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At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Understanding the hydrological response of a headwater-dominated catchment by analysis of distributed surface–subsurface interactions

Abstract We computationally explore the relationship between surface–subsurface exchange and hydrological response in a headwater-dominated high elevation, mountainous catchment in East River Watershed, Colorado, USA. In order to isolate the effect of surface–subsurface exchange on the hydrological response, we compare three model variations that differ only in soil permeability. Traditional methods of hydrograph analysis that have been developed for headwater catchments may fail to properly characterize catchments, where catchment response is tightly coupled to headwater inflow. Analyzing the spatially distributed hydrological response of such catchments gives additional information on the catchment functioning. Thus, we compute hydrographs, hydrological indices, and spatio-temporal distributions of hydrological variables. The indices and distributions are then linked to the hydrograph at the outlet of the catchment. Our results show that changes in the surface–subsurface exchange fluxes trigger different flow regimes, connectivity dynamics, and runoff generation mechanisms inside the catchment, and hence, affect the distributed hydrological response. Further, changes in surface–subsurface exchange rates lead to a nonlinear change in the degree of connectivity—quantified through the number of disconnected clusters of ponding water—in the catchment. Although the runoff formation in the catchment changes significantly, these changes do not significantly alter the aggregated streamflow hydrograph. This hints at a crucial gap in our ability to infer catchment function from aggregated signatures. We show that while these changes in distributed hydrological response may not always be observable through aggregated hydrological signatures, they can be quantified through the use of indices of connectivity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Conditional diffusion machine-learning framework for mapping valence electron distribution from convergent beam electron diffraction

Quantitative convergent beam electron diffraction (CBED) enables determination of aspherical valence electron distributions through refinement of low-order structure factors, which are highly sensitive to chemical bonding and charge density variations. However, conventional quantitative CBED (QCBED) requires solving a highly nonlinear inverse problem with many coupled parameters, and computationally intensive dynamical diffraction calculations, making it time-consuming and difficult to apply to complex systems. More broadly, reconstructing charge density and orbital electron distribution from diffraction data has long been a central challenge in both x-ray and electron crystallography. Here, in this study, we introduce an artificial-intelligence (AI)-based framework that replaces traditional refinement with a data-driven inverse solver. Using a large synthetic CBED dataset generated by Bloch-wave simulations, we train a conditional diffusion model to directly infer crystal structural parameters and multipole density formalism parameters, and hence valence electron distributions, from CBED patterns alone. By learning from forward simulations across realistic parameter space, the model effectively solves the inverse problem. Compared with direct regression approaches, the diffusion-based framework provides posterior parameter distributions for rigorous uncertainty quantification while preserving quantitative fidelity and reducing analysis time by orders of magnitude. By eliminating the need for external single-crystal x-ray diffraction data and complex nonlinear refinement, this approach enables practical, high-throughput, and in situ quantitative CBED, enabling real-time mapping of valence electron distributions and their correlation with functional responses in quantum and energy materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2010

Comparing Spatial Distributions of Solar Prominence Mass Derived from Coronal Absorption

In the present work we extend the use of this mass-inference technique to a sample of prominences observed in at least two coronal lines. This approach, in theory, allows a direct calculation of prominence mass and helium abundance and how these properties vary spatially and temporally. Our motivation is two-fold: to obtain a He(exp 0)/H(exp 0) abundance ratio, and to determine how the relative spatial distribution of the two species varies in prominences. The first of these relies on the theoretical expectation that the amount of absorption at each EUV wavelength is well-characterized. However, in this work we show that due to a saturation of the continuum absorption in the 625 A and 368 A lines (which have much higher opacity compared to 195 A-) the uncertainties in obtaining the relative abundances are too high to give meaningful estimates. This is an important finding because of its impact on future studies in this area. The comparison of the spatial distribution of helium and hydrogen presented here augments previous observational work indicating that cross-field diffusion of neutrals is an important mechanism for mass loss. Significantly different loss timescales for neutral He and H (helium drains much more rapidly than hydrogen) can impact prominence structure, and both the present and past studies suggest this mechanism is playing a role in structure and possibly dynamics. Section 2 of this paper contains a description of the observations and Section 3 summarizes the method used to infer mass along with the criteria imposed in choosing prominences appropriate for this study. Section 3 also contains a discussion of the problems due to limitations of the available data and the implications for determining relative abundances. We present our results in Section 4, including plots of radial-like scans of prominence mass in different lines to show the spatial distribution of the different species. The last section contains a discussion summarizing the importance of the qualitative results found in this work. The Appendix provides a detailed derivation of how to obtain prominence mass and helium abundance (A 1) and includes the data for all prominences studied (A2).

