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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Annual Greenland Accumulation Rates (2009-2012) from Airborne Snow Radar

Contemporary climate warming over the Arctic is accelerating mass loss from the Greenland Ice Sheet through increasing surface melt, emphasizing the need to closely monitor its surface mass balance in order to improve sea-level rise predictions. Snow accumulation is the largest component of the ice sheet's surface mass balance, but in situ observations thereof are inherently sparse and models are difficult to evaluate at large scales. Here, we quantify recent Greenland accumulation rates using ultra-wideband (2-6.5 gigahertz) airborne snow radar data collected as part of NASA's Operation IceBridge between 2009 and 2012. We use a semi-automated method to trace the observed radiostratigraphy and then derive annual net accumulation rates for 2009-2012. The uncertainty in these radar-derived accumulation rates is on average 14 percent. A comparison of the radarderived accumulation rates and contemporaneous ice cores shows that snow radar captures both the annual and longterm mean accumulation rate accurately. A comparison with outputs from a regional climate model (MAR - Modele Atmospherique Regional for Greenland and vicinity) shows that this model matches radar-derived accumulation rates in the ice sheet interior but produces higher values over southeastern Greenland. Our results demonstrate that snow radar can efficiently and accurately map patterns of snow accumulation across an ice sheet and that it is valuable for evaluating the accuracy of surface mass balance models.

surface melt↗
NASA NTRS2016

Assessment of the SMAP Passive Soil Moisture Product

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using an L-band (active) radar and an L-band (passive) radiometer. After an irrecoverable hardware failure of the radar on July 7, 2015, the radiometer-only soil moisture product became the only operational Level 2 soil moisture product for SMAP. The product provides soil moisture estimates posted on a 36 kilometer Earth-fixed grid produced using brightness temperature observations from descending passes. Within months after the commissioning of the SMAP radiometer, the product was assessed to have attained preliminary (beta) science quality, and data were released to the public for evaluation in September 2015. The product is available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. This paper provides a summary of the Level 2 Passive Soil Moisture Product (L2_SM_P) and its validation against in situ ground measurements collected from different data sources. Initial in situ comparisons conducted between March 31, 2015 and October 26, 2015, at a limited number of core validation sites (CVSs) and several hundred sparse network points, indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics. The accuracy of the soil moisture retrievals averaged over the CVSs was 0.038 cubic meter per cubic meter unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 cubic meter per cubic meter.

passive↗
NASA NTRS2017

Global Assimilation of X Project Loon Stratospheric Balloon Observations

Project Loon has an overall goal of providing worldwide internet coverage using a network of long-duration super-pressure balloons. Beginning in 2013, Loon has launched over 1600 balloons from multiple tropical and middle latitude locations. These GPS tracked balloon trajectories provide lower stratospheric wind information over the oceans and remote land areas where traditional radiosonde soundings are sparse, thus providing unique coverage of lower stratospheric winds. To fully investigate these Loon winds we: 1) compare the Loon winds to winds produced by a global data assimilation system (DAS: NASA GEOS) and 2) assimilate the Loon winds into the same comprehensive DAS. Results show that in middle latitudes the Loon winds and DAS winds agree well and assimilating the Loon winds have only a small impact on short-term forecasting of the Loon winds, however, in the tropics the loon winds and DAS winds often disagree substantially (8 m/s or more in magnitude) and in these cases assimilating the loon winds significantly improves the forecast of the loon winds. By highlighting cases where the Loon and DAS winds differ, these results can lead to improved understanding of stratospheric winds, especially in the tropics.

Loo↗
NASA NTRS2019

Global Assimilation of Loon Stratospheric Balloon Observations and Their Trajectories Relative to Tropical Waves

Project Loon has an overall goal of providing worldwide internet coverage using a network of long-duration super-pressure balloons. Beginning in 2013, Loon has launched over 1600 balloons from multiple tropical and middle latitude locations. These GPS tracked balloon trajectories provide lower stratospheric wind information over the oceans and remote land areas where traditional radiosonde soundings are sparse, thus providing unique coverage of lower stratospheric winds. To fully investigate these Loon winds we: 1) compare the Loon winds to winds produced by a global data assimilation system (DAS: NASA GEOS) and 2) assimilate the Loon winds into the same comprehensive DAS. During May through December 2016 Loon balloons were often able to remain near the equator by selectively adjusting the Loon altitude. Our results based on global wind analyses show that the expected mean poleward motion from the Brewer-Dobson circulation can be circumvented by vertically adjusting the Loon altitudes with the phasing with the meridional wind of equatorial Rossby waves, allowing the Loon balloons to remain in the tropics.

