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Results for “Domain Overlapping”

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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Identification of a carbonic anhydrase–Rubisco complex within the alpha-carboxysome

Carboxysomes are proteinaceous organelles that encapsulate key enzymes of CO 2 fixation—Rubisco and carbonic anhydrase—and are the centerpiece of the bacterial CO 2 concentrating mechanism (CCM). In the CCM, actively accumulated cytosolic bicarbonate diffuses into the carboxysome and is converted to CO 2 by carbonic anhydrase, producing a high CO 2 concentration near Rubisco and ensuring efficient carboxylation. Self-assembly of the α-carboxysome is orchestrated by the intrinsically disordered scaffolding protein, CsoS2, which interacts with both Rubisco and carboxysomal shell proteins, but it is unknown how the carbonic anhydrase, CsoSCA, is incorporated into the α-carboxysome. Here, we present the structural basis of carbonic anhydrase encapsulation into α-carboxysomes from Halothiobacillus neapolitanus . We find that CsoSCA interacts directly with Rubisco via an intrinsically disordered N-terminal domain. A 1.98 Å single-particle cryoelectron microscopy structure of Rubisco in complex with this peptide reveals that CsoSCA binding is predominantly mediated by a network of hydrogen bonds. CsoSCA's binding site overlaps with that of CsoS2, but the two proteins utilize substantially different motifs and modes of binding, revealing a plasticity of the Rubisco binding site. Our results advance the understanding of carboxysome biogenesis and highlight the importance of Rubisco, not only as an enzyme but also as a central hub for mediating assembly through protein interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

GrainNN: A neighbor-aware long short-term memory network for predicting microstructure evolution during polycrystalline grain formation

High fidelity simulations of grain formation in alloys are an indispensable tool for process-to-mechanical-properties characterization. Such simulations, however, can be computationally expensive as they require fine spatial and temporal discretizations. Their cost becomes an obstacle to parametric studies and ensemble runs and ultimately makes downstream tasks like optimal control and uncertainty quantification challenging. To enable such downstream tasks, we introduce GrainNN, an efficient and accurate reduced-order model for epitaxial grain growth in additive manufacturing conditions. GrainNN is a sequence-to-sequence long-short-term-memory (LSTM) deep neural network that evolves the dynamics of manually crafted features. Its innovations are (1) an attention mechanism with grain-microstructure-specific transformer architecture; and (2) an overlapping combination of several clones of the network to generalize to grain configurations that are different from those used for training. This design enables GrainNN to predict grain formation for unseen physical parameters, grain number, domain size and geometry. Furthermore, GrainNN not only reconstructs the quantities of interest but also can be pointwise accurate. In our numerical experiments, we use a polycrystalline phase field method to both generate the training data and assess GrainNN. For multiparametric, ensemble simulations with many grains, GrainNN can be orders of magnitude faster than phase field simulations, while delivering 5%–15% pointwise error. Additionally, this speedup includes the cost of the phase field simulations for generating training data.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Complexity in the medium-range order of gallium as a polyvalent liquid metal

Simplicity in chemical composition does not always translate into simplicity in the structures and dynamics of liquids and solids. Some elementary liquid metals, such as gallium, show unusual behaviors in thermodynamic and transport properties as a result of their complex atomic structure and dynamics. Here, in this work, we study the real-space atomic correlation function of liquid gallium by neutron scattering. In the pair-distribution function, there exist two kinds of medium-range order (MRO), characterized by oscillations beyond the first nearest neighbors. On the other hand, the first neighbor shell shows only one kind of bond. The two types of MRO are strongly overlapping in space and fluctuating in time. We propose that they are the basis for anomalous behavior of liquid gallium. This view challenges the current view that liquid gallium consists of fluctuating metallic and insulating domains. These findings shed light on the interpretation of similar microscopic anomalies observed in other semimetallic liquids.

