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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Effects of Fault Roughness on Coseismic Slip and Earthquake Locations

We report that fault zone structure is well known to exert strong controls on earthquake properties including coseismic slip distribution, rupture propagation direction, and hypocenter location. It has also been well established that the principal slip surface, which accommodates the majority of earthquake displacement, exhibits roughness at all scales following self-affine fractal distributions. Here we explore the relationship between fault roughness and specific earthquake properties including coseismic slip distribution and hypocenter location based on long-term simulations of earthquake catalogs on fractally rough faults. We begin by using the von Kármán autocorrelation function to procedurally generate single faults with different fractal roughness properties, which we place in a homogeneous elastic solid and apply pure right-lateral shear at a constant back slip rate with the earthquake simulator RSQSim. Running the simulations for 10,000 years each, we generate millions of earthquakes including thousands of events with Mw > 6.0, which rupture the surface. We show that the patterns of surface rupture in these large events follow self-affine fractal distributions with consistent fractal dimension related distinctly to the fractal dimension of the fault. In addition, the hypocenters of these large events occur in very specific predictable locations where the longest wavelength structure produces a stress asperity (i.e., restraining bend). The resulting patterns can explain many features observed on real fault systems, including clustered hypocenter locations, spatially variable coseismic slip distributions, and characteristic slip recurrence behavior. Lastly, these results demonstrate a quantitative link between a directly measurable fault property—roughness—and the properties of future earthquakes.

58 GEOSCIENCES↗
DOE OSTI2017

Frustration and thermalization in an artificial magnetic quasicrystal

Here, artificial frustrated systems offer a playground to study the emergent properties of interacting systems. Most work to date has been on spatially periodic systems, known as artificial spin ices when the interacting elements are magnetic. Here we have studied artificial magnetic quasicrystals based on quasiperiodic Penrose tiling patterns of interacting nanomagnets. We construct a low-energy configuration from a step-by-step approach that we propose as a ground state. Topologically induced emergent frustration means that this configuration cannot be constructed from vertices in their ground states. It has two parts, a quasi-one-dimensional ‘skeleton’ that spans the entire pattern and is capable of long-range order, surrounding ‘flippable’ clusters of macrospins that lead to macroscopic degeneracy. Magnetic force microscopy imaging of Penrose tiling arrays revealed superdomains that are larger for more strongly coupled arrays, especially after annealing the array above its blocking temperature.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2018

Informing energy consumption uncertainty: an analysis of energy data revisions

Quality energy consumption data are important for many types of analysis, and global data sets estimate trends of county level energy consumption, derived from country reported data and regional reports. We present a novel basis for informing uncertainty in energy data by quantifying the changes in reported energy consumption as countries update their previously reported data. We use 17 editions of the British Petroleum World Energy Statistics (2001-2017) to evaluate how reported energy consumption is revised over time in aggregate coal, oil, and natural gas consumption data. We find that 70% of non-zero data points are adjusted by an average of 1.3% of a country’s total fossil fuel use in the year after their first publication. Earlier data points are revised less often, but almost half of historical trends contain some revisions in later years. The size and rate of data revisions vary over countries and fuels: coal data points have larger, less frequent revisions while oil data points have smaller more frequent revisions, with natural gas in between. A K-means cluster analysis was performed to group together countries with similar revision patterns. These groups span income, economy classification, OECD membership, and regions. Standard country groupings, therefore, do not predict the extent to which a country's energy data has undergone revisions in the past.

Hoesly, Rachel M. (ORCID:0000000309652729)↗
DOE OSTI2026

CAHS: Context-Aware Homology Search

Protein homology search is foundational to bioinformatics: it supports annotation transfer, structure/function inference, and evolutionary analysis over rapidly expanding sequence repositories (e.g., UniProtKB). Profile hidden Markov models (pHMMs), as implemented in HMMER, remain the most widely trusted approach because they provide statistically calibrated E-values; however, their gap behavior is fixed once a profile is trained, despite biological evidence that insertion/deletion tolerance varies across flexible loops and intrinsically disordered regions. We present CAHS (Context-Aware Homology Search), a lightweight query-time adapter for pHMM search that incorporates learned and biologically motivated signals without changing HMMER's downstream search pipeline or its calibrated E-value reporting. Given a query sequence, CAHS computes per-residue representations from a protein language model and a disorder predictor, maps these to profile coordinates, and modulates only match-state transition rows (gap-open and gap-extension probabilities) while preserving Plan7 constraints. We comprehensively evaluate CAHS across six structurally diverse protein families and multi-domain architectures against a 570k-sequence target corpus. CAHS expands detection capability, retrieving thousands of additional remote homologs at relaxed thresholds by maintaining alignment quality through flexible regions. For multi-domain proteins, context-aware modulation resolves 94% of fragmented alignments. Crucially, CAHS preserves hit-set invariance at stringent operating points (E<10-10), demonstrating increased statistical confidence without inflating false positives. Furthermore, sharper statistical distinction between homologs and background noise during early filter stages yields up to a 3.87× acceleration in end-to-end wall-clock time on high-performance computing clusters. Overall, CAHS illustrates a practical AI-for-science design pattern: augmenting a trusted probabilistic model with query-specific learned signals to improve interpretable, reproducible inference in data-rich biology.

