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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Nimbus-7 global cloud climatology. I - Algorithms and validation

An improved version of the Nimbus-7 cloud retrieval algorithm was validated using data from Nimbus-7 Temperature Humidity Infrared Radiometer and Total Ozone Mapping Spectrometer to determine cloudiness parameters for the globe. Quantitative validation of total cloud amount was performed by comparing the algorithm results with estimates derived from GOES images and auxiliary meteorological data. The systematic errors of the Nimbus-7 total cloud-amount algorithm, relative to the GOES-derived estimates, were found to be less than 10 percent. The random errors of daily estimates ranged between 7 and 16 percent, day or night.

Stowe, L. L.↗
DOE OSTI2019

Asymmetrical Magnet Shape Optimization Based on S-C Mapping for Torque Profile Mitigation in Unidirectional Application of SPMS Machine

Surface-mounted Permanent Magnet Synchronous (SPMS) machines are widely used in applications that require low torque ripple, reduced vibration and acoustic noise. For those applications of SPMSM in which only unidirectional operation is needed, asymmetric design optimization show to have significant advantages over symmetric designs. In this paper, an analytical method based on Schwarz -Christoffel mapping is presented for asymmetrical magnet shape optimization in SPMS machine. In this method, permanent magnet is divided into finite cells so that each cell can be assigned with either air (OFF) or magnet (ON). The Optimization problem is solved using Genetic Algorithms (GAs). In order to remove non-manufacturable shapes, the cluster of materials and cleaning method is used and applied into magnet cells. The analytical method can significantly reduce the computation time used by optimization process as compared with methods based on Finite Element Analysis (FEA). This method is applied to a 12-pole SPMS machine. Validation of the S-C mapping method and final results are carried out using 2D and 3D finite element analysis. Here, the results obtained demonstrate that asymmetrical magnet shape improves cogging torque and torque ripple performance of SPMS machine by a significant margin.

42 ENGINEERING↗
NASA NTRS2004

Software for Generating Strip Maps from SAR Data

Jurassicprok is a computer program that generates strip-map digital elevation models and other data products from raw data acquired by an airborne synthetic-aperture radar (SAR) system. This software can process data from a variety of airborne SAR systems but is designed especially for the GeoSAR system, which is a dual-frequency (P- and X-band), single-pass interferometric SAR system for measuring elevation both at the bare ground surface and top of the vegetation canopy. Jurassicprok is a modified version of software developed previously for airborne-interferometric- SAR applications. The modifications were made to accommodate P-band interferometric processing, remove approximations that are not generally valid, and reduce processor-induced mapping errors to the centimeter level. Major additions and other improvements over the prior software include the following: a) A new, highly efficient multi-stage-modified wave-domain processing algorithm for accurately motion compensating ultra-wideband data; b) Adaptive regridding algorithms based on estimated noise and actual measured topography to reduce noise while maintaining spatial resolution; c) Exact expressions for height determination from interferogram data; d) Fully calibrated volumetric correlation data based on rigorous removal of geometric and signal-to-noise decorrelation terms; e) Strip range-Doppler image output in user-specified Doppler coordinates; f) An improved phase-unwrapping and absolute-phase-determination algorithm; g) A more flexible user interface with many additional processing options; h) Increased interferogram filtering options; and i) Ability to use disk space instead of random- access memory for some processing steps.

Hensley, Scott↗
NASA NTRS2010

A Comparison of Satellite-Derived Snow Maps with a Focus on Ephemeral Snow in North Carolina

In this paper, we focus on the attributes and limitations of four commonly-used daily snowcover products with respect to their ability to map ephemeral snow in central and eastern North Carolina. We show that the Moderate-Resolution Imaging Spectroradiometer (MODIS) fractional snow-cover maps can delineate the snow-covered area very well through the use of a fully-automated algorithm, but suffer from the limitation that cloud cover precludes mapping some ephemeral snow. The semi-automated Interactive Multi-sensor Snow and ice mapping system (IMS) and Rutgers Global Snow Lab (GSL) snow maps are often able to capture ephemeral snow cover because ground-station data are employed to develop the snow maps, The Rutgers GSL maps are based on the IMS maps. Finally, the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) provides some good detail of snow-water equivalent especially in deeper snow, but may miss ephemeral snow cover because it is often very thin or wet; the AMSR-E maps also suffer from coarse spatial resolution. We conclude that the southeastern United States represents a good test region for validating the ability of satellite snow-cover maps to capture ephemeral snow cover,