Hua, Chengyun [Oak Ridge National Laboratory (ORNL↗
DOE OSTI2022

Direction-optimizing Label Propagation Framework for Structure Detection in Graphs: Design, Implementation, and Experimental Analysis

Label Propagation is not only a well-known machine learning algorithm for classification but also an effective method for discovering communities and connected components in networks. We propose a new Direction-optimizing Label Propagation Algorithm (DOLPA) framework that enhances the performance of the standard Label Propagation Algorithm (LPA), increases its scalability, and extends its versatility and application scope. As a central feature, the DOLPA framework relies on the use of frontiers and alternates between label push and label pull operations to attain high performance. It is formulated in such a way that the same basic algorithm can be used for finding communities or connected components in graphs by only changing the objective function used. Additionally, DOLPA has parameters for tuning the processing order of vertices in a graph to reduce the number of edges visited and improve the quality of solution obtained. We present the design and implementation of the enhanced algorithm as well as our shared-memory parallelization of it using OpenMP. We also present an extensive experimental evaluation of our implementations using the LFR benchmark and real-world networks drawn from various domains. Compared with an implementation of LPA for community detection available in a widely used network analysis software, we achieve at most five times the F-Score while maintaining similar runtime for graphs with overlapping communities. We also compare DOLPA against an implementation of the Louvain method for community detection using the same LFR-graphs and show that DOLPA achieves about three times the F-Score at just 10% of the runtime. For connected component decomposition, our algorithm achieves orders of magnitude speedups over the basic LP-based algorithm on large-diameter graphs, up to 13.2× speedup over the Shiloach-Vishkin algorithm, and up to 1.6× speedup over Afforest on an Intel Xeon processor using 40 threads.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

VorLap

SAND2025-10210O VorLap is a vortex-induced vibration overlap prediction tool for static structures, such as parked wind turbines and marine turbines encountering fluid flow (water or air movement around them) that may induce vortex-induced vibration. This tool uses precomputed frequency domain data for specific cross-sectional shapes or a generalized shedding model, along with geometric data and structural natural frequency data, to identify areas and conditions where vortex-induced vibration may occur. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Moore, Kevin [Sandia National Lab. (SNL-CA), Liver↗
DOE OSTI2023

Serodominant SARS-CoV-2 Nucleocapsid Peptides Map to Unstructured Protein Regions

The SARS-CoV-2 nucleocapsid (N) protein is highly immunogenic, and anti-N antibodies are commonly used as markers for prior infection. While several studies have examined or predicted the antigenic regions of N, these have lacked consensus and structural context. Using COVID-19 patient sera to probe an overlapping peptide array, we identified six public and four private epitope regions across N, some of which are unique to this study. We further report the first deposited X-ray structure of the stable dimerization domain at 2.05 Å as similar to all other reported structures. Structural mapping revealed that most epitopes are derived from surface-exposed loops on the stable domains or from the unstructured linker regions. An antibody response to an epitope in the stable RNA binding domain was found more frequently in sera from patients requiring intensive care. Since emerging amino acid variations in N map to immunogenic peptides, N protein variation could impact detection of seroconversion for variants of concern.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2011

Multiple Damage Progression Paths in Model-Based Prognostics

Model-based prognostics approaches employ domain knowledge about a system, its components, and how they fail through the use of physics-based models. Component wear is driven by several different degradation phenomena, each resulting in their own damage progression path, overlapping to contribute to the overall degradation of the component. We develop a model-based prognostics methodology using particle filters, in which the problem of characterizing multiple damage progression paths is cast as a joint state-parameter estimation problem. The estimate is represented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control mechanism that maintains an uncertainty bound around the hidden parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump, to which we apply our model-based prognostics algorithms. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the chosen approach when multiple damage mechanisms are active

Daigle, Matthew↗
DOE OSTI2024

Report on laser-induced fluorescence transitions relevant for the microelectronics industry and sustainability applications

A wide variety of feed gases are used to generate low-temperature plasmas for the microelectronics and sustainability applications. These plasmas often have a complex combination of reactive and nonreactive species which may have spatial and temporal variations in density, temperature, and energy. Accurate knowledge of these parameters and their variations is critically important for understanding and advancing these applications through validated and predictive modeling and the design of relevant devices. Laser-induced fluorescence (LIF) provides both spatial and temporally resolved information about the plasma-produced radicals, ions, and metastables. However, the use of this powerful diagnostic tool requires the knowledge of optical transitions including excitation and fluorescence wavelengths which may not be available or scattered through a huge literature domain. In this paper, we collected, analyzed, and compiled the available transitions for laser-induced fluorescence for more than 160 chemical species relevant to the microelectronics industry and the sustainability applications. A list of species with overlapping LIF excitations and fluorescence wavelengths have been identified. Finally, this summary is intended to serve as a data reference for LIF transitions and should be updated in the future.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRSDate not supplied

