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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Emulation Modeling for Development of Cyber-Defense Capabilities for Satellite Systems

The objective of this project was to develop a novel capability to generate synthetic data sets for the purpose of training Machine Learning (ML) algorithms for the detection of malicious activities on satellite systems. The approach experimented with was to a) generate sparse data sets using emulation modeling and b) enlarge the sparse data using Generative Adversarial Networks (GANs). We based our emulation modeling on the Open Source NASA Operational Simulator for Small Satellites (NOS3) developed by the Katherine Johnson Independent Verification and Validation (IV&V) program in West Virginia. Significant new capabilities on NOS3 had to be developed for our data set generation needs. To expand these data sets for the purpose of training ML, we experimented with a) Extreme Learning Machines (ELMs) and b) Wasserstein-GANs (WGAN-GP).

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components (Final Technical Report)

Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this report, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study. In another approach, we investigate Thermal Tomography (TT), which is a computational method for reconstruction of depth profile of internal material defects from PIT nondestructive evaluation (NDE). TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of subsurface internals defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. Performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shape imported from scanning electron microscopy (SEM) images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on TT images of elliptical defects is capable of classifying shape and orientation of irregular defects. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN. We also investigate several approaches based on Gaussian Random Circle and Bezier Curves for constructing parametric models of irregular-shape defects.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Next-Generation, High-temperature, High-frequency, High-efficiency, High-power-density Traction System

To meet performance and reliability requirements necessary for broader adoption of electric drive vehicles, the Electrical and Electronics Technical Team of the U.S. Drive partnership has established aggressive design goals for next-generation electric vehicle drivetrains. Specifically, the 2025 roadmap stipulates a 100 kW/L power density target and a $\$$2.7/kW cost target for power electronics, in addition to high-voltage operation (i.e., greater than 800 VDC). The additional targets for traction motor and the overall system performance impose further challenges on the power electronics design. For example, many high specific power machines have reduced iron content, and therefore reduced intrinsic filtering, thus requiring the inverter to supply a low-distortion drive current. These machines also typically have a high pole count, thus requiring drive current at a higher electrical frequency. Other motors, such as brush-less dc and switch reluctance machines, require a carefully-shaped, non-sinusoidal drive current (Yang, Shang, Brown, & Krishnamurthy, 2015), (Zhang, Bowman, O'Connel, & Haran, 2018), (Anderson, et al., 2018). Two- and three-level inverter topologies are the conventional framework for the power electronics design of the drivetrain, and some demonstrations have shown recent progress towards addressing cost, power density and efficiency goals (Gurpinar & Ozpineci, 2018), (Zhu, Kim, Chen, Erickson, & Maksimović, 2018), (Deshpande, Chen, Narayanasamy, Sathyanarayanan, & Luo, 2018), (Alizadeh, et al., 2019). However, an unconventional approach may be necessary to take the dramatic leap in power density necessitated by the roadmap—while simultaneously addressing the other system needs. Therefore, this project leverages the flying capacitor multilevel (FCML) topology, together with a scalable, modular approach, to address these needs. This type of hybrid converter has several advantages: lower voltage (i.e., less than 300 V) transistors can be used, energy-dense capacitors process most of the power, and the output current waveform is multilevel and exhibits a frequency multiplying effect—in other words, the output has reduced dv/dt and filtering requirements for the same high voltage dc bus. For example, in an electric vehicle with an 800 V bus, a 10-level FCML could leverage 100 V, commercially available GaN devices switching at 115 kHz to produce a ~1 MHz switching waveform (modulated according to the motor drive requirements) with one ninth of the dv/dt of a two-level converter. Prior work has already demonstrated promising performance and gravimetric power density figures for more electric aircraft applications (Pallo, Foulkes, Modeer, Coday, & Pilawa-Podgurski, 2018). This project leverages lessons learned to achieve the volumetric power density of 100 kW/L by employing advanced liquid cooling, address the 300,000 mile reliability challenge with redundant design, topology failure studies and online health monitoring, and reduce costs to $\$$2.7/kW through the use of low-cost GaN devices, modular converter assemblies, and modest modifications to traditional manufacturing methods. The project involved several hardware designs, each achieving increasing performance. At the conclusion of the project, a volumetric power density of 380 kW/L was achieved, in a 800V dc-ac converter, greatly surpassing even the aggressive target goal.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