Hall, Dorothy K.↗
DOE OSTI2025

A Scalable Gaussian Process Approach to Shear Mapping with MuyGPs

Analysis of cosmic shear is an integral part of understanding structure growth across cosmic time, which in turn provides us with information about the nature of dark energy. Conventional methods generate shear maps from which we can infer the matter distribution in the universe. Current methods (e.g., Kaiser–Squires inversion) for generating these maps, however, are tricky to implement and can introduce bias. Recent alternatives construct a spatial process prior for the lensing potential, which allows for inference of the convergence and shear parameters given lensing shear measurements. Realizing these spatial processes, however, scales cubically in the number of observations—an unacceptable expense as near-term surveys expect billions of correlated measurements. Therefore, we present a linearly scaling shear map construction alternative using a scalable Gaussian process prior called MuyGPs. MuyGPs avoids cubic scaling by conditioning interpolation on only nearest neighbors and fits hyperparameters using batched leave-one-out cross-validation. This work is the first step toward a full, scalable mass mapping method. We work in a simplified regime where we validate our method by interpolating and analyzing maps given noisy point-estimate data from all three shear fields, taken from a suite of N -body ray-tracing simulations. We also show that we can perform these operations at the scale of billions of galaxies on high-performance computing platforms.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Emergent chirality in a polar meron to skyrmion phase transition

Polar skyrmions are predicted to emerge from the interplay of elastic, electrostatic and gradient energies, in contrast to the key role of the anti-symmetric Dzyalozhinskii-Moriya interaction in magnetic skyrmions. Here, we explore the reversible transition from a skyrmion state (topological charge of –1) to a two-dimensional, tetratic lattice of merons (with topological charge of –1/2) upon varying the temperature and elastic boundary conditions in [(PbTiO 3 ) 16 /(SrTiO 3 ) 16 ] 8 membranes. This topological phase transition is accompanied by a change in chirality, from zero-net chirality (in meronic phase) to net-handedness (in skyrmionic phase). We show how scanning electron diffraction provides a robust measure of the local polarization simultaneously with the strain state at sub-nm resolution, while also directly mapping the chirality of each skyrmion. Using this, we demonstrate strain as a crucial order parameter to drive isotropic-to-anisotropic structural transitions of chiral polar skyrmions to non-chiral merons, validated with X-ray reciprocal space mapping and phase-field simulations.

42 ENGINEERING↗
DOE OSTI2018

Deformation Mechanisms and Formability Window for As-Cast Mg-6Al-2Ca-1Sn-0.3Sr Alloy (MRI 230D)

The hot deformation characteristics of MRI 230D alloy have been evaluated in the temperature range 260-500 °C and strain rate range 0.0003-10 s{sup −1}, on the basis of processing map. The processing map exhibited two domains in the ranges: (1) 300-370 °C and 0.0003-0.001 s{sup −1} and (2) 370-480 °C and 0.0003-0.1 s{sup −1}. Dynamic recrystallization occurs in the both domains with basal slip dominating in the first domain along with climb as recovery process and second-order pyramidal slip dominating in the second with the recovery by cross-slip. In Domains (1) and (2), the apparent activation energy values estimated using the kinetic rate equation are 143 and 206 kJ/mole, respectively, the first one being close to that for lattice self-diffusion confirming climb. It is recommended that the alloy is best processed at 450 °C and strain rates less than 0.1 s{sup −1}, where non-basal slip and cross-slip occur extensively to impart excellent workability. The alloy exhibits flow instability in the form of adiabatic shear band formation and flow localization at lower temperatures and higher strain rates. Forging of a cup-shaped component was performed under various conditions, and the results validated the predictions of the processing map on the workability domains as well as the instability regimes.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Three-dimensional electronic resistivity mapping of solid electrolyte interphase on Si anode materials

