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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 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Personalized and uncertainty-aware coronary hemodynamics simulations: From Bayesian estimation to improved multi-fidelity uncertainty quantification

Non-invasive simulations of coronary hemodynamics have improved clinical risk stratification and treatment outcomes for coronary artery disease, compared to relying on anatomical imaging alone. However, simulations typically use empirical approaches to distribute total coronary flow amongst the arteries in the coronary tree, which ignores patient variability, the presence of disease, and other clinical factors. Further, uncertainty in the clinical data often remains unaccounted for in the modeling pipeline. We present an end-to-end uncertainty-aware pipeline to (1) personalize coronary flow simulations by incorporating vessel-specific coronary flows as well as cardiac function; and (2) predict clinical and biomechanical quantities of interest with improved precision, while accounting for uncertainty in the clinical data. We assimilate patient-specific measurements of myocardial blood flow from clinical CT myocardial perfusion imaging to estimate branch-specific coronary artery flows. Simulated noise in the clinical data is used to estimate the joint posterior distributions of the model parameters using adaptive Markov Chain Monte Carlo sampling. Additionally, the posterior predictive distribution for the relevant quantities of interest is determined using a new approach combining multi-fidelity Monte Carlo estimation with non-linear, data-driven dimensionality reduction. This leads to improved correlations between high- and low-fidelity model outputs. Our framework accurately recapitulates clinically measured cardiac function as well as branch-specific coronary flows under measurement noise uncertainty. We observe substantial reductions in confidence intervals for estimated quantities of interest compared to single-fidelity Monte Carlo estimation and state-of-the-art multi-fidelity Monte Carlo methods. This holds especially true for quantities of interest that showed limited correlation between the low- and high-fidelity model predictions. In addition, the proposed multi-fidelity Monte Carlo estimators are significantly cheaper to compute than traditional estimators, under a specified confidence level or variance. The proposed pipeline for personalized and uncertainty-aware predictions of coronary hemodynamics is based on routine clinical measurements and recently developed techniques for CT myocardial perfusion imaging. The proposed pipeline offers significant improvements in precision and reduction in computational cost.

Bayesian parameter estimation↗
DOE OSTI2022

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules

Abstract Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN surrogate for molecular design requires large-scale graph datasets and is usually a time-consuming process. Recent advances in GPUs and distributed computing open a path to reduce the computational cost for GCNN training effectively. However, efficient utilization of high performance computing (HPC) resources for training requires simultaneously optimizing large-scale data management and scalable stochastic batched optimization techniques. In this work, we focus on building GCNN models on HPC systems to predict material properties of millions of molecules. We use HydraGNN, our in-house library for large-scale GCNN training, leveraging distributed data parallelism in PyTorch. We use ADIOS, a high-performance data management framework for efficient storage and reading of large molecular graph data. We perform parallel training on two open-source large-scale graph datasets to build a GCNN predictor for an important quantum property known as the HOMO-LUMO gap. We measure the scalability, accuracy, and convergence of our approach on two DOE supercomputers: the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) and the Perlmutter system at the National Energy Research Scientific Computing Center (NERSC). We present our experimental results with HydraGNN showing (i) reduction of data loading time up to 4.2 times compared with a conventional method and (ii) linear scaling performance for training up to 1024 GPUs on both Summit and Perlmutter.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2014

Experimental Investigation of a Flexible Wing with a Variable Camber Continuous Trailing Edge Flap Design

This paper presents experimental results of a flexible wing wind tunnel model with a variable camber continuous trailing edge flap (VCCTEF) design for drag minimization, tested at the University of Washington Aeronautical Labo- ratory (UWAL). The wind tunnel test was designed to explore the relative merit of the VCCTEF concept for improved cruise efficiency through the use of low-cost aeroelastic model test techniques. The flexible wing model is a 10-scaled model of a typical transport wing and is constructed of woven fabric composites and foam core. The wing structural stiffness in bending is tailored to be half of the stiffness of a Boeing 757-era transport wing, while the torsional stiffness is about the same. This stiffness reduction results in a wing tip deflection of about 10 of the wing semi-span. The VCCTEF is a multi-segment flap design having three chordwise camber segments and five spanwise flap sections for a total of 15 individual flap elements. The three chordwise camber segments can be positioned appropriately to create a desired trailing edge camber. Elastomeric material is used to cover the gaps in between the spanwise flap sections, thereby creating a continuous trailing edge. Wind tunnel data indicate a high degree of data correlation and repeata- bility. The VCCTEF can achieve a drag reduction of up to 6.31 and an improvement in the lift-to-drag ratio (LD) of up to 4.85. The paper also presents two methods for estimating the lift coefficient of a rigid wing using a dynamic pressure correction and an aeroelastic deflection correction. Both methods provide good estimates of the rigid-wing lift coefficient.