Gilbert, Holly↗
DOE OSTI2021

Distributionally Robust Partially Observable Markov Decision Process with Moment-Based Ambiguity

In this paper, we consider a distributionally robust partially observable Markov decision process (DR-POMDP), where the distribution of the transition-observation probabilities is unknown at the beginning of each decision period, but their realizations can be inferred using side information at the end of each period after an action being taken. We build an ambiguity set of the joint distribution using bounded moments via conic constraints and seek an optimal policy to maximize the worst-case (minimum) reward for any distribution in the set. We show that the value function of DR-POMDP is piecewise linear convex with respect to the belief state and propose a heuristic search value iteration method for obtaining lower and upper bounds of the value function. We conduct numerical studies and demonstrate the computational performance of our approach via testing instances of a dynamic epidemic control problem. Our results show that DR-POMDP can produce more robust policies under misspecified distributions of transition-observation probabilities as compared to POMDP but has less costly solutions than robust POMDP. The DR-POMDP policies are also insensitive to varying parameter in the ambiguity set and to noise added to the true transition-observation probability values obtained at the end of each decision period.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Modeling the role of local crystallographic correlations in microstructures of Ti-6Al-4V using a correlated structure visco-plastic self-consistent polycrystal plasticity formulation

This paper presents a multi-level crystal plasticity-based simulation framework for modeling mechanical response and microstructure evolution of Ti-6Al-4V with α-lath/lamellar microstructures. Here, the model is a correlated structure visco-plastic self-consistent (CS-VPSC) formulation linking three scales: a single crystals micro-scale, a lath/lamellar colony meso-scale, and a lath/lamellar aggregate macro-scale. A selected hardening law for the evolution of critical resolved shear stress per slip system used in CS-VPSC is phenomenological. However, it adjusts the resistances of basal and prismatic slip systems based on the geometry of slip transfer between adjacent lamellae. Consistent with experimental evidences, the resolved shear stress on the pyramidal slip planes is dependent not only on the stress in the direction of slip but also on the two orthogonal shear stress components and the three normal stress components (non-Schmid effects). Electron backscatter diffraction (EBSD) data in conjunction with a procedure relying on α→β phase transformation is used to construct paired variants of α-laths/lamellae satisfying their local crystallographic correlations. The procedure fits volume fractions of individual laths/lamellae with the experimental EBSD data and selects a distribution of habit planes between adjacent variants with respect to the loading direction. The simulation framework is applied to interpret the deformation behavior in tension and compression along two sample directions of Ti-6Al-4V fabricated via laser powder bed fusion. Moreover, the model is used to simulate texture evolution during rolling of the material to large strains. It is demonstrated that the model is capable of predicting plastic anisotropy/asymmetry and the concomitant texture evolution. While the model reveals a significant effect of habit plane inclination with respect to the loading direction on yield stress, the comparison of the data and model predictions shows that a random distribution of habit planes fits the flow response. It is further inferred that the tension-compression asymmetry arises from the non-Schmid effects.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Joint state-parameter estimation for the reduced fracture model via the united filter

Here, in this paper, we introduce an effective United Filter method for jointly estimating the solution state and physical parameters in flow and transport problems within fractured porous media. Fluid flow and transport in fractured porous media are critical in subsurface hydrology, geophysics, and reservoir geomechanics. Reduced fracture models, which represent fractures as lower-dimensional interfaces, enable efficient multi-scale simulations. However, reduced fracture models also face accuracy challenges due to modeling errors and uncertainties in physical parameters such as permeability and fracture geometry. To address these challenges, we propose a United Filter method, which integrates the Ensemble Score Filter (EnSF) for state estimation with the Direct Filter for parameter estimation. EnSF, based on a score-based diffusion model framework, produces ensemble representations of the state distribution without deep learning. Meanwhile, the Direct Filter, a recursive Bayesian inference method, estimates parameters directly from state observations. The United Filter combines these methods iteratively: EnSF estimates are used to refine parameter values, which are then fed back to improve state estimation. Numerical experiments demonstrate that the United Filter method surpasses the state-of-the-art Augmented Ensemble Kalman Filter, delivering more accurate state and parameter estimation for reduced fracture models. This framework also provides a robust and efficient solution for PDE-constrained inverse problems with uncertainties and sparse observations.

