion that measures the resistance of metals and alloys to ignition is discussed. It consists of the power per unit area required for ignition conveniently expressed as the product of contact pressure P and average linear velocity ALV. Data are presented that demonstrate that materials that are high in nickel and copper require greater P-ALV products for ignition (more resistant to ignition) than materials that are high in iron. Aluminum and titanium alloys are shown to require the lowest P-ALV products for ignition. The effects of varying surface velocity, contact pressure, coefficient of friction, and oxygen pressure on P-ALV products required for ignition are discussed. The results indicate that P-ALV product required for ignition increases as surface velocity increases, as contact pressure decreases, and as the coefficient of friction decreases. Increasing oxygen pressure will cause the P-ALV product required for ignition to decrease at low pressures and increase at high pressures. In some cases, the relative ranking of materials based on P-ALV products will also change as conditions are varied. The data presented in this paper are from a development program to determine the merits of using such a dynamic test method for ranking materials for oxygen service. The results to date have indicated that more testing is required before this method can be made into a standard test. The data presented do show some of the major parameters affecting the ignition of materials when exposed to frictional heating. The repeatability of the data at any set of conditions have not been thoroughly investigated.

Benz, F. J.↗
NASA NTRSDate not supplied

Development of a Coherent Doppler Lidar for Precision Landing on Planetary Bodies

A coherent Doppler lidar has been developed by NASA for providing vector velocity and altitude data to landing vehicles. Future robotic and manned missions to planetary bodies demand precise ground-relative velocity and altitude data to execute complex descent maneuvers for safe, soft and pinpoint landing at a pre-designated site. Operating from over five kilometers altitude, this lidar provides velocity and range data within a few cm/sec and a few meters precision, respectively, depending on the vehicle dynamics. Two upcoming lunar landing missions will serve as the technology demonstration for robotic and manned landing missions to the Moon, Mars, and other solar system destinations. This paper describes the lidar design and its expected performance on landing vehicles.

Coherent Laser Radar↗
NASA NTRS2008

A Performance Assessment of an Airborne Separation Assistance System Using Realistic Complex Traffic Flows

This paper presents the results from a study that investigates the performance of a tactical Airborne Separation Assistance System (ASAS) in en route airspace, under varying demand levels, with realistic traffic flows. The ASAS concept studied here allows flight crews of equipped aircraft to perform separation from other air traffic autonomously. This study addresses the tactical aspects of an ASAS using aircraft state data (i.e. position and velocity) to detect and resolve projected conflicts. In addition, use of a conflict prevention system helps ASAS-equipped aircraft avoid maneuvers that may cause new conflicts. ASAS-capable aircraft are equipped with satellite-based navigation and Automatic Dependent Surveillance Broadcast (ADS-B) for transmission and receipt of aircraft state data. In addition to tactical conflict detection and resolution (CD&R), a complete, integrated ASAS is likely to incorporate a strategic CD&R component with a longer look-ahead time, using trajectory intent information. A system-wide traffic flow management (TFM) component, located at the FAA command center helps aircraft to avoid regions of excessive traffic density and complexity. A Traffic Alert and Collision Avoidance System (TCAS), as used today is the system of last resort. This integrated approach avoids sole reliance on the use of the tactical CD&R studied here, but the tactical component remains a critical element of the complete ASAS. The focus of this paper is to determine to what extent the proposed tactical component of ASAS alone can maintain aircraft separation at demand levels up to three times that of current traffic. The study also investigates the effect of mixing ASAS-equipped aircraft with unequipped aircraft (i.e. current day) that do not have the capability to self-separate. Position and velocity data for unequipped aircraft needs to be available to ASASequipped. Most likely, for this future concept, state data would be available from instrument flight rules (IFR) aircraft, equipped with at least ADS-B transmission capability. The objective is to reduce the number of losses of separation to a minimum and investigate the limits of tactical-only CD&R. Thus, the objective is not, expressly, to achieve zero losses of separation with tactical ASAS because this is one component of an integrated ASAS.

Smith, Jeremy C.↗
DOE OSTI2020

Using Deep Learning to Derive Shear-Wave Velocity Models from Surface-Wave Dispersion Data

We present a new algorithm for derivations of 1D shear-wave velocity models from surface-wave dispersion data using convolutional neural networks (CNNs). The technique is applied for continental China and the plate boundary region in southern California. Different CNNs are designed for these two regions and are trained using theoretical Rayleigh-wave phase and group velocity images computed from reference 1D VS models. The methodology is tested with 3260 phase–group images for continental China and 4160 phase–group images for southern California. The conversions of these images to velocity profiles take ~23 s for continental China and ~30 s for southern California on a personal laptop with the NVIDIA GeForce GTX 1060 core and a memory of 6 GB. The results obtained by the CNNs show high correlation with previous studies using conventional methods. The effectiveness of the CNN technique makes this fast method an important alternative for deriving shear-wave velocity models from large datasets of surface-wave dispersion data.

Geochemistry & Geophysics↗