Aerodynamics↗
NASA NTRSDate not supplied

Hydrogen Plasma Reduction of Silicates for Lunar Oxygen Liberation

As crewed missions to the moon become closer, the focus on in-situ resource utilization efforts has increased. Since lunar soil has abundant oxygen on the surface of the moon, work has been done to process this resource in an efficient manner. Hydrogen reduction of lunar regolith has emerged as a method of liberating oxygen that can be used for fuel and water production. Traditional reduction techniques require high temperatures and increased complexity when dealing with high amounts of silicates. Plasma can reduce silicates efficiently and thus has become of high interest for use in the lunar highlands where silicates exist in high quantities. NASA’s Kennedy Space Center is exploring the use of low temperature plasmas for the reduction of silicates. This work shows that exposing silicates to a hydrogen plasma produces water. Residual gas analysis and optical emission spectroscopy were used to measure the relative quantities of water vapor production and OH (A-X) emission for hydrogen plasma interactions with lunar highland simulant and silica. X-ray photo-electron spectroscopy and scanning electron microscopy/energy-dispersive X-ray spectroscopy were also used to observe surface modifications to the silicates that reflect the key chemical reactions that occurred. From this data, plasma has emerged as a promising solution to produce oxygen on the lunar surface. *Work supported by NASA's Science Technology Mission Directorate Center Innovation Fund.

R. P. Gott↗
NASA NTRSDate not supplied

Plasma Reduction of Regolith for Oxygen Liberation

As crewed missions to the moon become closer, the focus on in-situ resource utilization efforts has increased. Since lunar soil has abundant oxygen on the surface of the moon, work has been done to process this resource in an efficient manner. Hydrogen reduction of lunar regolith has emerged as a method of liberating oxygen that can be used for fuel and water production. Traditional reduction techniques require high temperatures and increased complexity when dealing with high amounts of silicates. Plasma can reduce silicates efficiently and thus has become of high interest for use in the lunar highlands where silicates exist in high quantities. NASA’s Kennedy Space Center is exploring the use of low temperature plasmas for the reduction of silicates. This work shows that exposing silicates to a hydrogen plasma produces water. Residual gas analysis and optical emission spectroscopy were used to measure the relative quantities of water vapor production and OH (A-X) emission for hydrogen plasma interactions with lunar highland simulant and silica. X-ray photo-electron spectroscopy and scanning electron microscopy/energy-dispersive X-ray spectroscopy were also used to observe surface modifications to the silicates that reflect the key chemical reactions that occurred. From this data, plasma has emerged as a promising solution to produce oxygen on the lunar surface.

Ryan Gott↗
DOE OSTI2018

Lensing reconstruction in post-Born cosmic microwave background weak lensing

The study of the cosmic microwave background (CMB) lensing potential has established itself by now as a robust way of probing the physics of large-scale structure growth. The most common estimators of the lensing potential are derived under the assumption of Gaussianity of the matter distribution and in the Born approximation of the photon diffusion. In this paper we study the performance of quadratic estimators when applied to realistic sky maps extracted from multiple-lens ray tracing techniques in cosmological $N$-body simulations. These are expected to model accurately the effects due to the non-Gaussianity of the matter distribution induced by its nonlinearity and the deviation from the Born approximation. We show that both these effects on their own lead to reconstruction biases, but these tend to partially cancel each other when both these effects are considered together. We forecast the impact of these biases on the estimation of cosmological parameters for future high-sensitivity CMB experiments like CMB-S4. We find that the cold dark matter density, $Ω_{cdm}$, the optical depth to reionization $τ$, the amplitude of primordial inflationary perturbations, $A_s$, and the sum of neutrino masses, $M_ν$, could be biased at the $1–2σ$ level, if no external data set is used. We also observe a reduction of the bias if external data like baryon acoustic oscillations is included.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2001