Homogenization of Satellite Based Hyperspectral Infrared Sounder Data To Build Long Term Climate Record

Building long term climate record using data from multiple hyperspectral Infrared (IR)sounders requires the homogenization of different data record to ensure the consistency andcontinuity. Such a requirement comes from two perspectives: 1) the need to adjust theoverlapping measurements of different sounders to ensure the radiometric consistency in thespectral radiance domain; 2) the need for a rigorously defined scheme to ensure the radiometricconsistency being transferred to the essential climate variables derived from the radiance record.We develop a solution that uses a spectral fingerprinting scheme to derive anomalies of keyclimate variables from long term spectral radiance data record constructed using both AIRS andCrIS observations aboard AQUA, SNPP and JPSS satellites. The fingerprinting scheme usescommon radiative kernels for all sounder measurements and therefore effectively avoids thealgorithm introduced inconsistency in retrieved geophysical variables. Our approach uses aunified sampling scheme to match AIRS and CrIS observations in both spectral and spatial-temporal domain, facilitating the intercomparison of spectral radiances from different sensors(platforms). The optimized liner inversion scheme allows the direct quantification and thereforethe adjustment for the impact on the derived climate anomalies imposed by potential radiometricinconsistency between the overlapping measurements. Such a scheme also enables the low-latency data processing of long term hyperspectral sounder data records. This paper provides a detailed introduction of the spectral fingerprinting methodology.Also introduced here is the climate fingerprinting Sounder Product (ClimFiSP) developed basedon the fingerprinting methodology. ClimFiSP products include the space-time averagedproperties of key climate variables that are derived from the long-term, space-time averagedradiances from AQUA-AIRS, SNPP-CrIS, and JPSS1-CrIS. ClimFiSP will be available to usersthrough NASA'sGoddard Earth Sciences Data and Information Services Center (GES DISC).

Wan Wu↗
NASA NTRSDate not supplied

Automated Tracking of Shallow Maritime Clouds on Geostationary Imagery to Extract Lifecycle Characteristics

Shallow moist convection is ubiquitous throughout the tropics and represents a key player in boundary layer processes. Satellites have provided many statistics on shallow clouds, such as size, structure, and geographical coverage, from static views of recurring cloud fields. But determining why certain cloud features appear and persist for different periods requires a time-evolving view of their behaviors. Geostationary satellites provide a unique opportunity to follow the time evolution of individual convective features, given their enhanced spatial and temporal sampling. A cloud-tracking tool was developed to identify properties of cloud lifecycle from the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2EX) field campaign of 2019. The mission conducted intensive sampling of shallow cumulus in the West Pacific Ocean, in tandem with Rapid Scan imagery from the Advanced Himawari Imager (AHI) on the Japan Meteorological Agency’s (JMA) Himawari-8 satellite. Shallow cumulus was segmented according to thresholds in 0.5-km visible reflectance and with blurring techniques. Despite being limited to daytime hours, the segmentations yielded the best resolution possible for capturing cloud initiation and decay. The tracking procedure is based on a computer vision package that includes Kalman filters for motion prediction, object overlap search, and the Hungarian (or Kuhn-Munkres) matching algorithm for track designation. AHI radiances available within the tracked cloud boundaries are assembled to form individual spectral histories. The resulting catalog provides thousands of cloud histories for domains measuring only a few degrees in latitude and longitude. We present an overview of the cloud-tracking tool, strategies to identify development stages from cloud tracks, and preliminary results that document cumulus lifecycle properties from satellite. The application of AHI 0.5-km reflectance has both strengths and limitations when attempting to track lifecycles of the smallest resolvable clouds. We show that by aggregating cloud tracks from a few case studies of CAMP2EX, we can discern differences in cloud lifetime and development according to ensembles selected from areas of interest. The results demonstrate an ability to quantify lifetimes and assess rates of change in cloud characteristics that are likely controlled by the surrounding environment and meteorology.