Silicon is a promising candidate for the lithium ion battery (LIB) anode because of the order-of-magnitude improvement in capacity over current state-of-the-art graphite anodes. In systems featuring both C and Si anodes, electronic resistivity of the solid-electrolyte interphase (SEI) layer is a critical factor for preventing continuous electrolyte-decomposition reactions at the electrode/electrolyte interface. However, the in-situ measurement of SEI electronic resistance has been complicated by ion transport and electronic contributions from other parts of the battery circuit. Ex-situ measurements of SEI resistivity at microscopic scales are also lacking. We report on a nanometer-resolution three-dimensional technique that enables ex-situ mapping of electronic resistivity of SEI formed on a model single-crystalline wafer Si anode. Our novel experimental approach uses scanning spreading-resistance microscopy resistance imaging and mechanical depth profiling. In addition to resistance mapping, this method also provides an alternative technique for locating buried interfaces, where mechanical or electronic properties differ sufficiently between layers. Further validation of this method was obtained by resistance mapping of a reference sample with a designed a-Si:H layer stack of different doping concentrations. The results show relatively uniform lateral resistivity distribution of the SEIs but steep decreases in resistivity in the vertical direction. Resistivity vs. depth profiles are highly dependent on cycling conditions, but they generally show a resistivity decrease from the most superficial levels of SEI and a thickness increase with continued cycling prior to SEI stabilization. The most prominent resistivity increase was observed on SEI formed in Gen2 electrolyte (EC:EMC [3:7 by wt.] + 1.2 M LiPF6) with 10 wt% fluoroethylene carbonate additive; this result may partially explain the significant improvements of sustained electrochemical cycling and coulombic efficiency observed with this electrolyte additive. Our approach provides a novel and unparalleled three-dimensional approach in characterizing electronic resistivity, which contributes significantly to understanding SEI formation and the intrinsic properties critical to battery performance.

25 ENERGY STORAGE↗
DOE OSTI2025

Efficient quantum simulation of QCD jets on the light front

Quark and gluon jets provide one of the best ways to probe the matter produced in ultrarelativistic high-energy collisions, from cold nuclear matter to hot quark-gluon plasma. In this work, we propose a unified framework for efficient quantum simulation of many-body dynamics using the ( 3 + 1 )-dimensional QCD Hamiltonian on the light front, particularly suited for studying the scattering of quark and gluon jets on nuclear matter in heavy-ion collisions. We describe scalable methods for mapping physical degrees of freedom onto qubits and for simulating in-medium jet evolution. We then validate our framework by implementing an algorithm that directly maps second-quantized Fock states onto qubits and uses Trotterized simulation for simulating time dynamics. Using a classical emulator, we investigate the evolution of quark and gluon jets with up to three particles in Fock states, extending prior studies. These calculations enable the study of key observables, including jet momentum broadening, particle production, and parton distribution functions. Published by the American Physical Society 2025

Qian, Wenyang (ORCID:0000000155250996)↗
DOE OSTI2015

SU-E-J-92: Validating Dose Uncertainty Estimates Produced by AUTODIRECT, An Automated Program to Evaluate Deformable Image Registration Accuracy