Routine detection of Epstein-Barr virus specific T-cells in the peripheral blood by flow cytometry

The ability to detect cytomegalovirus-specific T-cells (CD4(+)) in the peripheral blood by flow cytometry has been recently described by Picker et al. In this method, cells are incubated with viral antigen and responding (cytokine producing) T-cells are then identified by flow cytometry. To date, this technique has not been reliably used to detect Epstein-Barr virus (EBV)-specific T-cells primarily due to the superantigen/mitogenic properties of the virus which non-specifically activate T-cells. By modifying culture conditions under which the antigens are presented, we have overcome this limitation and developed an assay to detect and quantitate EBV-specific T-cells. The detection of cytokine producing T-cells by flow cytometry requires an extremely strong signal (such as culture in the presence of PMA and ionomycin). Our data indicate that in modified culture conditions (early removal of viral antigen) the non-specific activation of T-cells by EBV is reduced, but antigen presentation will continue uninhibited. Using this method, EBV-specific T-cells may be legitimately detected using flow cytometry. No reduction in the numbers of antigen-specific T-cells was observed by the early removal of target antigen when verified using cytomegalovirus antigen (a virus with no non-specific T-cell activation properties). In EBV-seropositive individuals, the phenotype of the EBV-specific cytokine producing T-cells was evaluated using four-color flow cytometry and found to be CD45(+), CD3(+), CD4(+), CD45RA(-), CD69(+), CD25(-). This phenotype indicates the stimulation of circulating previously unactivated memory T-cells. No cytokine production was observed in CD4(+) T-cells from EBV-seronegative individuals, confirming the specificity of this assay. In addition, the use of four color cytometry (CD45, CD3, CD69, IFNgamma/IL-2) allows the total quantitative assessment of EBV-specific T-cells while monitoring the interference of EBV non-specific mitogenic activity. This method may have significant utility for the monitoring of the immune response to latent virus infection/reactivation.

Non-NASA Center↗
NASA NTRS2011

Inter-Annual and Decadal Changes in Tropospheric and Stratospheric Ozone

Ozone data beginning October 2004 from the Aura Ozone Monitoring Instrument (OMI) and Aura Microwave Limb Sounder (MLS) are used to evaluate the accuracy of the Cloud slicing technique in effort to develop long data records of tropospheric and stratospheric ozone and studying their long-term changes. Using this technique, we have produced a 32-year (1979-2010) long record of tropospheric and stratospheric ozone from the combined Total Ozone Mapping Spectrometer (Toms) and OMI. The analyses of these time series suggest that the quasi-biennial oscillation (QBO) is the dominant source of inter-annual changes of 30-40 Dobson Units (DU). Tropospheric ozone also indicates a QBO signal in the peak to peak changes varying from 2 to 7 DU. Decadal changes in global stratospheric ozone indicate a turnaround in ozone loss around mid 1990's with most of these changes occurring in the Northern Hemisphere from the subtropics to high latitudes. The trend results are generally consistent with the prediction of chemistry climate models which include the reduction of ozone destroying substances beginning in the late 1980's mandated by the Montreal Protocol.

Ziemke, Jr. R.↗
NASA NTRS2013

Numerical Investigation of a Fuselage Boundary Layer Ingestion Propulsion Concept

In the present study, a numerical assessment of the performance of fuselage boundary layer ingestion (BLI) propulsion techniques was conducted. This study is an initial investigation into coupling the aerodynamics of the fuselage with a BLI propulsion system to determine if there is sufficient potential to warrant further investigation of this concept. Numerical simulations of flow around baseline, Boundary Layer Controlled (BLC), and propelled boundary layer controlled airships were performed. Computed results showed good agreement with wind tunnel data and previous numerical studies. Numerical simulations and sensitivity analysis were then conducted on four BLI configurations. The two design variables selected for the parametric study of the new configurations were the inlet area and the inlet to exit area ratio. Current results show that BLI propulsors may offer power savings of up to 85% over the baseline configuration. These interim results include the simplifying assumption that inlet ram drag is negligible and therefore likely overstate the reduction in power. It has been found that inlet ram drag is not negligible and should be included in future analysis.

