Search NASA⌕ Search

SEARCH · Search NASA

Results for “Spatial Behavior”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 973 records · Page 54

California wildfire spread derived using VIIRS satellite observations and an object-based tracking system

Changing wildfire regimes in the western US and other fire-prone regions pose considerable risks to human health and ecosystem function. However, our understanding of wildfire behavior is still limited by a lack of data products that systematically quantify fire spread, behavior and impacts. Here we develop a novel object-based system for tracking the progression of individual fires using 375 m Visible Infrared Imaging Radiometer Suite active fire detections. At each half-daily time step, fire pixels are clustered according to their spatial proximity, and are either appended to an existing active fire object or are assigned to a new object. This automatic system allows us to update the attributes of each fire event, delineate the fire perimeter, and identify the active fire front shortly after satellite data acquisition. Using this system, we mapped the history of California fires during 2012–2020. Our approach and data stream may be useful for calibration and evaluation of fire spread models, estimation of near-real-time wildfire emissions, and as means for prescribing initial conditions in fire forecast models.

54 ENVIRONMENTAL SCIENCES↗

FAST.Farm Response to Varying Wind Inflow and Discretization

FAST.Farm is a newly developed multiphysics, midfidelity engineering tool that can be used to predict turbine power and structural loads of wind turbines in a wind farm. Previous studies have shown the similarities and differences between FAST.Farm and large-eddy simulations (LES) using the same LES-precursor-generated ambient wind inflow. The ability to generate ambient wind inflow using a synthetic turbulence engineering model (e.g., TurbSim or the Mann model) has recently been integrated into FAST.Farm to potentially enable more computationally efficient usage of the tool. This work aims to: 1) compare FAST.Farm simulations using LES-generated inflow to those using synthetically generated inflow from TurbSim, and 2) establish guidelines by which TurbSim-generated inflow should be generated for wind farm analysis. It was found that properly setting spatial coherence parameters for the transverse wind velocity components is necessary to accurately predict wake meandering. It is shown that TurbSim-generated inflow can be used in FAST.Farm to accurately predict thrust, power, speed, and torque for waked and unwaked turbines; wake meandering behavior across different atmospheric conditions; and averaged wake-deficit advection and evolution effects.

17 WIND ENERGY↗

Cloud Coalescence: A Dynamical Instability Affecting Multiphase Environments

Mass and size distributions are the key characteristics of any astrophysical object, including the densest clumps comprising the cold phase of multiphase environments. In our recent papers, we showed how individual clouds of various sizes form and evolve in active galactic nuclei. In particular, we showed that large clouds undergo damped oscillations as a response to their formation process. Here we follow up this investigation, addressing how different size clouds interact. We find that smaller clouds become trapped in the advective flows generated by larger clouds. The explanation for this behavior leads to a rather remarkable conclusion: even in the absence of gravity, complexes of clouds are dynamically unstable. In an idealized environment (e.g., one free of turbulence and magnetic fields) a perfectly symmetric arrangement of static clouds will remain static, but any small spatial perturbation will lead to all clouds coalescing into a single, large cloud, given enough time. Here, by using numerical simulations, we investigate the main factors that determine the rate of coalescence. In addition to the cloud separation distance, we find that the transient response of clouds to a disturbance is the primary factor. Turbulent motions in the flow can easily suppress this tendency for spatially well-separated clouds to coalesce, so it is as yet unclear if this phenomenon can occur in nature. Nevertheless, this Letter casts strong doubts on a recent hypothesis that large clouds are prone to fragmentation.

79 ASTRONOMY AND ASTROPHYSICS↗

Spectroscopic and photometric observations of M supergiants in Carina.