AI Applications to Physics Experiments at Jefferson Lab

We survey how AI/ML is being deployed across Jefferson Lab's experimental and accelerator programs. In EPSCI, Hydra applies computer vision to automate real-time data-quality monitoring across all four experimental halls, replacing manual inspection of hundreds to thousands of histograms per shift. AIEC (AI Experiment Controls) uses ML to stabilize drift chamber gains and is now part of standard CEBAF production running, while AI Optimized Polarization (AIOP) targets autonomous control of polarized targets and photon beam angular alignment. In CASA, cavity fault classification models identify faulted cavities and trip types from waveform data with ~85% and ~78% agreement to labeled data, respectively, and are deployed in production; a separate effort applies LLMs and hybrid search to make the CEBAF operations logbook AI-ready. QCD-focused work includes transformer- and GAN-based generative models for particle-level event simulation, with distributed GAN training scaling studies on Polaris. Additional efforts span ML-on-FPGA for the EIC and a new Data Science Department coordinating anomaly detection, uncertainty quantification, and HPC-scalable ML lab-wide. Collectively, these projects illustrate AI's growing role in improving efficiency across JLab's nuclear physics mission.

Mei, Xinxin [Thomas Jefferson National Accelerator↗
DOE OSTI2021

Inpainting radar missing data regions with deep learning

Abstract. Missing and low-quality data regions are a frequent problem for weather radars. They stem from a variety of sources: beam blockage, instrument failure, near-ground blind zones, and many others. Filling in missing data regions is often useful for estimating local atmospheric properties and the application of high-level data processing schemes without the need for preprocessing and error-handling steps – feature detection and tracking, for instance. Interpolation schemes are typically used for this task, though they tend to produce unrealistically spatially smoothed results that are not representative of the atmospheric turbulence and variability that are usually resolved by weather radars. Recently, generative adversarial networks (GANs) have achieved impressive results in the area of photo inpainting. Here, they are demonstrated as a tool for infilling radar missing data regions. These neural networks are capable of extending large-scale cloud and precipitation features that border missing data regions into the regions while hallucinating plausible small-scale variability. In other words, they can inpaint missing data with accurate large-scale features and plausible local small-scale features. This method is demonstrated on a scanning C-band and vertically pointing Ka-band radar that were deployed as part of the Cloud Aerosol and Complex Terrain Interactions (CACTI) field campaign. Three missing data scenarios are explored: infilling low-level blind zones and short outage periods for the Ka-band radar and infilling beam blockage areas for the C-band radar. Two deep-learning-based approaches are tested, a convolutional neural network (CNN) and a GAN that optimize pixel-level error or combined pixel-level error and adversarial loss respectively. Both deep-learning approaches significantly outperform traditional inpainting schemes under several pixel-level and perceptual quality metrics.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1970

Growth of single-crystal gallium nitride

Use of ultrahigh purity ammonia prevents oxygen contamination of GaN during growth, making it possible to grow the GaN at temperatures as high as 825 degrees C, at which point single crystal wafers are deposited on /0001/-oriented sapphire surfaces.

Clough, R.↗
NASA NTRS1999

Morphology and Structure of ZnO Films Synthesized by Off-Axis Sputtering Deposition

ZnO is a wide-band-gap oxide material that has many applications. A new potential application of ZnO material is for light emitting devices since its structure and electrical properties are similar to that of the GaN material (a blue laser candidate). It also is a good substrate for fabricating GaN-based devices. Off-axis sputtering technique has revealed great potential in synthesizing excellent oxide materials because the negative ion bombardment is greatly reduced when adatoms condense on substrates. The surface of films grown by off-axis sputtering will be much smoother than that produced in a regular sputtering configuration. A growth mechanism is studied by investigating the morphology and structure of ZnO films under different growth conditions and orientations. ZnO films are deposited on (0001) sapphire and quartz substrates by off- axis sputtering deposition at various oxygen/argon mixture ratios and pressures and at different temperatures. All films reveal highly textured structures on quartz substrates and epitaxial growth on sapphire substrates. Two off-axis configurations, vertical and horizontal orientations are conducted to study the process of film growth, surface morphology, and film structure. X-ray diffraction, scanning probe microscopy, and electrical measurements are used to characterize these films. Detailed results will be discussed in the presentation. Keywords: ZnO, Photonic material, Off-axis sputtering, Growth mechanism