Coy, L.↗
NASA NTRSDate not supplied

Surface Expression of Bed Topography in Greenland and Antarctica

It has long been recognized that major subglacial topographic features beneath the Greenland and Antarctic ice sheets – whether especially prominent or wide – can generate an observable surface expression. Recent advances in digital elevation models (ArcticDEM, REMA) and bed-to-surface transfer theory now permit widespread observation of this phenomena and a more robust interpretation. Hillshading a digital elevation model from the direction of ice flow permits straightforward detection of major surface features. For Greenland, comparison against the growing catalog of airborne radar-sounding data confirms that the large majority of these features are associated with subglacial topography – typically valleys. These observations suggest a better path toward interpolating subglacial topography between sparse radar observations by developing methods that also require fidelity to observed surface relief.

Joe Macgregor↗
NASA NTRS2022

Gaussian Process Regression Method for Costing SmallSat Bus Capabilities

NASA is responding to the growing interest in, andcapabilities of, small satellites for science applications with an increasingnumber and frequency of Announcements of Opportunityfor small satellite space missions. Estimating the probabilitythat these mission concepts will fit within the small cost capsof these opportunities is largely driven by the probability thatone of the burgeoning number of small satellite providers will beable to meet the payload’s accommodation requirements withinthe budget for the spacecraft. JPL has collected a databasecontaining technical specifications and cost of commerciallyavailable Smallsat buses across various vendors. The primarypurpose of the database is for use in JPL’s Team X architecturestudies to inform cost estimates of a spacecraft bus which fitsthe customer’s technical requirements for their payload andmission. Customer needs are often unique and don’t alignperfectly with an off-the-shelf commercial spacecraft bus, whichmotivates the need to develop a cost model across the continuoustechnical parameter space.Al’s Bus Cost Distribution Estimator (ABCDE) uses Gaussianprocess regression (GPR) to predict commercial Smallsat spacecraftbus cost based on a subset of a customer’s technicalrequirements (payload mass, payload power, delta V, pointingcontrol, and downlink rate). GPR is implemented in ABCDE asa Bayesian method which fits an implied multivariate regressionon the technical parameters and uses kriging to intentionally“overfit” the residuals. Overfitting the residuals allows costestimates to collapse in uncertainty closer to the data pointswhile maintaining larger uncertainty intervals in regions of parameterspace with fewer data records. The data used to fit thismodel is sensitive and represents cost estimates for off-the-shelfcommercial buses. GPR simultaneously protects the sensitivityof the database and uses the sparse nature of the database toaccount for uncertainty in cost in a useful way. For a givenset of customer technical requirements, the tool provides a costestimate distribution, the percentiles of which can be interpretedas a confidence level of finding a commercial bus under a specifiedcost cap. ABCDE dramatically pushes the boundaries ofspacecraft cost estimation models due to its Bayesian methodology(accounting for the maximum uncertainty in the underlyingregression), the mathematically advanced kriging methodology,and the novelty of its application in Team X architecture tradestudies.

Austin, Alex↗
NASA NTRSDate not supplied

Performance Optimization Methods for a Memory-Bound, Unstructured-Grid CFD Application on Massively Parallel GPU Platforms

Computational performance of the FUN3D unstructured-grid computational fluid dynamics (CFD) application on massively parallel GPU environments is memory-bound and highly dependent upon efficient reads from and atomic updates to the irregular cell-, edge-, and node-based data structures. In this talk, we present recent efforts into optimizing select performance-critical kernels on NVIDIA Tesla V100 and A100 GPUs and AMD CDNA MI100 GPUs. A novel use of L2 cache residency controls and asynchronous loads into on-chip shared memory are explored on the A100 GPU for the sparse iterative solver, which is dominated by mixed-precision, sparse matrix vector multiplication. Demonstrations show that these methods improve global memory bandwidth utilization by 13.5% on the A100 GPU. Several techniques are also presented that use registers and/or shared memory to facilitate array transposition and aggregation which combine to reduce the frequency and increase the cache efficiency of floating-point atomic updates to the irregular data structures. These methods are demonstrated to improve the kernel throughput by nearly 500% on select kernels on the AMD MI100 over atomic updates directly to global memory. Overall, both V100 and A100 GPUs outperformed the MI100 GPU on kernels dominated by double-precision atomic updates; however, the techniques demonstrated here reduced the performance gap and improved the MI100 performance.