Bayesian inference↗
DOE OSTI2022

AI and extreme scale computing to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non-precessing black hole mergers

We use artificial intelligence (AI) to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non precessing binary black hole mergers. We trained AI models using 14 million waveforms, produced with the surrogate model NRHybSur3dq8, that include modes up to $\ell$ ≤ 4 and (5,5), except for (4,0) and (4,1), that describe binaries with mass-ratios $\textit{q}$ ≤ 8, individual spins $s^z_{\{1,2\}} \in$[–0.8,0.8], and inclination angle $θ \in$ [0,π]. Our probabilistic AI surrogates can accurately constrain the mass-ratio, individual spins, effective spin, and inclination angle of numerical relativity waveforms that describe such signal manifold. We compared the predictions of our AI models with Gaussian process regression, random forest, k-nearest neighbors, and linear regression, and with traditional Bayesian inference methods through the PyCBC Inference toolkit, finding that AI outperforms all these approaches in terms of accuracy, and are between three to four orders of magnitude faster than traditional Bayesian inference methods. Our AI surrogates were trained within 3.4 hours using distributed training on 1,536 NVIDIA V100 GPUs in the Summit supercomputer.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Labels as a feature: Network homophily for systematically annotating human GPCR drug-target interactions

Machine learning has revolutionized drug discovery by enabling the exploration of vast, uncharted chemical spaces essential for discovering novel patentable drugs. Despite the critical role of human G protein-coupled receptors in FDA-approved drugs, exhaustive in-distribution drug-target interaction testing across all pairs of human G protein-coupled receptors and known drugs is rare due to significant economic and technical challenges. This often leaves off-target effects unexplored, which poses a considerable risk to drug safety. In contrast to the traditional focus on out-of-distribution exploration (drug discovery), we introduce a neighborhood-to-prediction model termed Chemical Space Neural Networks that leverages network homophily and training-free graph neural networks with labels as features. We show that Chemical Space Neural Networks’ ability to make accurate predictions strongly correlates with network homophily. Thus, labels as features strongly increase a machine learning model’s capacity to enhance in-distribution prediction accuracy, which we show by integrating labeled data during inference. We validate these advancements in a high-throughput yeast biosensing system (3773 drug-target interactions, 539 compounds, 7 human G protein-coupled receptors) to discover novel drug-target interactions for FDA-approved drugs and to expand the general understanding of how to build reliable predictors to guide experimental verification.

Hansson, Frederik G↗
DOE OSTI2023

Limited impact of jet-induced feedback in the multi-phase nuclear interstellar medium of 4C12.50

4C12.50 (IRAS 13451+1232) at z = 0.122 is an ultraluminous infared radio galaxy that has often been proposed as a prime candidate for the link between ultraluminous infared galaxies and young radio galaxies. It is also an interesting target to investigate whether and how radio-induced feedback affects the evolution of galaxies in the early phases of radio activity. We study, in detail for the first time, the hot (≥1500 K) molecular gas in 4C12.50. The potential impact of the radio jet on this gas phase, as well as on the star formation activity, are investigated. We also study the ionised (including coronal) gas as traced by the near-infrared lines. Using near-infrared long slit spectroscopy obtained with EMIR on GTC and X-shooter on VLT, we analyse the emission line spectrum of the ionised, coronal, and, specially, the hot molecular gas in the western nucleus hosting the compact radio jet. Based on high spatial resolution ALMA CO(2–1) data, we also revise the location of 4C12.50 in the Kennicutt-Schmidt diagram in order to investigate whether star formation is suppressed. 4C12.50 hosts (2.1 ± 0.4) × 10 4 M ⊙ of hot molecular gas. An unusually high rotational temperature T rot = 3020 ± 160 K is inferred. The molecular gas mass obeys a power-law temperature distribution, dM H2 /dT ∝ T –5 , from T ~300 K and up to ~3000 K. Both results support the idea that shocks (probably induced by the radio jet) contribute to the heating and excitation of the hot molecular gas. A molecular outflow is not detected. The coupling of the outflowing ionised and neutral outflows with the hot molecular gas is poor. Contrary to other studies, we claim that there is no evidence for star formation suppression in this object. If radio-induced feedback can regulate the star formation activity in galaxies, 4C12.50 is a promising candidate to reveal this phenomenon in action. However, we find no solid evidence for a current or past impact of this mechanism on the evolution of this system, neither by clearing out the dusty central cocoon efficiently, nor by suppressing the star formation activity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Three-dimensional reconstruction of neutron, gamma-ray, and x-ray sources using a cylindrical-harmonics expansion