Elmiligui, Alaa A.↗
DOE OSTI2022

A comparison of neural network architectures for data-driven reduced-order modeling

The popularity of deep convolutional autoencoders (CAEs) has engendered new and effective reduced-order models (ROMs) for the simulation of large-scale dynamical systems. Despite this, it is still unknown whether deep CAEs provide superior performance over established linear techniques or other network-based methods in all modeling scenarios. To elucidate this, the effect of autoencoder architecture on its associated ROM is studied through the comparison of deep CAEs against two alternatives: a simple fully connected autoencoder, and a novel graph convolutional autoencoder. Through benchmark experiments, it is shown that the superior autoencoder architecture for a given ROM application is highly dependent on the size of the latent space and the structure of the snapshot data, with the proposed architecture demonstrating benefits on data with irregular connectivity when the latent space is sufficiently large.

42 ENGINEERING↗
NASA NTRS1993

Discrimination of poorly exposed lithologies in AVIRIS data

One of the advantages afforded by imaging spectrometers such as AVIRIS is the capability to detect target materials at a sub-pixel scale. This paper presents several examples of the identification of poorly exposed geologic materials - materials which are either subpixel in scale or which, while having some surface expression over several pixels, are partially covered by vegetation or other materials. Sabol et al. (1992) noted that a primary factor in the ability to distinguish sub-pixel targets is the spectral contrast between the target and its surroundings. In most cases, this contrast is best expressed as an absorption feature or features present in the target but absent in the surroundings. Under such circumstances, techniques such as band depth mapping (Clark et al., 1992) are feasible. However, the only difference between a target material and its surroundings is often expressed solely in the continuum. We define the 'continuum' as the reflectance or radiance spanning spectral space between spectral features. Differences in continuum slope and shape can only be determined by reduction techniques which considers the entire spectral range; i.e., techniques such as spectral mixture analysis (Adams et al., 1989) and recently developed techniques which utilize an orthogonal subspace projection operator (Harsanyi, 1993). Two of the three examples considered herein deal with cases where the target material differs from its surroundings only by such a subtle continuum change.

Farrand, William H.↗
DOE OSTI2025

Hybrid data-driven cement-stabilized soil design: An integration of machine learning, multi-objective optimization, and life cycle assessment

Soil stabilization is crucial in geotechnical engineering, yet conventional methods are often time-consuming, resource-intensive, and environmentally unsustainable. Despite growing interest in Machine Learning (ML) and optimization tools for mix design, few studies integrate these methods with decision-making techniques and environmental assessment to support practical implementation. This study proposes a hybrid data-driven framework for predicting strength, optimizing mix compositions, and evaluating environmental impacts via life cycle assessment of cement-stabilized soft soils. Six ML models were evaluated, and the top-performing eXtreme Gradient Boosting (XGB) model was further improved using the Grey Wolf Optimizer (GWO). The optimized XGB-GWO model, integrated with a polynomial cost function, served as the objective function in a multi-objective optimization problem solved via the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), with final mix selection guided by the entropy-weighted TOPSIS method. Validation through a case study produced mix designs offering superior strength-cost trade-offs, with the optimal mix achieving 2243.2 kPa unconfined compressive strength and a 16.07 % reduction in carbon emissions compared to the highest-cost design. In conclusion, this study offers a sustainable, scalable approach to soil stabilization and supports informed decision-making in construction.

Life cycle assessment↗
NASA NTRS1990

Computer vision research at Marshall Space Flight Center

Orbital docking, inspection, and sevicing are operations which have the potential for capability enhancement as well as cost reduction for space operations by the application of computer vision technology. Research at MSFC has been a natural outgrowth of orbital docking simulations for remote manually controlled vehicles such as the Teleoperator Retrieval System and the Orbital Maneuvering Vehicle (OMV). Baseline design of the OMV dictates teleoperator control from a ground station. This necessitates a high data-rate communication network and results in several seconds of time delay. Operational costs and vehicle control difficulties could be alleviated by an autonomous or semi-autonomous control system onboard the OMV which would be based on a computer vision system having capability to recognize video images in real time. A concept under development at MSFC with these attributes is based on syntactic pattern recognition. It uses tree graphs for rapid recognition of binary images of known orbiting target vehicles. This technique and others being investigated at MSFC will be evaluated in realistic conditions by the use of MSFC orbital docking simulators. Computer vision is also being applied at MSFC as part of the supporting development for Work Package One of Space Station Freedom.