Cloud Tracking↗
NASA NTRSDate not supplied

Automated Tracking of Shallow Maritime Clouds on Geostationary Imagery to Extract Lifecycle Characteristics

Shallow moist convection is ubiquitous throughout the tropics and represents a key player in boundary layer processes. Satellites have provided valuable insight on shallow clouds, such as size, structure, and geographical coverage, from static views of recurring cloud fields. But determining why certain cloud features appear and persist for different periods requires a time-evolving view of their behaviors. Geostationary satellites provide a unique opportunity to follow the time evolution of individual convective features, given their enhanced spatial and temporal sampling. A cloud-tracking tool was developed to identify properties of cloud lifecycle from the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2EX) field campaign of 2019. The mission conducted intensive sampling of shallow cumulus in the West Pacific Ocean, in tandem with Rapid Scan imagery from the Advanced Himawari Imager (AHI) on the Japan Meteorological Agency’s (JMA) Himawari-8 satellite. Shallow cumulus were segmented according to thresholds in 0.5-km visible reflectance and with blurring techniques. Despite being limited to daytime hours, the segmentations yielded the best resolution available for capturing cloud initiation, growth, and decay. The tracking procedure is based on a computer vision package that includes Kalman filters for motion prediction, object overlap search, and the Hungarian (or Kuhn-Munkres) matching algorithm for track designation. AHI radiances available within the tracked cloud boundaries are assembled to form individual spectral histories. The resulting catalog provides thousands of cloud histories for domains measuring only a few degrees in latitude and longitude. We present an overview of the cloud-tracking tool and its results for cloud fields sampled throughout CAMP2EX by the airborne P-3. Cloud tracks were selected from about 10 flights to form ensembles, specific groups of tracks occurring in a region with airborne sampling. Cloud lifecycle properties, including duration and maximum area, are calculated for all ensemble members, and analyzed for cloud behavior and P-3 coincidences. By following this strategy, we quantitatively assess the degree of airborne sampling for specific cloud classes defined by the lifecycle calculations. We can summarize which cloud classes had more sampling, the stage of development during sampling, and general differences in character (e.g., isolated congestus vs. cold-pool producer).

Cloud Tracking↗
NASA NTRSDate not supplied

A Machine Learning Framework for Error Compensation in Radiative Transfer Calculations

Radiative heat transfer influences the amount of heat flux transferred to the surface of the hypersonic vehicle, which is essential to evaluate the performance of thermal protection systems. The radiative heat flux is found to be computationally prohibitive while accounting for the variation in spatial, angular, and spectral domains. A new methodology has been recently developed to alleviate the cost of computation in the spectral domain by constructing flow-agnostic reduced-order models (ROMs). The developed spectral ROM databases provide grouping strategies that account for non-equilibrium absorption and emission as well as interaction between disparate species due to spectral overlap in associated radiative processes. However, the developed ROMs need to be optimized for a specific combination of interacting gas species and would need to re-calibrated in case individual species are added/omitted. In this work, we use various machine learning (ML) techniques to approximate the radiative intensities determined by a ROM optimized for a specific gas mixture. The ML model relies on the ROM databases developed for a single species which ignores any spectral overlap. Thus, radiation evaluation starts with a simple summation of radiative intensities predicted using these non-calibrated ROMs for the contributing species. The ML framework then provides a correction to account for the interplay in the frequency, i.e., emission of photons by one species and absorption by another, and yields mixture-specific radiation fields. Once trained on the individual ROM databases, the ML framework offers instantaneous corrections that serves as a time/cost effective alternative to the optimization of ROMs for a specific gas mixture. The ML framework is trained on both the high fidelity and ROM evaluated line of sight (LOS) data from Orion, Stardust, and FIRE II cases to obtain a general purpose correction model for earth re-entry scenarios when radiation contributions from both atomic nitrogen and atomic oxygen are considered. A geometric length scale parameter is used in the training process to account for errors introduced in the ROM databases as a consequence of high optical thickness. The efficacy of the ML framework is underscored through extensive analysis of train and test errors with respect to all the re-entry scenarios. The applicability of such an ML framework was further corroborated by embedding it in a state-of-the-art US3D - NERO system for determining the radiative heat flux transferred to the hypersonic vehicle surface.

Radiation↗
NASA NTRS1991

Multidomain spectral solution of the Euler gas-dynamics equations

The present interfacial treatments for Euler gasdynamic equation computations via the multidomain Chebyshev spectral collocation method are applicable both at subdomain corners and in overlapping or patched subdomains, for interfaces located in the sub-, super-, or transonic regions of a flow. The results thus obtained are found to be (1) spectrally accurate, (2) both more accurate and more efficient than a single-domain calculation, and (3) potentially more accurate and efficient than a single-domain calculation. Interfacial wave reflection is insignificant.