Purpose: Deformable image registration (DIR) is a powerful tool with the potential to deformably map dose from one computed-tomography (CT) image to another. Errors in the DIR, however, will produce errors in the transferred dose distribution. We have proposed a software tool, called AUTODIRECT (automated DIR evaluation of confidence tool), which predicts voxel-specific dose mapping errors on a patient-by-patient basis. This work validates the effectiveness of AUTODIRECT to predict dose mapping errors with virtual and physical phantom datasets. Methods: AUTODIRECT requires 4 inputs: moving and fixed CT images and two noise scans of a water phantom (for noise characterization). Then, AUTODIRECT uses algorithms to generate test deformations and applies them to the moving and fixed images (along with processing) to digitally create sets of test images, with known ground-truth deformations that are similar to the actual one. The clinical DIR algorithm is then applied to these test image sets (currently 4) . From these tests, AUTODIRECT generates spatial and dose uncertainty estimates for each image voxel based on a Student’s t distribution. This work compares these uncertainty estimates to the actual errors made by the Velocity Deformable Multi Pass algorithm on 11 virtual and 1 physical phantom datasets. Results: For 11 of the 12 tests, the predicted dose error distributions from AUTODIRECT are well matched to the actual error distributions within 1–6% for 10 virtual phantoms, and 9% for the physical phantom. For one of the cases though, the predictions underestimated the errors in the tail of the distribution. Conclusion: Overall, the AUTODIRECT algorithm performed well on the 12 phantom cases for Velocity and was shown to generate accurate estimates of dose warping uncertainty. AUTODIRECT is able to automatically generate patient-, organ- , and voxel-specific DIR uncertainty estimates. This ability would be useful for patient-specific DIR quality assurance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-J-246: A Deformation-Field Map Based Liver 4D CBCT Reconstruction Method Using Gold Nanoparticles as Constraints

Purpose: To investigate the feasibility of using nanoparticle markers to validate liver tumor motion together with a deformation field map-based four dimensional (4D) cone-beam computed tomography (CBCT) reconstruction method. Methods: A technique for lung 4D-CBCT reconstruction has been previously developed using a deformation field map (DFM)-based strategy. In this method, each phase of the 4D-CBCT is considered as a deformation of a prior CT volume. The DFM is solved by a motion modeling and free-form deformation (MM-FD) technique, using a data fidelity constraint and the deformation energy minimization. For liver imaging, there is low contrast of a liver tumor in on-board projections. A validation of liver tumor motion using implanted gold nanoparticles, along with the MM-FD deformation technique is implemented to reconstruct onboard 4D CBCT liver radiotherapy images. These nanoparticles were placed around the liver tumor to reflect the tumor positions in both CT simulation and on-board image acquisition. When reconstructing each phase of the 4D-CBCT, the migrations of the gold nanoparticles act as a constraint to regularize the deformation field, along with the data fidelity and the energy minimization constraints. In this study, multiple tumor diameters and positions were simulated within the liver for on-board 4D-CBCT imaging. The on-board 4D-CBCT reconstructed by the proposed method was compared with the “ground truth” image. Results: The preliminary data, which uses reconstruction for lung radiotherapy suggests that the advanced reconstruction algorithm including the gold nanoparticle constraint will Resultin volume percentage differences (VPD) between lesions in reconstructed images by MM-FD and “ground truth” on-board images of 11.5% (± 9.4%) and a center of mass shift of 1.3 mm (± 1.3 mm) for liver radiotherapy. Conclusion: The advanced MM-FD technique enforcing the additional constraints from gold nanoparticles, results in improved accuracy for reconstructing on-board 4D-CBCT of liver tumor. Varian medical systems research grant.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Avian Activity Classification Using Recurrent Networks to Fuse Videos with Metadata on Imbalanced Datasets

Activity classification plays a crucial role in various real-life scenarios involving both humans and animals. There is an increasing need for precise activity classification focused on avian-solar interactions, as the usage of solar energy facilities, such as photovoltaic array power stations, has been observed to impact bird species richness, behavior, and activity. However, there has been no work to develop an automated system to monitor and classify these avian-solar interactions. All current methods rely on human observers, which is time and human resources costly and subject to errors related to searcher efficiency. With the recent success of Deep Learning models in activity classification problems, this paper develops a recurrent neural network-based model to automatically classify six avian activities around solar energy facilities. Our proposed model integrates critical feature engineering metadata with video frame data, enabling improved learning and more accurate activity classification. Furthermore, we address the challenge of data imbalance during training and demonstrate the efficacy of our model in detecting and classifying different activities within video tracks. Additionally, we analyze the saliency/backpropagation map of the trained proposed model and validate its decision-making rationale.