Vinz, Frank L.↗
NASA NTRSDate not supplied

Effect of Electronic Shot Noise on Dynamic Measurements using Optical Techniques: Examples from Rayleigh Scattering and Unsteady PSP

Electronic shot noise is an unavoidable reality in all optical techniques that depend on measuring light intensity. For steady-state, time-averaged measurements the impact of shot noise can be easily reduced by increasing the exposure time, or by averaging over multiple-exposures. That is not the case for unsteady measurements, where time histories of light intensity variations need to be created either by high-speed photography (unsteady PSP) or via photoelectron counting (spectrally resolved Rayleigh scattering) over short time durations using a photo-multiplier tube (PMT) and photon counting electronics. Electronic shot noise introduces a fixed amount of random error, which can overwhelm the light intensity variation caused by turbulent fluctuations. Spectrum computed from such time series shows a fixed noise floor that is independent of the number of data points in the time series. For a fixed optical system, where the collected power of the luminescent light (uPSP), or the scattered light (Rayleigh) is fixed, one needs to resort to special techniques to obtain enough signal-to noise ratio (SNR). For the uPSP application it is shown that averaging of the adjacent pixels improves SNR; although this may lead to a sacrifice of spatial resolution. A second means is to increase the exposure time, which leads to a lowering of the frequency range. For the Rayleigh application, improvements of SNR can be achieved via two different cross-correlation based approaches. The first involves measuring the light intensity using two PMTs using short, contiguous gates; thereby, creating two time-series of data. The second one involves collecting one long time-series of data using one set of measurement device, followed by an odd-even splitting into two time series. When the two time-series are cross-correlated, and a power spectrum is calculated, a significant reduction in the shot noise floor can be achieved. Examples from measurements of density and velocity fluctuations spectra from two different Rayleigh setup are presented to demonstrate the process.

uPSP↗
DOE OSTI2025

Signatures of enhanced superconducting properties in niobium cavities

Superconducting radio-frequency (SRF) niobium cavities are critical for modern particle accelerators, as well as for advancing superconducting quantum systems and enabling ultrasensitive searches for new physics. In this work, we report a systematic observation of an anomalous frequency dip in Nb cavities, which occurs at temperatures just below the critical temperature (𝑇𝑐 ), indicative of enhanced superconducting properties at 𝑇 ≪ 𝑇 𝑐 . The magnitude of this dip is strongly correlated with the rf surface resistance, impurity distribution near the surface, and 𝑇𝑐 . Additionally, we report measurements of the coherence peak in the ac conductivity of two Nb SRF cavities processed using distinct methods. By comparing recent theories developed to model this experimental data, we show that the frequency-dip feature, larger coherence peak height, and reduction in the temperature-dependent surface resistance with rf current occur at minimal but finite levels of disorder.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Reduced Order Modeling conditioned on monitored features for response and error bounds estimation in engineered systems

Reduced Order Models (ROMs) form essential tools across engineering domains by virtue of their function as surrogates for computationally intensive digital twinning simulators. Although purely data-driven methods are available for ROM construction, schemes that allow to retain a portion of the physics tend to enhance the interpretability and generalization of ROMs. However, physics-based techniques can adversely scale when dealing with nonlinear systems that feature parametric dependencies. This study introduces a generative physics-based ROM that is suited for nonlinear systems with parametric dependencies and is additionally able to provide numerical error bounds associated with the respective estimates. A main contribution of this work is the conditioning of these parametric ROMs to features that can be derived from monitoring measurements, feasibly in an online fashion. This is contrary to most existing ROM schemes, which remain restricted to the prescription of the physics-based, and usually a priori unknown, system parameters. Our work utilizes conditional Variational Autoencoders to continuously map the required reduction bases to a feature vector extracted from limited output measurements, while additionally allowing for a probabilistic assessment of the ROM-estimated Quantities of Interest. An auxiliary task using a neural network-based parametrization of suitable probability distributions is introduced to re-establish the link with physical model parameters. We verify the proposed scheme on a series of simulated case studies incorporating effects of geometric and material nonlinearity under parametric dependencies related to system properties and input load characteristics.