Spectroscopic study of 30 Southern-Hemisphere M supergiants mostly in Carina in the blue and near-infrared, and photometrical study of these stars from 0.4 to 18 microns. The uncertainties in the determinations of interstellar extinction are discussed, and the spatial distribution of the M supergiants in the Carina arm is shown. The presence of the 11-micron excess attributed to silicate dust is a common feature. Stars of the same spectral type and luminosity class are remarkably homogeneous in their long-wave behavior. The silicate feature becomes more prominent in the more luminous stars and in stars of later spectral type. Four composite systems show little long-wave excess. The two VV Cephei objects have excesses probably produced by gas emission, and the other two have little or no excess - supporting the suggestion that the presence of the early star prohibits the formation of a dust envelope. Three stars - VY CMa, VX Sgr, and HD 9767 - appear to be extreme examples of stars with large excesses over the entire long-wave region. It is suggested that these objects are surrounded by large amounts of particulate material over a great range of distances from the stars.

Humphreys, R. M.↗

Seismology and space-based geodesy

The potential of space-based geodetic measurement of crustal deformation in the context of seismology is explored. The achievements of seismological source theory and data analyses, mechanical modeling of fault zone behavior, and advances in space-based geodesy are reviewed, with emphasis on realizable contributions of space-based geodetic measurements specifically to seismology. The fundamental relationships between crustal deformation associated with an earthquake and the geodetically observable data are summarized. The response and spatial and temporal resolution of the geodetic data necessary to understand deformation at various phases of the earthquake cycle is stressed. The use of VLBI, SLR, and GPS measurements for studying global geodynamics properties that can be investigated to some extent with seismic data is discussed. The potential contributions of continuously operating strain monitoring networks and globally distributed geodetic observatories to existing worldwide modern digital seismographic networks are evaluated in reference to mutually addressable problems in seismology, geophysics, and tectonics.

Tralli, David M.↗

Man-Machine Interaction Design and Analysis System (MIDAS): Memory Representation and Procedural Implications for Airborne Communication Modalities

The Man-Machine Interaction Design and Analysis System (MIDAS) has been under development for the past ten years through a joint US Army and NASA cooperative agreement. MIDAS represents multiple human operators and selected perceptual, cognitive, and physical functions of those operators as they interact with simulated systems. MIDAS has been used as an integrated predictive framework for the investigation of human/machine systems, particularly in situations with high demands on the operators. Specific examples include: nuclear power plant crew simulation, military helicopter flight crew response, and police force emergency dispatch. In recent applications to airborne systems development, MIDAS has demonstrated an ability to predict flight crew decision-making and procedural behavior when interacting with automated flight management systems and Air Traffic Control. In this paper we describe two enhancements to MIDAS. The first involves the addition of working memory in the form of an articulatory buffer for verbal communication protocols and a visuo-spatial buffer for communications via digital datalink. The second enhancement is a representation of multiple operators working as a team. This enhanced model was used to predict the performance of human flight crews and their level of compliance with commercial aviation communication procedures. We show how the data produced by MIDAS compares with flight crew performance data from full mission simulations. Finally, we discuss the use of these features to study communications issues connected with aircraft-based separation assurance.

Corker, Kevin M.↗

Computational Models of Human Performance: Validation of Memory and Procedural Representation in Advanced Air/Ground Simulation

The Man-Machine Interaction Design and Analysis System (MIDAS) under joint U.S. Army and NASA cooperative is intended to assist designers of complex human/automation systems in successfully incorporating human performance capabilities and limitations into decision and action support systems. MIDAS is a computational representation of multiple human operators, selected perceptual, cognitive, and physical functions of those operators, and the physical/functional representation of the equipment with which they operate. MIDAS has been used as an integrated predictive framework for the investigation of human/machine systems, particularly in situations with high demands on the operators. We have extended the human performance models to include representation of both human operators and intelligent aiding systems in flight management, and air traffic service. The focus of this development is to predict human performance in response to aiding system developed to identify aircraft conflict and to assist in the shared authority for resolution. The demands of this application requires representation of many intelligent agents sharing world-models, coordinating action/intention, and cooperative scheduling of goals and action in an somewhat unpredictable world of operations. In recent applications to airborne systems development, MIDAS has demonstrated an ability to predict flight crew decision-making and procedural behavior when interacting with automated flight management systems and Air Traffic Control. In this paper, we describe two enhancements to MIDAS. The first involves the addition of working memory in the form of an articulatory buffer for verbal communication protocols and a visuo-spatial buffer for communications via digital datalink. The second enhancement is a representation of multiple operators working as a team. This enhanced model was used to predict the performance of human flight crews and their level of compliance with commercial aviation communication procedures. We show how the data produced by MIDAS compares with flight crew performance data from full mission simulations. Finally, we discuss the use of these features to study communication issues connected with aircraft-based separation assurance.