Bhattaram, Swethasree [Georgia Institute of Techno↗
NASA NTRS1996

Cloud Classification in Polar and Desert Regions and Smoke Classification from Biomass Burning Using a Hierarchical Neural Network

This research focuses on a new neural network scene classification technique. The task is to identify scene elements in Advanced Very High Resolution Radiometry (AVHRR) data from three scene types: polar, desert and smoke from biomass burning in South America (smoke). The ultimate goal of this research is to design and implement a computer system which will identify the clouds present on a whole-Earth satellite view as a means of tracking global climate changes. Previous research has reported results for rule-based systems (Tovinkere et at 1992, 1993) for standard back propagation (Watters et at. 1993) and for a hierarchical approach (Corwin et al 1994) for polar data. This research uses a hierarchical neural network with don't care conditions and applies this technique to complex scenes. A hierarchical neural network consists of a switching network and a collection of leaf networks. The idea of the hierarchical neural network is that it is a simpler task to classify a certain pattern from a subset of patterns than it is to classify a pattern from the entire set. Therefore, the first task is to cluster the classes into groups. The switching, or decision network, performs an initial classification by selecting a leaf network. The leaf networks contain a reduced set of similar classes, and it is in the various leaf networks that the actual classification takes place. The grouping of classes in the various leaf networks is determined by applying an iterative clustering algorithm. Several clustering algorithms were investigated, but due to the size of the data sets, the exhaustive search algorithms were eliminated. A heuristic approach using a confusion matrix from a lightly trained neural network provided the basis for the clustering algorithm. Once the clusters have been identified, the hierarchical network can be trained. The approach of using don't care nodes results from the difficulty in generating extremely complex surfaces in order to separate one class from all of the others. This approach finds pairwise separating surfaces and forms the more complex separating surface from combinations of simpler surfaces. This technique both reduces training time and improves accuracy over the previously reported results. Accuracies of 97.47%, 95.70%, and 99.05% were achieved for the polar, desert and smoke data sets.

Alexander, June↗
NASA NTRS1989

Dynamics of super cloud clusters, westerly wind bursts, 30-60 day oscillations and ENSO - An unified view

Results are presented on numerical experiments that were carried out to investigate the mechanisms of the observed variabilities in wind and convection associated with supercloud clusters (SCCs), westerly wind bursts, and 30-60 day oscillations in the western Pacific region. It is shown that the generation of a 30-60 day eastward propagating precipitation pattern in the Lau and Peng (1987) model, which can be identified as SCC, is accompanied by convective clusters coming in opposite direction to that of the SCC itself. The results suggest that the westward propagating cloud clusters are produced at the initial stage of the 30-60 day disturbance due to mutual adjustment of the large-scale flow and heating.

Lau, K.-M.↗
DOE OSTI2026

Characterizing Spatial and Temporal Variability of California’s Coastal Upwelling Using a Satellite-Derived CUTI Index

Ocean upwelling off California is a wind-driven coastal process with complex interactions between spatial wind patterns and sea surface temperatures. Large-scale spatial and temporal upwelling characterizations are traditionally based on numerical models, while quantifying upwelling with direct observations is not straightforward. To complement model-based upwelling characterizations, here we use satellite estimates of winds, sea surface temperatures, and ocean currents along the California coast to calculate the coastal upwelling transport index (CUTI), originally calculated from a numerical ocean model. The CUTI framework accounts for both Ekman and geostrophic offshore-directed ocean transport, the latter of which is often neglected in satellite-based upwelling assessments. The approach yields the CUTI over a 30-yr period at a daily temporal and 0.25° spatial resolution to analyze upwelling patterns, with focus on potential Morro Bay and Humboldt wind energy areas. Morro Bay (CUTI median of 0.63 m 2 s −1 ) exhibits stronger, more consistent upwelling throughout the year, whereas Humboldt (CUTI median of 0.45 m 2 s −1 ) exhibits a pronounced seasonal cycle with strongest upwelling in the summer and occasional downwelling in winter. The cluster analysis of 1993–2022 satellite maps of CUTI identifies four characteristic spatial upwelling patterns along the coast, corresponding to distinct spatial distributions of sea surface temperature anomalies, Ekman transport, and geostrophic ocean water transport. This dataset provides an observation-based foundation for characterizing natural coastal upwelling off the California coast, showing good agreement with the original model CUTI while being based on satellite data and offering increased spatial resolution.