Kopriva, David A.↗
NASA NTRS1993

A zonal CFD method for three-dimensional wing simulations

The primary objective of this work is to demonstrate the feasibility of a 3D potential/viscous flow coupling procedure for reducing computational effort while maintaining solution accuracy. The closed-loop, overlapped, velocity-coupling concept has been developed in a new code, ZAP3D, that couples a potential flow panel code with a Navier-Stokes method. The current ZAP3D calculation for an aspect ratio 5 wing with an outer domain radius of about 1.2 chords represents a speed-up in CPU time over the ARC3D large domain calculation by about a factor of 2.5. This improvement is achieved for less than a 0.5 percent deviation in C(L), 10 counts change in C(D), and 0.0015 variation in C(My). Additional reductions in the required computational domain for ZAP3D are expected as the method is further developed and refined.

Summa, J. M.↗
NASA NTRS1992

Proceedings of the Workshop on Change of Representation and Problem Reformulation

The proceedings of the third Workshop on Change of representation and Problem Reformulation is presented. In contrast to the first two workshops, this workshop was focused on analytic or knowledge-based approaches, as opposed to statistical or empirical approaches called 'constructive induction'. The organizing committee believes that there is a potential for combining analytic and inductive approaches at a future date. However, it became apparent at the previous two workshops that the communities pursuing these different approaches are currently interested in largely non-overlapping issues. The constructive induction community has been holding its own workshops, principally in conjunction with the machine learning conference. While this workshop is more focused on analytic approaches, the organizing committee has made an effort to include more application domains. We have greatly expanded from the origins in the machine learning community. Participants in this workshop come from the full spectrum of AI application domains including planning, qualitative physics, software engineering, knowledge representation, and machine learning.

Lowry, Michael R.↗
DOE OSTI2022

Nebulized delivery of a broadly neutralizing SARS-CoV-2 RBD-specific nanobody prevents clinical, virological, and pathological disease in a Syrian hamster model of COVID-19

There remains an unmet need for globally deployable, low-cost therapeutics for the ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Previously, we reported on the isolation and in vitro characterization of a potent single-domain nanobody, NIH-CoVnb-112, specific for the receptor-binding domain (RBD) of SARS-CoV-2. Here, we report on the molecular basis for the observed broad in vitro neutralization capability of NIH-CoVnb-112 against variant SARS-CoV-2 pseudoviruses. The structure of NIH-CoVnb-112 bound to SARS-CoV-2 RBD reveals a large contact surface area overlapping the angiotensin converting enzyme 2 (ACE2) binding site, which is largely unencumbered by the common RBD mutations. In an in vivo pilot study, we demonstrate effective reductions in weight loss, viral burden, and lung pathology in a Syrian hamster model of COVID-19 following nebulized delivery of NIH-CoVnb-112. These findings support the further development of NIH-CoVnb-112 as a potential adjunct preventative therapeutic for the treatment of SARS-CoV-2 infection.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1991

Kinematic domain decomposition to simulate flows past moving objects

A new method is developed to solve the unsteady Navier-Stokes equations on a composite grid which consists of subdomain grids moving with respect to each other. These subdomains are structured grids with different topologies. However, their interfaces are unstructured in the sense that they may or may not arbitrarily overlap with each other. This method captures the boundary-motion-induced flow component. The method is demonstrated for a transonic flow past an airfoil which experiences a combined motion of pitching and plunging.

Baysal, Oktay↗
DOE OSTI2021

Charmed and ϕ meson decay constants from 2+1-flavor lattice QCD *

Abstract On a lattice with 2+1-flavor dynamical domain-wall fermions at the physical pion mass, we calculate the decay constants of , , and . The lattice size is , which corresponds to a spatial extension of fm, with a lattice spacing of fm. For the valence light, strange, and charm quarks, we use overlap fermions at several mass points close to their physical values. Our results at the physical point are MeV, MeV, MeV, MeV, and MeV. The couplings of and to the tensor current ( ) can be derived from ratios and , respectively, which are the first lattice quantum chromodynamics (QCD) results. We also obtain ratios and , which reflect the size of heavy quark symmetry breaking in charmed mesons. Ratios and can be taken as a measure of SU (3) flavor symmetry breaking.

Chen, Ying↗