Avian activity classification; bidirectional LSTM;↗
NASA NTRS2020

Biomass Estimation from Simulated GEDI, ICESat-2 and NISAR Across Environmental Gradients in Sonoma County, California

Estimates of the magnitude and distribution of aboveground carbon in Earth’s forests remain uncertain, yet knowledge of forest carbon content at a global scale is critical for forest management in support of climate mitigation. In light of this knowledge gap, several upcoming spaceborne missions aim to map forest aboveground biomass, and many new biomass products are expected from these datasets. As these new missions host different technologies, each with relative strengths and weaknesses for biomass retrieval, as well as different spatial resolutions, consistently comparing or combining biomass estimates from these new datasets will be challenging. This paper presents a demonstration of an inter-comparison of biomass estimates from simulations of three NASA missions (GEDI, ICESat-2 and NISAR) over Sonoma county in California, USA. We use a high resolution, locally calibrated airborne lidar map as our reference dataset, and emphasize the importance of considering uncertainties in both reference maps and spaceborne estimates when conducting biomass product validation. GEDI and ICESat-2 were simulated from airborne lidar point clouds, while UAVSAR’s L-band backscatter was used as a proxy for NISAR. To estimate biomass for the lidar missions we used GEDI’s footprint-level biomass algorithms, and also adapted these for application to ICESat-2. For UAVSAR, we developed a locally trained biomass model, calibrated against the ALS reference map. Each mission simulation was evaluated in comparison to the local reference map at its native product resolution (25 m, 100m transect, and 1 ha) yielding RMSEs of 57%, 75%, and 89% for GEDI, NISAR, and ICESat-2 respectively. RMSE values increased for GEDI’s power beam during simulated daytime conditions (64%), coverage beam during nighttime conditions (72%), and coverage beam daytime conditions (87%). We also test the application of GEDI’s biomass modeling framework for estimation of biomass from ICESat-2, and fine that ICESat-2 yields reasonable biomass estimates, particularly in relatively short, open canopies. Results suggest that while all three missions will produce datasets useful for biomass mapping, tall, dense canopies such as those found in Sonoma County present the greatest challenges for all three missions, while steep slopes also prove challenging for single-date SAR based biomass retrieval. Our methods provide guidance for the inter-comparison and validation of spaceborne biomass estimates through the use of airborne lidar reference maps, and could be repeated with on-orbit estimates in any area with high quality field plot and ALS data. These methods allow for regional interpretations and filtering of multi-mission biomass estimates toward improved wall-to-wall biomass maps through data fusion.

GEDI↗
DOE OSTI2024

CFD unified approach under Eulerian–Lagrangian framework for methanol and gasoline direct injection sprays in evaporative and flash boiling conditions

Innovative synthetic fuels for advanced propulsion systems, such as methanol and ammonia, and synthetic blended fuels (E00, E10, and E30), known for their high volatility, are often injected directly into combustion chambers. It follows that Eulerian–Lagrangian spray models need to accurately capture the spray collapse as a consequence of flash boiling onset and be capable of proficiently handling the preferential evaporation of multi-component fuels in evaporative scenarios. So, we performed the assessment of an Eulerian–Lagrangian CFD code for simulating methanol and E00 gasoline blend sprays in both early and late injection conditions involving flash boiling conditions and preferential evaporation. The adoption of an effervescent breakup model and of a non-equilibrium phase transition model for the discrete phase allows the adoption of a setup that is almost completely free from specific constant tuning, especially for what concerns the breakup model. We validated the simulations using experimental PLV maps of methanol and E00 sprays issued from the ECN Spray M injector. The results highlight a significantly different morphology of the methanol spray compared to the E00 one under late injection conditions. Under stratified combustion, low-volatile fuels are likely to be ignited first, and the flame propagates toward the high-volatile fuels. In conclusion, the spray collapse was also correctly reproduced, inducing the presence of a low-pressure zone and modifying the spray morphology.