16 TIDAL AND WAVE POWER↗
NASA NTRS2023

The optimization of model ensemble composition and size can enhance the robustness of crop yield projections

Linked climate and crop simulation models are widely used to assess the impact of climate change on agriculture. However, it is unclear how ensemble configurations (model composition and size) influence crop yield projections and uncertainty. Here, we investigate the influences of ensemble configurations on crop yield projections and modeling uncertainty from Global Gridded Crop Models and Global Climate Models under future climate change. We performed a cluster analysis to identify distinct groups of ensemble members based on their projected outcomes, revealing unique patterns in crop yield projections and corresponding uncertainty levels, particularly for wheat and soybean. Furthermore, our findings suggest that approximately six Global Gridded Crop Models and 10 Global Climate Models are sufficient to capture modeling uncertainty, while a cluster-based selection of 3-4 Global Gridded Crop Models effectively represents the full ensemble. The contribution of individual Global Gridded Crop Models to overall uncertainty varies depending on region and crop type, emphasizing the importance of considering the impact of specific models when selecting models for local-scale applications. Our results emphasize the importance of model composition and ensemble size in identifying the primary sources of uncertainty in crop yield projections, offering valuable guidance for optimizing ensemble configurations in climate-crop modeling studies tailored to specific applications.

Agriculture↗
DOE OSTI2021

Elucidation of Agonist and Antagonist Dynamic Binding Patterns in ER-α by Integration of Molecular Docking, Molecular Dynamics Simulations and Quantum Mechanical Calculations

Estrogen receptor alpha (ERα) is a ligand-dependent transcriptional factor in the nuclear receptor superfamily. Many structures of ERα bound with agonists and antagonists have been determined. However, the dynamic binding patterns of agonists and antagonists in the binding site of ERα remains unclear. Therefore, we performed molecular docking, molecular dynamics (MD) simulations, and quantum mechanical calculations to elucidate agonist and antagonist dynamic binding patterns in ERα. 17β-estradiol (E2) and 4-hydroxytamoxifen (OHT) were docked in the ligand binding pockets of the agonist and antagonist bound ERα. The best complex conformations from molecular docking were subjected to 100 nanosecond MD simulations. Hierarchical clustering was conducted to group the structures in the trajectory from MD simulations. The representative structure from each cluster was selected to calculate the binding interaction energy value for elucidation of the dynamic binding patterns of agonists and antagonists in the binding site of ERα. The binding interaction energy analysis revealed that OHT binds ERα more tightly in the antagonist conformer, while E2 prefers the agonist conformer. The results may help identify ERα antagonists as drug candidates and facilitate risk assessment of chemicals through ER-mediated responses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Long-Term Variability of Soil Moisture in the Southern Sierra: Measurement and Prediction

Using 6 yr (Water Year [WY] 2009–WY 2014) of hourly in situ measurements from a spatially distributed water-balance cluster, we quantified the long-term accuracy of an algorithm used to predict spatial patterns of depth-integrated soil-water storage within the rain–snow transition zone of the southern Sierra Nevada. The algorithm—the multivariate, non-parametric regression-tree estimator Random Forest—was used to predict soil-water storage based on a combination of attributes at each instrument cluster (soil texture, topographic wetness index, elevation, northness, and canopy cover). Out-of-bag R 2 (similar to cross-validation for Random Forest) was used to quantify the accuracy of the estimator for unobserved data. Accuracy was consistently high during the wet-up, snow-cover, and early recession periods of average and wet years. The accuracy declined at the end of a 3-yr dry period, and the relative rank of the independent variables in the model shifted. Soil texture was the highest-ranked independent variable across all years, followed by elevation and northness. Topographic wetness increased in importance during dry periods. Northness exhibited high importance during the wet-up and early recession periods of most water years. As a result, during dry years, the importance of elevation declined. In dry years, notable differences in soil-water storage at each depth include lower-than-average storage in the deeper regolith at the beginning of the water year and lower storage in near-surface layers during the winter resulting from transient snow cover.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Data–Driven Velocity Model Evaluation Using K–Means Clustering