Corker, Kevin M.↗

Space-borne Observations of Atmospheric Pre-Earthquake Signals in Seismically Active Areas: Case Study for Greece 2008-2009

We are conducting theoretical studies and practical validation of atm osphere/ionosphere phenomena preceding major earthquakes. Our approach is based on monitoring of two physical parameters from space: outgoi ng long-wavelength radiation (OLR) on the top of the atmosphere and e lectron and electron density variations in the ionosphere via GPS Tot al Electron Content (GPS/TEC). We retrospectively analyzed the temporal and spatial variations of OLR an GPS/TEC parameters characterizing the state of the atmosphere and ionosphere several days before four m ajor earthquakes (M>6) in Greece for 2008-2009: M6.9 of 02.12.08, M6. 2 02.20.08; M6.4 of 06.08.08 and M6.4 of 07.01.09.We found anomalous behavior before all of these events (over land and sea) over regions o f maximum stress. We expect that our analysis reveal the underlying p hysics of pre-earthquake signals associated with some of the largest earthquakes in Greece.

Ouzounov, D. P.↗

FAST.Farm Response to Varying Wind Inflow Techniques: Preprint

FAST.Farm is a newly developed multiphysics, midfidelity engineering tool that can be used to predict turbine power and structural loads of wind turbines in a wind farm. Previous studies have shown the similarities and differences between FAST.Farm and large-eddy simulations (LES) using the same LES-precursor-generated ambient wind inflow. The ability to generate ambient wind inflow using a synthetic turbulence engineering model (e.g., TurbSim or the Mann model) has recently been integrated into FAST.Farm to potentially enable more computationally efficient usage of the tool. This work aims to: 1) compare FAST.Farm simulations using LES-generated inflow to those using synthetically generated inflow from TurbSim, and 2) establish guidelines by which TurbSim-generated inflow should be generated for wind farm analysis. It was found that properly setting spatial coherence parameters for the transverse wind velocity components is necessary to accurately predict wake meandering. It is shown that TurbSim-generated inflow can be used in FAST.Farm to accurately predict thrust, power, speed, and torque for waked and unwaked turbines; wake meandering behavior across different atmospheric conditions; and averaged wake-deficit advection and evolution effects.

17 WIND ENERGY↗

A Data Processing Pipeline for Adversarial Socio-Technical Network Analysis

With the rapid adoption of emerging technologies, there is a need to catalog and model sociotechnical interdependencies that have been historically used to influence the operation of Critical Infrastructure networks including the impacts of mergers and acquisitions, hostile takeovers, and foreign investment. Our research intends to address this need with two primary contributions. First, we have developed a data curation and processing pipeline to generate sociotechnical networks extracted from a variety of data sources including SEC filings and infrastructure asset databases. The pipeline, implemented in Apache Airflow, extracts and normalizes the representation of entities and relations, specified within ontologies. Our intent is to provide an extensible, machine-actionable approach to quickly communicate such models, reproduce previous results, and adapt them to new, unanticipated situations. Second, networks produced by our pipeline enable the development of graph-theoretic metrics that consider the properties of network components in addition to its topology. Metadata associated with network components---whether semantic, temporal, or geospatial---affects the alignment of generated networks with assumptions underlying complexity metrics. Validation of generated networks relative to component types defined by an ontology, may allow the research community to adapt metrics to the semantics of the domains being studied. Generated networks may be processed as knowledge, dynamic, or spatial graphs and enables a variety of analyses including automated reasoning and measures of network complexity. Automated reasoning views extracted entities and relations as a knowledge graph; this enables application of inference rules that represent historically-attested adversarial business methods and applies that behavior to a specific geographic context. Measures of network complexity, including degree distribution, reachability analyses, temporal analysis, and community detection can be adapted to indicate adversarial organizational influence.