Zhu, Shen↗
NASA NTRS2001

Study of Electrical Contacts and Devices in Advanced Semiconductors

Research conducted at Tuskegee University concentrates on electrical contacts to GaN films and their characterization with the objective of understanding contact formation and realizing low-resistance metal contacts. Contact properties are known to be strongly related to surface preparation. It appears that the as-received material had a thin oxide film on the surface of the GaN film. Various cleaning treatments were employed in order to render the surface contamination free and removal of the oxide film. Metal films were then deposited by e-beam evaporation. Electrical characteristics of these contacts indicated that the optimal treatment was an organic solvent cleaning followed by etching in buffered oxide solution. Contacts established with Al were observed to be ohmic in nature, whereas Au, Cr, Ti, and Pt exhibit rectifying contacts. Platinum contacts were almost ideal as shown by an ideality factor of 1.02.

Hall, H. P.↗
NASA NTRS2009

An LED Approach for Measuring the Photocatalytic Breakdown of Crystal Violet Dye

A simple technique to assess the reactivity of photocatalytic coatings sprayed onto transmissive glass surfaces was developed. This new method uses ultraviolet (UV) gallium nitride (GaN) light-emitting diodes (LEDs) to drive a photocatalytic reaction (the photocatalytic breakdown of a UV-resistant dye applied to a surface coated with the semiconductor titanium dioxide); and then a combination of a stabilized white light LED and a spectrometer to track the dye degradation as a function of time. Simple, standardized evaluation techniques that assess photocatalytic materials over a variety of environmental conditions, including illumination level, are not generally available and are greatly needed prior to in situ application of photocatalytic technologies. To date, much research pertaining to this aspect of photocatalysis has been limited and has focused primarily on laboratory experiments using mercury lamps. Mercury lamp illumination levels are difficult to control over large ranges and are temporally modulated by line power, limiting their use in helping to understand and predict how photocatalytic materials will behave in natural environmental settings and conditions. The methodology described here, using steady-state LEDs and time series spectroradiometric techniques, is a novel approach to explore the effect of UV light on the photocatalytic degradation of a UV resistant dye (crystal violet). GaN UV LED arrays, centered around 365 nm with an adjustable DC power supply, are used to create a small, spatially uniform light field where the steady state light level can be varied over three to four orders of magnitude. For this study, a set of glass microscope slides was custom coated with a thinly sprayed layer of photocatalytic titanium dioxide. Crystal violet was then applied to these titanium-dioxide coated slides and to uncoated control slides. The slides were then illuminated at various light levels from the dye side of the slide by the UV LED array. To monitor dye degradation on the slides over time, a temperature-stabilized white light LED was used to illuminate the opposite side of the slides. As the dye degraded, the amount of light from the white light LED transmitted through the slide was monitored with a spectrometer and subsequently analyzed to determine and compare the rate of dye degradation for photocatalytically coated versus uncoated slide surfaces. The long-term stability of the spectrometer/white light LED combination, which required only a single reference spectra to be taken for a time series sequence of several hours, enabled accurate measurements of transmitted light over time. Time series transmission curves were generated and results demonstrated that over time the transmission increased much more rapidly on the coated slides than on the control slides. This experimental configuration and methodology for photocatalytic activity measurement minimizes many external variable effects and allows low light level studies to be performed. This study also compares the advantages of this novel LED light source design to traditional mercury lamp systems and non-LED lamp approaches that have conventionally been used. The methodology and experimental design research summarized in this abstract is partly funded by the Department of Homeland Security, Science and Technology Directorate, and by the NASA Stennis Space Center Innovative Partnerships Program.