E00↗
DOE OSTI2019

1CPN: A coarse-grained multi-scale model of chromatin

A central question in epigenetics is how histone modifications influence the 3D structure of eukaryotic genomes and, ultimately, how this 3D structure is manifested in gene expression. The wide range of length scales that influence the 3D genome structure presents important challenges; epigenetic modifications to histones occur on scales of angstroms, yet the resulting effects of these modifications on genome structure can span micrometers. There is a scarcity of computational tools capable of providing a mechanistic picture of how molecular information from individual histones is propagated up to large regions of the genome. In this work, a new molecular model of chromatin is presented that provides such a picture. This new model, referred to as 1CPN, is structured around a rigorous multiscale approach, whereby free energies from an established and extensively validated model of the nucleosome are mapped onto a reduced coarse-grained topology. As such, 1CPN incorporates detailed physics from the nucleosome, such as histone modifications and DNA sequence, while maintaining the computational efficiency that is required to permit kilobase-scale simulations of genomic DNA. The 1CPN model reproduces the free energies and dynamics of both single nucleosomes and short chromatin fibers, and it is shown to be compatible with recently developed models of the linker histone. It is applied here to examine the effects of the linker DNA on the free energies of chromatin assembly and to demonstrate that these free energies are strongly dependent on the linker DNA length, pitch, and even DNA sequence. In conclusion, the 1CPN model is implemented in the LAMMPS simulation package and is distributed freely for public use.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Probing cosmological isotropy with type Ia supernovae

We investigate the validity of the cosmological principle by mapping the cosmological parameters H{sub 0} and q{sub 0} through the celestial sphere. In our analysis, which is performed in a low-redshift regime to follow a model-independent approach, we use two compilations of type Ia Supernovae (SNe Ia), namely the Union2.1 and the JLA data sets. First, we show that the angular distributions for both SNe Ia data sets are statistically anisotropic at a high confidence level (p-value < 0.0001), particularly the JLA sample. Then we find that the cosmic expansion and acceleration are mainly of dipolar type, with maximal anisotropic expansion [acceleration] pointing toward (l,b)≃(326{sup ∘},12{sup ∘}) [(l,b)≃(174{sup ∘},27{sup ∘})], and (l,b)≃(58{sup ∘},−60{sup ∘}) [(l,b)≃(225{sup ∘},51{sup ∘})] for the Union2.1 and JLA data, respectively. Second, we use a geometrical method to test the hypothesis that the non-uniformly distributed SNe Ia events could introduce anisotropic imprints on the cosmological expansion and acceleration. For the JLA compilation, we found significant correlations between the celestial distribution of data points and the directional studies of H{sub 0} and q{sub 0}, suggesting that these results can be attributed to the intrinsic anisotropy of the sample. In the case of the Union2.1 data, nonetheless, these correlations are less pronounced, and we verify that the dipole asymmetry found in the H{sub 0} analyses coincides with the well-known bulk-flow motion of our local group. From these analyses, we conclude that the directional asymmetry on the cosmological parameter maps are mainly either of local origin or due to celestial incompleteness of current SNe Ia samples.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Towards Automatic Mapping of Vulnerabilities to Attack Patterns using Large Language Models

With the advent of new devices and applications, cyber attack surface is continuously evolving due to the emergence of new attack techniques and vulnerabilities. Hence, security management tool must assess the cyber risk of an enterprise at regular interval basis through comprehensively identifying associations among attack techniques, weakness, and vulnerabilities. However, existing repositories providing such associations are incomplete (i.e., missing associations), inducing the likelihood of undermining the risk of particular set of attack techniques. Moreover, such associations still rely on manual interpretation, which is slow compared to attack speed and ineffective for the increasing list of vulnerabilities and attack actions. Therefore, there is an urge to develop methodologies for automatically associating vulnerabilities to all relevant attack techniques. In this paper, we present a framework, named VWC-MAP, that can automatically identify all relevant attack techniques of a vulnerability via weakness based on their text descriptions, applying natural language process (NLP) techniques. To achieve that, we present a novel two-tiered classification approach, where the first tier classifies vulnerabilities to weakness, and the second tier classifies weakness to attack techniques. This research has improved the scalability of the current state-of-the-art tool to make vulnerability to weakness mapping significantly faster. Moreover, this paper presents two novel approaches for weakness to attack technique mapping applying Text-to-Text and link prediction techniques. Our experiment results cross-validated through cyber-security experts show that VWC-MAP can associate vulnerabilities to weakness types with 87% accuracy and to new attack patterns with 80% accuracy.

Das, Siddhartha Shankar↗