GPU CPU unstructured CFD memory↗
DOE OSTI2025

Searches for New Physics With Muon Conversion at Fermilab and Triboson Production at the LHC

We report on several efforts to search for physics beyond the standard model of particle physics at broad energy scales. The Mu2e experiment at Fermilab will search for charged lepton flavor violation via the muon to electron conversion process, which is suppressed in the Standard Model. Mu2e will be operated at a low energy, yet can probe New Physics at very high mass scales (O(1e3 - 1e4 ) TeV). At high energies, the CMS experiment at the CERN LHC continues to deliver an impressive suite of Standard Model measurements and limits on a variety of New Physics signatures. Mu2e is under construction and slated to collect its first physics data in the coming years. This thesis describes work done during the construction phase of Mu2e and focuses on two critical areas: magnetic field modeling and statistical analysis. We describe a novel method for field modeling which we validate using a simulated dataset representing the expected magnetic field in the Detector Solenoid. This method blends a standard least-squares fitting technique that utilizes physically motivated analytical model functions with a novel physics informed network that is constructed to obey Maxwell’s equations. We show the technique can model the field with an accuracy of 10−7 despite the presence of injected noise in the pseudo-measurements at the 10−5 level. We then present preliminary results of the calibration of 3D Hall probes at the sub-10−4 level. These probes will be used to directly measure the Mu2e Detector Solenoid magnetic field on a sparse grid; these measurements serve as the input to the field model fitting. Finally, we describe the first implementation of both an unbinned shape analysis and a Bayesian interpretation applied to Mu2e pseudo-data. Up to 20% tighter limits can be set by the shape analysis compared to a standard cut & count analysis. The AlCap experiment collected data at PSI in 2015 to measure several important quantities related to nuclear muon capture on an aluminum target, which is a significant background process for Mu2e. The neutron emission from muon capture can introduce background hits in the Mu2e detectors and can increase radiation damage in various elements of the apparatus. We present measurements of the neutron group fluence and mean neutron multiplicity for muon capture on aluminum nuclei. Finally, we discuss an analysis of triboson production at CMS using an Effective Field Theory framework. Standard Model triboson production, which was first observed at CMS in 2020, has a relatively small cross section and provides direct access to both anomalous triple gauge couplings and quartic gauge couplings. These couplings, interpreted in the Standard Model Effective Field Theory, are studied in the present work. We target the boosted regime where the background rate is low and yields are enhanced when dimension-6 and dimension-8 Wilson coefficients are non-zero. We do not observe an excess in the data and therefore set bounds on the Wilson coefficients. For dimension-6 coefficients the tightest observed (expected) bounds are set on cW /Λ2 where Λ is the mass scale of new physics; the bounds are [−0.13, 0.12] TeV−2 ([−0.12, 0.12] TeV−2 ) at 95% CL. The tightest bounds in dimension-8 are set on fT,0 / Λ4 ; the observed (expected) bounds at 95% CL are [−0.63, 0.69] TeV−4 ([−0.54, 0.62] TeV−4 ). Additional results are presented which include scenarios where multiple Wilson coefficients are non-zero, the application of signal model clipping to address unitarity violation in Effective Field Theories, and a novel template fit developed for easier reinterpretation of our results.

Kampa, Cole Erik [Northwestern U. (main)] (ORCID:0↗
DOE OSTI2024

Improved Subseasonal Forecasting of Extreme Polar Vortices Using Machine Learning