Inertial confinement fusion capsule implosions produce neutron, gamma-ray, and x-ray emission, which are recorded by a variety of detectors, both time integrated and time resolved, to determine the performance of the implosion. Further, two-dimensional emission images from multiple directions can now be combined to infer three-dimensional structures in the implosion, such as the distribution of thermonuclear fuel density, carbon ablator, and impurities. Because of the cost and complexity of the imaging systems, however, only a few measurements can be made, so reconstructions of the source must be made from a limited number of views. Here, a cylindrical-harmonics decomposition technique to reconstruct the three-dimensional object from two views in the same symmetry plane is presented. In the limit of zero order, this method recovers the Abel inversion method. The detailed algorithms used for this characterization and the resulting reconstructed neutron source from an experiment collected at the National Ignition Facility are presented.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Sizes of pure and doped helium droplets from single shot x-ray imaging

Advancements in x-ray free-electron lasers on producing ultrashort, ultrabright, and coherent x-ray pulses enable single-shot imaging of fragile nanostructures, such as superfluid helium droplets. Furthermore, this imaging technique gives unique access to the sizes and shapes of individual droplets. In the past, such droplet characteristics have only been indirectly inferred by ensemble averaging techniques. Here, we report on the size distributions of both pure and doped droplets collected from single-shot x-ray imaging and produced from the free-jet expansion of helium through a 5 μm diameter nozzle at 20 bars and nozzle temperatures ranging from 4.2 to 9 K. This work extends the measurement of large helium nanodroplets containing 10 9 –10 11 atoms, which are shown to follow an exponential size distribution. Additionally, we demonstrate that the size distributions of the doped droplets follow those of the pure droplets at the same stagnation condition but with smaller average sizes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Cosmic dipoles from large-scale structure surveys

Large-scale structure surveys can be used to measure the dipole in the cosmic microwave background (CMB), in the luminosity distances inferred from type-Ia supernova observations, and in the spatial distribution of galaxies and quasars. The measurements of these cosmic dipoles appear to be mutually inconsistent, even though they are expected to indicate the common observer velocity. This observational tension may represent a significant challenge to the standard model of cosmology. Here, in this work, we study in detail what contributes to the cosmic dipoles from CMB, supernova, and galaxy survey in the standard Λ cold dark matter (Λ⁢ CDM) model, though our theoretical model can be applied beyond the standard model. While measurements of the cosmic dipoles yield the relative velocities between the source samples and the observer velocity, the motion of the observer is the dominant contribution in the conformal Newtonian gauge, and the intrinsic velocities of the samples fall steeply with increasing redshift of the sources. Hence the cosmic dipoles of CMB, type-Ia supernovae, and galaxies should be aligned but can have different amplitudes. We also clarify several misconceptions that are commonly found in the literature.

cosmic microwave background↗
DOE OSTI2022

A Scalable Pipeline for Gigapixel Whole Slide Imaging Analysis on Leadership Class HPC Systems

Whole Slide Imaging (WSI) captures microscopic details of a patient's histopathological features at multiple res-olutions organized across different levels. Images produced by WSI are gigapixel-sized, and saving a single image in memory requires a few gigabytes which is scarce since a complicated model occupies tens of gigabytes. Performing a simple met-ric operation on these large images is also expensive. High-performance computing (HPC) can help us quickly analyze such large images using distributed training of complex deep learning models. One popular approach in analyzing these images is to divide a WSI image into smaller tiles (patches) and then train a simpler model with these reduced-sized but large numbers of patches. However, we need to solve three pre-processing challenges efficiently for pursuing this patch-based approach. 1) Creating small patches from a high-resolution image can result in a high number (hundreds of thousands per image) of patches. Storing and processing these images can be challenging due to a large number of I/O and arithmetic operations. To reduce I/Oand memory accesses, an optimal balance between the size and number of patches must exist to reduce I/O and memory accesses. 2) WSI images may have tiny annotated regions for cancer tissue and a significant portion with normal and fatty tissues; correct patch sampling should avoid dataset imbalance. 3) storing and retrieving many patches to and from disk storage might incur I/O latency while training a deep learning model. An efficient distributed data loader should reduce I/O latency during the training and inference steps. This paper explores these three challenges and provides empirical and algorithmic solutions deployed on the Summit supercomputer hosted at the Oak Ridge Leadership Computing Facility.