97 MATHEMATICS AND COMPUTING↗

Large-Scale Structure of the Molecular Gas in Taurus Revealed by High Spatial Dynamic Range Spectral Line Mapping

Viewgraph topics include: optical image of Taurus; dust extinction in IR has provided a new tool for probing cloud morphology; observations of the gas can contribute critical information on gas temperature, gas column density and distribution, mass, and kinematics; the Taurus molecular cloud complex; average spectra in each mask region; mas 2 data; dealing with mask 1 data; behavior of mask 1 pixels; distribution of CO column densities; conversion to H2 column density; variable CO/H2 ratio with values much less than 10(exp -4) at low N indicated by UV results; histogram of N(H2) distribution; H2 column density distribution in Taurus; cumulative distribution of mass and area; lower CO fractional abundance in mask 0 and 1 regions greatly increases mass determined in the analysis; masses determined with variable X(CO) and including diffuse regions agrees well with the found from L(CO); distribution of young stars as a function of molecular column density; star formation efficiency; star formation rate and gas depletion; and enlarged images of some of the regions with numerous young stars. Additional slides examine the origin of the Taurus molecular cloud, evolution from HI gas, kinematics as a clue to its origin, and its relationship to star formation.

molecular clouds↗

A spatially-resolved model of neutron-irradiated tungsten coupling stochastic cluster dynamics and finite deformation plasticity

Structural materials used in nuclear reactors face severe degradation in mechanical properties, such as hardening and embrittlement. At the microscopic scale, this occurs due to creation and accumulation of irradiation-induced defects and their interaction with system dislocations. Although techniques exist which can model evolution of irradiation defects, for instance kinetic transport theory-based models, their interaction with mechanical deformation of the bulk material has not been investigated extensively. In this work, we demonstrate a novel spatially-resolved multiscale coupling between microscopic irradiation defect evolution, modeled using Stochastic Cluster Dynamics (SCD) and macroscopic mechanical deformation modeled using a finite-deformation plasticity model. SCD is used to determine the statistically averaged defect cluster spacing, dependent on operating conditions such as irradiation dose and temperature. This acts as an initial condition that governs the critical resolved shear stress of dislocation glide in the macroscopic plasticity model. This framework is used to predict mechanical behavior in post-mortem test of irradiated Tungsten samples, which has found its importance as structural material used in nuclear reactors. The results obtained using the coupled approach are in good agreement with experimental data of uniaxial tension tests. The model is able to capture the effect of temperature and irradiation dose on the material hardening. Two methods are proposed to estimate hardness – using Tabor's Law relating uniaxial yield stress to hardness and from flat-punch simulations. The results are in reasonable agreement with hardness data from micro-indentation experiments of irradiated Tungsten samples. Finally, the model is also able to reveal microstructural details such as spatial variation in defect density and local stress.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Uncovering grain and subgrain microstructure at the scale of additive manufacturing melt tracks with a scalable cellular automaton solidification model

Metal additive manufacturing, characterized by rapid solidification, yields refined grains with a distinctive cellular subgrain microstructure that plays a pivotal role in determining material properties. Due to the significant computational expense demanded to simulate the required physics with submicron spatial resolution, their numerical simulations have been limited to proof-of-concept studies to either 2D or small subregions of a melt pool. In this study, an open-source, scalable, solidification code, muMatScale, based on the cellular automaton method, has been developed to predict the grain and the underlying subgrain microstructure over an entire melt pool. The model incorporates flexible parallelization schemes, utilizing MPI and OpenMP GPU Offloading, in addition to appropriate multi-physics specific to non-equilibrium rapid solidification in AM. The impact of nucleation parameters on grain microstructures was investigated with a focus on grain size variations and morphology transitions. With selected nucleation parameters, the simulation predicted the grain size, subgrain morphology, crystallographic orientation, and microsegregation aligned with experimental measurements. The model demonstrates that epitaxial grain growth is a dominant factor at the melt pool boundary, influencing grain size variation under different grain sizes in the build plate while maintaining consistent primary dendrite arm spacing under identical thermal conditions. Here, the highly efficient numerical model enables large-scale simulations with a spatial resolution of 100 nm or less, unveiling unprecedented insights into thermal and solutal diffusion driven grain growth, and the subgrains with microsegregation within grains in 3D across scales. muMatScale will enable the linking of submicron length-scale microstructure to part-level material behavior by investigating fundamental solidification problems at the intercellular scale in many-track and many-layer builds.