Our research was focused on forecasting the position and shape of the winter stratospheric polar vortex at a subseasonal timescale of 15 days in advance. To achieve this, we employed both statistical and neural network machine learning techniques. The analysis was performed on 42 winter seasons of reanalysis data provided by NASA giving us a total of 6,342 days of data. The state of the polar vortex for determined by using geometric moments to calculate the centroid latitude and the aspect ratio of an ellipse fit onto the vortex. Timeseries for thirty additional precursors were calculated to help improve the predictive capabilities of the algorithm. Feature importance of these precursors was performed using random forest to measure the predictive importance and the ideal number of precursors. Then, using the precursors identified as important, various statistical methods were tested for predictive accuracy with random forest and nearest neighbor performing the best. An echo state network, a type of recurrent neural network that features sparsely connected hidden layer and a reduced number of trainable parameters that allows for rapid training and testing, was also implemented for the forecasting problem. Hyperparameter tuning was performed for each methods using a subset of the training data. The algorithms were trained and tuned on the first 41 years of data, then tested for accuracy on the final year. In general, the centroid latitude of the polar vortex proved easier to predict than the aspect ratio across all algorithms. Random forest outperformed other statistical forecasting algorithms overall but struggled to predict extreme values. Forecasting from echo state network suggested a strong predictive capability past 15 days, but further work is required to fully realize the potential of recurrent neural network approaches.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Generating multi-scale Li-ion battery cathode particles with radial grain architectures using stereological generative adversarial networks

Abstract Understanding structure-property relationships of Li-ion battery cathodes is crucial for optimizing rate-performance and cycle-life resilience. However, correlating the morphology of cathode particles, such as in LiNi0.8Mn0.1Co0.1O2 (NMC811), and their inner grain architecture with electrode performance is challenging, particularly, due to the significant length-scale difference between grain and particle sizes. Experimentally, it is not feasible to image such a high number of particles with full granular detail. A second challenge is that sufficiently high-resolution 3D imaging techniques remain expensive and are sparsely available at research institutions. Here, we present a stereological generative adversarial network-based model fitting approach to tackle this, that generates representative 3D information from 2D data, enabling characterization of materials in 3D using cost-effective 2D data. Once calibrated, this multi-scale model can rapidly generate virtual cathode particles that are statistically similar to experimental data, and thus is suitable for virtual characterization and materials testing through numerical simulations. A large dataset of simulated particles with inner grain architecture has been made publicly available.

25 ENERGY STORAGE↗
NASA NTRS1983

Progress on a generalized coordinates tensor product finite element 3DPNS algorithm for subsonic

A generalized coordinates form of the penalty finite element algorithm for the 3-dimensional parabolic Navier-Stokes equations for turbulent subsonic flows was derived. This algorithm formulation requires only three distinct hypermatrices and is applicable using any boundary fitted coordinate transformation procedure. The tensor matrix product approximation to the Jacobian of the Newton linear algebra matrix statement was also derived. Tne Newton algorithm was restructured to replace large sparse matrix solution procedures with grid sweeping using alpha-block tridiagonal matrices, where alpha equals the number of dependent variables. Numerical experiments were conducted and the resultant data gives guidance on potentially preferred tensor product constructions for the penalty finite element 3DPNS algorithm.

Baker, A. J.↗
NASA NTRS2021

Thermal Infrared Detector Sparse Array for NASA Planetary Applications

In this work, we present an uncooled infrared (IR) thermal imager featuring a sparse array of resonant pixels that can be monolithically integrated with the readout electronics for long-term operation in hightemperature environments. Each of the detector pixels is made of an IR-sensitive GaN microresonator developed to be operated at up to 500 0C. The pixels are employed in sparse array configuration to allow for routing the signal carrying traces and to ease the routing from pixel to circuit, as well as to reduce the power consumption. A 128´128 sparse resonator array with the array density of 37.5% is designed and used in conjunction with the Compressed Sensing method to reconstruct complex test images from highly incomplete data. Capabilities and performance metrics of the recovery technique are explored.

Rais-Zadeh, Mina↗
DOE OSTI2024

Score-based diffusion models for generating liquid argon time projection chamber images

For the first time, we show high-fidelity generation of Liquid Argon Time Projection Chamber (LArTPC-like) data using a generative neural network. This demonstrates that methods developed for natural images do transfer to LArTPC-produced images, which, in contrast to natural images, are globally sparse but locally dense. We present the score-based diffusion method employed. We evaluate the fidelity of the generated images using several quality metrics, including modified measures used to evaluate natural images, comparisons between high-dimensional distributions, and comparisons relevant to LArTPC experiments. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2025