Dash, Sajal↗
DOE OSTI2022

Genome Streamlining, Proteorhodopsin, and Organic Nitrogen Metabolism in Freshwater Nitrifiers

Microbial nitrification is a critical process governing nitrogen availability in aquatic systems. Freshwater nitrifiers have received little attention, leaving many unanswered questions about their taxonomic distribution, functional potential, and ecological interactions. Here, we reconstructed genomes to infer the metabolism and ecology of free-living picoplanktonic nitrifiers across the Laurentian Great Lakes, a connected series of five of Earth’s largest lakes. Surprisingly, ammonia-oxidizing bacteria (AOB) related to Nitrosospira dominated over ammonia-oxidizing archaea (AOA) at nearly all stations, with distinct ecotypes prevailing in the transparent, oligotrophic upper lakes compared to Lakes Erie and Ontario. Unexpectedly, one ecotype of Nitrosospira encodes proteorhodopsin, which could enhance survival under conditions where ammonia oxidation is inhibited or substrate limited. Nitrite-oxidizing bacteria (NOB) “Candidatus Nitrotoga” and Nitrospira fluctuated in dominance, with the latter prevailing in deeper, less-productive basins. Genome reconstructions reveal highly reduced genomes and features consistent with genome streamlining, along with diverse adaptations to sunlight and oxidative stress and widespread capacity for organic nitrogen use. Our findings expand the known functional diversity of nitrifiers and establish their ecological genomics in large lake ecosystems. By elucidating links between microbial biodiversity and biogeochemical cycling, our work also informs ecosystem models of the Laurentian Great Lakes, a critical freshwater resource experiencing rapid environmental change.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1984

The production of ion conics by oblique double layers

Magnetized test ions are subjected to acceleration through a numerically simulated oblique double layer in order to determine whether they emerge with velocity vectors aligned with or oblique to the ambient magnetic field. A criterion for oblique alignment, depending on the double-layer parameters and on the external magnetization, is obtained. When it is applied to observed and theoretical auroral double layers, this criterion predicts that accelerated heavy ions will be substantially less magnetic field aligned than will accelerated hydrogen ions, thus suggesting auroral double layers as a source of high-energy ion conics. Test particle simulations are also used to investigate the perpendicular heating of ions at low altitudes by the electric fields associated with moving auroral arcs. The rapid motion of small-scale structures in the arcs is suggested as a source of low-energy conical ion distributions, and the slow drifts of the entire arc forms are inferred to heat ionospheric ions.

Borovsky, J. E.↗
NASA NTRS1989

Experimental investigation of convective stability in a superposed fluid and porous layer when heated from below

Experiments have been carried out in a horizontal superposed fluid and porous layer contained in a test box 24 cm x 12 cm x 4 cm high. The porous layer consisted of 3 mm diameter glass beads, and the fluids used were water, 60 and 90 percent glycerin-water solutions, and 100 percent glycerin. The depth ratio d, which is the ratio of the thickness of the fluid layer to that of the porous layer, varied from 0 to 1.0. Fluids of increasingly higher viscosity were used for cases with larger d in order to keep the temperature difference across the tank within reasonable limits. The size of the convection cells was inferred from temperature measurements made with embedded thermocouples and from temperature distributions at the top of the layer by use of liquid crystal film. The experimental results showed: (1) a precipitous decrease in the critical Rayleigh number as the depth of the fluid layer was increased from zero, and (2) an eightfold decrease in the critical wavelength between d = 0.1 and 0.2. Both of these results were predicted by the linear stability theory reported earlier (Chen and Chen, 1988).

Chen, Falin↗
NASA NTRS1990

3-D Euler calculations for aft-propfan integration

The problem of reducing engine-airframe installation drag for a transport aircraft with an aft-mounted propfan or turboprop engine is investigated. A three-dimensional Euler code is used for the analysis. Installation drag effects are inferred from a qualitative analysis of the computed velocity and pressure distributions. The basic design philosophy is to alleviate flow acceleration near the strut/nacelle junction by aerodynamic means. This involves minor reshaping and relocation of the propulsion system.

Naik, Dinesh A.↗
NASA NTRS1992

Quasar evolution and the growth of black holes

A 'minimalist' model of AGN evolution is analyzed that links the measured luminosity function to an elementary description of black hole accretion. The observed luminosity function of bright AGN is extrapolated and simple prescriptions for the growth and luminosity of black holes are introduced to infer quasar birth rates, mean fueling rates, and relict black hole distribution functions. It is deduced that the mean accretion rate scales as (M exp -1./5)(t exp -6.7) and that, for the most conservative model used, the number of relict black holes per decade declines only as M exp -0.4 for black hole masses between 3 x 10 exp 7 and 3 x 10 exp 9 solar masses. If all sufficiently massive galaxies pass through a quasar phase with asymptotic black hole mass a monotonic function of the galaxy mass, then it is possible to compare the space density of galaxies with estimated central masses to that of distant quasars.

Small, Todd A.↗