Ryan, Robert E.↗
NASA NTRS2010

Power Amplifier Module with 734-mW Continuous Wave Output Power

Research findings were reported from an investigation of new gallium nitride (GaN) monolithic millimeter-wave integrated circuit (MMIC) power amplifiers (PAs) targeting the highest output power and the highest efficiency for class-A operation in W-band (75-110 GHz). W-band PAs are a major component of many frequency multiplied submillimeter-wave LO signal sources. For spectrometer arrays, substantial W-band power is required due to the passive lossy frequency multipliers-to generate higher frequency signals in nonlinear Schottky diode-based LO sources. By advancing PA technology, the LO system performance can be increased with possible cost reductions compared to current GaAs PAs. High-power, high-efficiency GaN PAs are cross-cutting and can enable more efficient local oscillator distribution systems for new astrophysics and planetary receivers and heterodyne array instruments. It can also allow for a new, electronically scannable solid-state array technology for future Earth science radar instruments and communications platforms.

Fung, King Man↗
NASA NTRS2003

Transport Equations for CAD Modeling of Al(x)Ga(1-x)N/GaN HEMTs

BEMTs formed from Al(x)Ga(1-x)N/GaN heterostructures are being investigated for high RF power and efficiency around the world by many groups, both academic and industrial. In these devices, the 2DEG formation is dominated by both spontaneous and piezoelectric polarization fields, with each component having nearly the same order of magnitude. The piezoelectric portion is induced by the mechanical strain in the structure, and to analyze these devices, one must incorporate the stress/strain relationships, along with the standard semiconductor transport equations. These equations for Wurtzite GaN are not easily found in the open literature, hence this paper summarizes them, along with the constitutive equations for piezoelectric materials. The equations are cast into the format for the Wurtzite crystal class, which is the most common way GaN is grown epitaxially.

Freeman, Jon C.↗
NASA NTRS2019

A Communication Channel Density Estimating Generative Adversarial Network

Autoencoder-based communication systems use neural network channel models to backwardly propagate message reconstruction error gradients across an approximation of the physical communication channel. In this work, we develop and test a new generative adversarial network (GAN) architecture for the purpose of training a stochastic channel approximating neural network. In previous research, investigators have focused on additive white Gaussian noise (AWGN) channels and/or simplified Rayleigh fading channels, both of which are linear and have well defined analytic solutions. Given that training a neural network is computationally expensive, channel approximation networks— and more generally the autoencoder systems—should be evaluated in communication environments that are traditionally difficult. To that end, our investigation focuses on channels that contain a combination of non-linear amplifier distortion, pulse shape filtering, intersymbol interference, frequency-dependent group delay, multipath, and non-Gaussian statistics. Each of our models are trained without any prior knowledge of the channel. We show that the trained models have learned to generalize over an arbitrary amplifier drive level and constellation alphabet. We demonstrate the versatility of our GAN architecture by comparing the marginal probability density function of several channel simulations with that of their corresponding neural network approximations

Smith, Aaron↗
NASA NTRS2019

Multi-Band and Multi-Function Reconfigurable Gallium Nitride Based Fully Solid-State Microwave Power Module for Cognitive Radio/Radar Platforms

This paper presents as a proof-of-concept (POC) the design, integration, and performance of a novel reconfigurable S-/X-band Gallium Nitride (GaN) based fully solid-state microwave power module (SSMPM) for the role as the transmit module in a cognitive radio (CR). The SSMPM synergistically integrates multiple amplifiers through diplexing and high power switches to enable a single SSMPM capable of functioning as both S-/X-band amplifiers for telemetry, tracking, and command (TT&C), telecommunications, and science data downlink or as X-band radar for proximity sensing onboard a planetary exploration spacecraft. Integration of an electric field shaping field plate (FP) onto the GaN high electron mobility transistors (HEMTs) in this SSMPM provides increased performance and reliability for operation in the harsh conditions of space. This SSMPM is capable of delivering saturated power (Psat) of 39 dBm (8 W continuous wave (CW)) at S-band, P(sub sat) of 43 dBm (20 W CW) at X-band, and P(sub sat) of >50 dBm (>100 W Pulsed) at X-band.