In this work, we develop a data-driven clustering method to evaluate a velocity model using surface wave velocity dispersion. This is done by first computing theoretical dispersion curves for 1-D velocity profiles of all the grid locations and then splitting the resulting dispersion curves into a certain number of groups via the K-means clustering. The observed dispersion curves are also clustered following the same procedure and the velocity model is assessed by comparing the spatial patterns obtained for the observed and synthetic data sets. The method is applied to evaluate two community velocity models in southern California, CVM-S4.26 and CVM-H15.1, using phase velocity maps derived for 3–16 s Rayleigh waves. We found a good correlation in the spatial distribution of clusters between the result of CVM-S4.26 and that of the observed data, suggesting that the CVM-S4.26 fits the observed dispersion maps better than the CVM-H15.1 in terms of features extracted from the clustering analysis.

58 GEOSCIENCES↗
DOE OSTI2017

Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants

Plant metabolism underpins many traits of ecological and agronomic importance. Plants produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have not yet been elucidated. To engineer and improve metabolic traits, we will need comprehensive and accurate knowledge of the organization and regulation of plant metabolism at the genome scale. Here, we present a computational pipeline to identify metabolic enzymes, pathways, and gene clusters from a sequenced genome. Using this pipeline, we generated metabolic pathway databases for 22 species and identified metabolic gene clusters from 18 species. This unified resource can be used to conduct a wide array of comparative studies of plant metabolism. Using the resource, we discovered a widespread occurrence of metabolic gene clusters in plants: 11,969 clusters from 18 species. The prevalence of metabolic gene clusters offers an intriguing possibility of an untapped source for uncovering new metabolite biosynthesis pathways. For example, more than 1,700 clusters contain enzymes that could generate a specialized metabolite scaffold (signature enzymes) and enzymes that modify the scaffold (tailoring enzymes). In four species with sufficient gene expression data, we identified 43 highly coexpressed clusters that contain signature and tailoring enzymes, of which eight were characterized previously to be functional pathways. Finally, we identified patterns of genome organization that implicate local gene duplication and, to a lesser extent, single gene transposition as having played roles in the evolution of plant metabolic gene clusters.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1975

Joint pattern recognition/data compression concept for ERTS multispectral imaging

This paper describes a new technique which jointly applies clustering and source encoding concepts to obtain data compression. The cluster compression technique basically uses clustering to extract features from the measurement data set which are used to describe characteristics of the entire data set. In addition, the features may be used to approximate each individual measurement vector by forming a sequence of scalar numbers which define each measurement vector in terms of the cluster features. This sequence, called the feature map, is then efficiently represented by using source encoding concepts. A description of a practical cluster compression algorithm is given and experimental results are presented to show trade-offs and characteristics of various implementations. Examples are provided which demonstrate the application of cluster compression to multispectral image data of the Earth Resources Technology Satellite.

Hilbert, E. E.↗
DOE OSTI2018

A genome-wide analysis of the flax (Linum usitatissimum L.) dirigent protein family: from gene identification and evolution to differential regulation

Identification of DIR encoding genes in flax genome. Analysis of phylogeny, gene/protein structures and evolution. Identification of new conserved motifs linked to biochemical functions. Investigation of spatio-temporal gene expression and response to stress. Dirigent proteins (DIRs) were discovered during 8-8' lignan biosynthesis studies, through identification of stereoselective coupling to afford either (+)- or (-)-pinoresinols from E-coniferyl alcohol. DIRs are also involved or potentially involved in terpenoid, allyl/propenyl phenol lignan, pterocarpan and lignin biosynthesis. DIRs have very large multigene families in different vascular plants including flax, with most still of unknown function. DIR studies typically focus on a small subset of genes and identification of biochemical/physiological functions. Herein, a genome-wide analysis and characterization of the predicted flax DIR 44-membered multigene family was performed, this species being a rich natural grain source of 8-8' linked secoisolariciresinol-derived lignan oligomers. All predicted DIR sequences, including their promoters, were analyzed together with their public gene expression datasets. Expression patterns of selected DIRs were examined using qPCR, as well as through clustering analysis of DIR gene expression. These analyses further implicated roles for specific DIRs in (-)-pinoresinol formation in seed-coats, as well as (+)-pinoresinol in vegetative organs and/or specific responses to stress. Phylogeny and gene expression analysis segregated flax DIRs into six distinct clusters with new cluster-specific motifs identified. We propose that these findings can serve as a foundation to further systematically determine functions of DIRs, i.e. other than those already known in lignan biosynthesis in flax and other species. Given the differential expression profiles and inducibility of the flax DIR family, we provisionally propose that some DIR genes of unknown function could be involved in different aspects of secondary cell wall biosynthesis and plant defense.