Real-time Anomaly Detection for Liquid Argon Time Projection Chambers

We present a real-time anomaly detection framework for liquid argon time projection chambers (LArTPCs), targeting applications in particle physics experiments such as the Short Baseline Near Detector (SBND) or the future Deep Underground Neutrino Experiment (DUNE). These experiments employ detectors that generate and stream high-resolution but sparse images of neutrino and other particle interactions. Our approach utilizes anomaly detection with autoencoders, compressed through knowledge distillation (KD), to enable the detection of anomalous signals in the data through efficient inference on resource-constrained hardware. The framework is targeted for deployment on computing platforms equipped with field-programmable gate arrays (FPGAs), GPUs, or CPUs, allowing low-latency selection of relevant activity directly from the raw detector data stream. We demonstrate that our approach is suitable for the detection and localization of anomalously "high-multiplicity" activity, and outline promising applications for LArTPC online data filtering and triggering.

FOS: Physical sciences↗
NASA NTRS1990

Recognition of simple visual images using a sparse distributed memory: Some implementations and experiments

Previously, a method was described of representing a class of simple visual images so that they could be used with a Sparse Distributed Memory (SDM). Herein, two possible implementations are described of a SDM, for which these images, suitably encoded, will serve both as addresses to the memory and as data to be stored in the memory. A key feature of both implementations is that a pattern that is represented as an unordered set with a variable number of members can be used as an address to the memory. In the 1st model, an image is encoded as a 9072 bit string to be used as a read or write address; the bit string may also be used as data to be stored in the memory. Another representation, in which an image is encoded as a 256 bit string, may be used with either model as data to be stored in the memory, but not as an address. In the 2nd model, an image is not represented as a vector of fixed length to be used as an address. Instead, a rule is given for determining which memory locations are to be activated in response to an encoded image. This activation rule treats the pieces of an image as an unordered set. With this model, the memory can be simulated, based on a method of computing the approximate result of a read operation.

Jaeckel, Louis A.↗
NASA NTRS1992

Large-scale structure in the Southern Sky Redshift Survey

The power spectrum from the Southern Sky Redshift Survey and the CfA samples are measured in order to explore the amplitude of fluctuation in the galaxy density. At lambda of less than or equal to 30/h Mpc the observed power spectrum is quite consistent with the standard CDM model. At larger scales the data indicate an excess of power over the standard CDM model. The observed power spectrum from these optical galaxy samples is in good agreement with that drawn from the sparsely sampled IRAS galaxies. The shape of the power spectrum is also studied by examining the relation between the genus per unit volume and the smoothing length. It is found that, over Gaussian smoothing scales from 6 to 14/h Mpc, the power spectrum has a slope of about -1. The topology of the galaxy density field is studied by measuring the shift of the genus curve from the Gaussian case. Over all smoothing scales studied, the observed genus curves are consistent with a random phase distribution of the galaxy density field, as predicted by the inflationary scenarios.

Park, Changbom↗
NASA NTRS1994

Multiresolution representation and numerical algorithms: A brief review

In this paper we review recent developments in techniques to represent data in terms of its local scale components. These techniques enable us to obtain data compression by eliminating scale-coefficients which are sufficiently small. This capability for data compression can be used to reduce the cost of many numerical solution algorithms by either applying it to the numerical solution operator in order to get an approximate sparse representation, or by applying it to the numerical solution itself in order to reduce the number of quantities that need to be computed.

Harten, Amiram↗
NASA NTRS2009

Synthesis and Assimilation Systems - Essential Adjuncts to the Global Ocean Observing System

Ocean assimilation systems synthesize diverse in situ and satellite data streams into four-dimensional state estimates by combining the various observations with the model. Assimilation is particularly important for the ocean where subsurface observations, even today, are sparse and intermittent compared with the scales needed to represent ocean variability and where satellites only sense the surface. Developments in assimilation and in the observing system have advanced our understanding and prediction of ocean variations at mesoscale and climate scales. Use of these systems for assessing the observing system helps identify the strengths of each observation type. Results indicate that the ocean remains under-sampled and that further improvements in the observing system are needed. Prospects for future advances lie in improved models and better estimates of error statistics for both models and observations. Future developments will be increasingly towards consistent analyses across components of the Earth system. However, even today ocean synthesis and assimilation systems are providing products that are useful for many applications and should be considered an integral part of the global ocean observing and information system.

Rienecker, Michele M.↗