Simons, Rainee N.↗
NASA NTRSDate not supplied

Analyzing Natural Language Context in Human-Machine Teaming using Supervised Machine Learning

Building a foundation for trustworthiness and trust verification in multi-asset teaming is the research challenge of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR). The Design Reference Mission (DRM) for ATTRACTOR is a search and rescue mission objective governed by a multi-member team consisting of human and machine operators. A crucial component to the effort is the communication between humans and autonomous agents throughout both planning and execution stages of the mission. Intuitive communication methods and modalities are posited as critical enablers for certifying trust and trustworthiness. This paper reports on the data collection and analysis conducted in support of the Human Informed Natural-language GANs Evaluation (HINGE)project to attain explainable and trusted communication between human-machine assets. Two identically curated image description datasets were acquired for HINGE, both consisting of two unique input modalities (typed vs. verbal) and retrieved in two distinct contexts (general vs. specific). The gathered datasets were assessed and compared using Parts-of-Speech (POS)features, sentence similarity metrics, and linguistic analysis. Then, the datasets were modeled and tested separately and in combination with one another using machine learning algorithms. The comparison and testing results reveal a superior dataset, by which a preferred context and input is understood, for generating image representations of missing persons using a Generative Adversarial Network (GAN).

Bryan A Barrows↗
NASA NTRS2021

Development of Gallium Nitride Monolithic Microwave Integrated Circuits for Ka-Band Remote Sensing

We have been developing gallium nitride (GaN)monolithic microwave integrated circuits (MMICs) towards thegoal of implementing a single chip transceiver frontend forcloud radar arrays at Ka-band. We present our first iterationdesign, fabrication and test cycle implementation of GaN poweramplifiers (PAs), low noise amplifiers (LNAs), driver amplifier,single-pole- double and triple throw switches, and voltagecontrolled attenuator. These components are necessary forimplementing a transceiver unit cell concept with digitalcalibration capabilities to enable scalable array sizes for radarand radiometry.

Cao, Yu↗
NASA NTRSDate not supplied

Generative Framework Approach to Match Landsat and Sentinel-2 Data

Linear regression and histogram matching based techniques have been widely used to minimize the surface reflectance difference between two similar satellite observations such as Landsat-8/9 and Sentinel-2A/B products [1]. However, regionally or globally derived conversion factors may not be suitable for all land cover types and locations, resulting in noticeable residual differences between the sensors. Generative Adversarial Network (GAN) has shown promise in the field of image processing for domain or style transfer[2]. In this work we aim to minimize the surface reflectance difference between Landsat and Sentinel-2 products based on GAN.

Sujit Roy↗
DOE OSTI2020

Printed assemblies of ultrathin, microscale inorganic light emitting diodes for deformable and semitransparent displays

Described herein are printable structures and methods for making, assembling and arranging electronic devices. A number of the methods described herein are useful for assembling electronic devices where one or more device components are embedded in a polymer which is patterned during the embedding process with trenches for electrical interconnects between device components. Some methods described herein are useful for assembling electronic devices by printing methods, such as by dry transfer contact printing methods. Also described herein are GaN light emitting diodes and methods for making and arranging GaN light emitting diodes, for example for display or lighting systems.

42 ENGINEERING↗
DOE OSTI2020

Deep photoenhanced wet material etching using high-power ultraviolet light emitting diodes

Methods and systems for etching a substrate using photoenhanced wet etching techniques are described. At least one light emitting diode source is used to create a high intensity of ultraviolet light at the surface of the substrate or at one or more layers formed on the substrate. Etching rates in GaN substrates and GaN layers are improved by an order of magnitude over conventional systems. Systems and methods for forming a device structure free of a substrate are described. The device structure is grown or applied over a release layer on a substrate. The device structure is exposed to photoenhanced wet etch environments to vertically and laterally etch the release layer to separate the device structure from the substrate.

36 MATERIALS SCIENCE↗