dirigent protein↗
DOE OSTI2021

Formin nanoclustering-mediated actin assembly during plant flagellin and DSF signaling

Plants respond to bacterial infection acutely with actin remodeling during plant immune responses. The mechanisms by which bacterial virulence factors (VFs) modulate plant actin polymerization remain enigmatic. Here, we show that plant-type-I formin serves as the molecular sensor for actin remodeling in response to two bacterial VFs: Xanthomonas campestris pv. campestris (Xcc) diffusible signal factor (DSF), and pathogen-associated molecular pattern (PAMP) flagellin in pattern-triggered immunity (PTI). Both VFs regulate actin assembly by tuning the clustering and nucleation activity of formin on the plasma membrane (PM) at the nano-sized scale. By being integrated within the cell-wall-PM-actin cytoskeleton (CW-PM-AC) continuum, the dynamic behavior and function of formins are highly dependent on each scaffold layer’s composition within the CW-PM-AC continuum during both DSF and PTI signaling. Our results reveal a central mechanism for rapid actin remodeling during plant-bacteria interactions, in which bacterial signaling molecules fine tune plant formin nanoclustering in a host mechanical-structure-dependent manner.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Identification of tau leptons using a convolutional neural network with domain adaptation

A tau lepton identification algorithm,DeepTau, based on convolutional neural network techniques, has been developed in the CMS experiment to discriminate reconstructed hadronic decays of tau leptons (τ h ) from quark or gluon jets and electrons and muons that are misreconstructed as τ h candidates. The latest version of this algorithm, v2.5, includes domain adaptation by backpropagation, a technique that reduces discrepancies between collision data and simulation in the region with the highest purity of genuine τh candidates. Additionally, a refined training workflow improves classification performance with respect to the previous version of the algorithm, with a reduction of 30–50% in the probability for quark and gluon jets to be misidentified as τ h candidates for given reconstruction and identification efficiencies. This paper presents the novel improvements introduced in theDeepTau algorithm and evaluates its performance in LHC proton-proton collision data at √(s) = 13 and 13.6 TeV collected in 2018 and 2022 with integrated luminosities of 60 and 35 fb -1 , respectively. Techniques to calibrate the performance of the τ h identification algorithm in simulation with respect to its measured performance in real data are presented, together with a subset of results among those measured for use in CMS physics analyses.

Large detector-systems performance↗
DOE OSTI2021

Active matter commensuration and frustration effects on periodic substrates

In this report we show that self-driven particles coupled to a periodic obstacle array exhibit active matter commensuration effects that are absent in the Brownian limit. As the obstacle size is varied for sufficiently large activity, a series of commensuration effects appear in which the motility induced phase separation produces commensurate crystalline states, while for other obstacle sizes we find frustrated or amorphous states. The commensuration effects are associated with peaks in the amount of sixfold ordering and the maximum cluster size. When a drift force is added to the system, the mobility contains peaks and dips similar to those found in transport studies for commensuration effects in superconducting vortices and colloidal particles.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Parallel Optical Flow Processing of 4D Cardiac CT Data on Multicore Clusters, In: 2014 IEEE 17th International Conference on Computational Science and Engineering

Optical flow is the distribution of apparent velocities of movement of brightness patterns in a sequence of images. For large 3D image sequences, optical flow applications are time consuming and memory-bound. To cope with these problems, in this paper, we present parallel optical flow processing of 4D cardiac CT data on multicore cluster systems to significantly shorten the time for computing velocity fields of the heart in order to aid cardiologists in diagnosing heart disease such as myocardial infarction and cardiac dysrhythmia in time. First, we modify and extend two traditional 2D optical flow methods Horn-Schunck and Lucas-Kanade to three-dimensional cases to process the 4D cardiac CT data. Second, we extend Mat lab MPI to support parallel computing with Mat lab and Octave on these cluster systems. Then we develop the parallel Mat lab/Octave optical flow applications for the 4D cardiac CT data in detail. Our experimental results show that these parallel optical flow applications have good scalability with close to linear speedup, and are able to shorten the image processing time significantly from more than 5 hours on 4 cores to 1.5 minutes on 1024 cores.

Engineering↗