Conditional VAEs↗
DOE OSTI2021

Discovery of Energy Storage Molecular Materials Using Quantum Chemistry-Guided Multiobjective Bayesian Optimization

Redox flow batteries (RFBs) are a promising technology for stationary energy storage applications due to their flexible design, scalability, and low cost. In RFBs, energy is carried in flowable redoxactive materials (redoxmers) which are stored externally and pumped to the cell during operation. Further improvements in the energy density of RFBs necessitates redoxmer designs with wider redox potential windows and higher solubility. Additionally, designing redoxmers with a fluorescence-enabled self-reporting functionality allows monitoring of the state of health of RFBs. To accelerate the discovery of redoxmers with desired properties, state-of-the- art machine learning ( ML) methods, such as multiobjective Bayesian optimization (MBO), are useful. Here, we first employed density functional theory calculations to generate a database of reduction potentials, solvation free energies, and absorption wavelengths for 1400 redoxmer molecules based on a 2,1,3-benzothiadiazole (BzNSN) core structure. From the computed properties, we identified 22 Pareto-optimal molecules that represent best trade-off among all of the desired properties. We further utilized these data to develop and benchmark an MBO approach to identify candidates quickly and efficiently with multiple targeted properties. With MBO, optimal candidates from the 1400-molecule data set can be identified at least 15 times more efficiently compared to the brute force or random selection approach. Importantly, we utilized this approach for discovering promising redoxmers from an unseen database of 1 million BzNSN-based molecules, where we discovered 16 new Pareto-optimal molecules with significant improvements in properties over the initial 1400 molecules. Furthermore, we anticipate that this active learning technique is general and can be utilized for the discovery of any class of functional materials that satisfies multiple desired property criteria.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2013

NASA Tech Briefs, October 2013

Topics include: A Short-Range Distance Sensor with Exceptional Linearity; Miniature Trace Gas Detector Based on Microfabricated Optical Resonators; Commercial Non-Dispersive Infrared Spectroscopy Sensors for Sub-Ambient Carbon Dioxide Detection; Fast, Large-Area, Wide-Bandgap UV Photodetector for Cherenkov Light Detection; Mission Data System Java Edition Version 7; Adaptive Distributed Environment for Procedure Training (ADEPT); LEGEND, a LEO-to-GEO Environment Debris Model; Electronics/Computers; Millimeter-Wave Localizers for Aircraft-to-Aircraft Approach Navigation; Impedance Discontinuity Reduction Between High-Speed Differential Connectors and PCB Interfaces; SpaceCube Version 1.5; High-Pressure Lightweight Thrusters; Non-Magnetic, Tough, Corrosion- and Wear-Resistant Knives From Bulk Metallic Glasses and Composites; Ambient Dried Aerogels; Applications for Gradient Metal Alloys Fabricated Using Additive Manufacturing; Passivation of Flexible YBCO Superconducting Current Lead With Amorphous SiO2 Layer; Propellant-Flow-Actuated Rocket Engine Igniter; Lightweight Liquid Helium Dewar for High-Altitude Balloon Payloads; Method to Increase Performance of Foil Bearings Through Passive Thermal Management; Unibody Composite Pressurized Structure; JWST Integrated Science Instrument Module Alignment Optimization Tool; Radar Range Sidelobe Reduction Using Adaptive Pulse Compression Technique; Digitally Calibrated TR Modules Enabling Real-Time Beamforming SweepSAR Architectures; Electro-Optic Time-to-Space Converter for Optical Detector Jitter Mitigation; Partially Transparent Petaled Mask/Occulter for Visible-Range Spectrum; Educational NASA Computational and Scientific Studies (enCOMPASS); Coarse-Grain Bandwidth Estimation Scheme for Large-Scale Network; Detection of Moving Targets Using Soliton Resonance Effect; High-Efficiency Nested Hall Thrusters for Robotic Solar System Exploration; High-Voltage Clock Driver for Photon-Counting CCD Characterization; Development of the Code RITRACKS; and Enabling Microliquid Chromatography by Microbead Packing of Microchannels.

Source record↗