36 MATERIALS SCIENCE↗

Comparison of structurally diverse simulation models for prediction of epidemic outcomes caused by a long-distance dispersed pathogen

Long-distance dispersal (LDD) pathogens pose substantial challenges for epidemic control due to their ability to generate new infection foci at great distances. While various modeling approaches have been developed to understand and manage such outbreaks, little work has compared how models of different structures behave under shared conditions. Here, in this study, we compare four structurally distinct epidemiological models — EPIMUL, GEMF, PoPS, and Warwick — each adapted to simulate the spread of wheat stripe rust (WSR), a wind-dispersed LDD pathogen, under identical epidemiological parameters and dispersal kernel. Using data from a controlled field experiment, we evaluate the ability of each model to replicate disease prevalence under nine intervention scenarios that vary in timing and culling area. While the models differ substantially in design — ranging from spatial grid-based to network-based and raster-based frameworks — the shared dispersal kernel allowed for close alignment in their predictions. All models accurately captured general epidemic trends, particularly the strong effect of early intervention on disease suppression. We qualitatively compared their behavioral responses across scenarios and also evaluated an ensemble prediction by averaging across model outputs. Our findings highlight how integrating shared epidemiological components into distinct modeling frameworks can improve consistency and accuracy, while reinforcing the importance of early culling in managing LDD pathogen outbreaks.

Dispersal kernel↗

Reactive particle-tracking solutions to a benchmark problem on heavy metal cycling in lake sediments

Geochemical systems are known to exhibit highly variable spatiotemporal behavior. This may be observed both in non-smooth concentration curves in space for a single sampling time and also in variability between samples taken from the same location at different times. However, most models that are designed to simulate these systems provide only single-solution smooth curves and fail to capture the noise and variability seen in the data. We apply a recently developed reactive particle-tracking method to a system that displays highly complex geochemical behavior. When the method is made to most closely resemble a corresponding Eulerian method, in its unperturbed form, we see near-exact match between solutions of the two models. More importantly, we consider two approaches for perturbing the model and find that the spatially-perturbed condition is able to capture a greater degree of the variability present in the data. This method of perturbation is a task to which particle methods are uniquely suited and Eulerian models are not well-suited. Additionally, because of the nature of the algorithm, noisy spatial gradients can be highly resolved by a large number of mobile particles, and this incurs negligible computational cost, as compared to expensive chemistry calculations.

54 ENVIRONMENTAL SCIENCES↗

Surface engineering of polymeric membranes with metal oxides for improved fouling resistance and superior oil-water separation

Rapid industrial growth has increased the need for efficient membranes to separate oil-water emulsions. Polyvinylidene fluoride (PVDF) membranes, although widely used due to their chemical inertness and favorable mechanical properties, suffer from fouling due to their intrinsic hydrophobicity. Modifying these membranes after fabrication offers a practical solution as it easily fits into existing large-scale manufacturing processes. Atomic layer deposition (ALD), an atomically-precise vapor phase surface modification technique, can create ultrathin metal oxide layers that greatly improve membrane hydrophilicity without significantly affecting the original pore size. However, PVDF's lack of reactive chemical moieties makes ALD challenging. Here, in this study, we present a simple alkali treatment that greatly enhances ALD nucleation and growth on PVDF membranes. This treatment imparts exceptional oil-water emulsion separation capabilities and antifouling behavior in PVDF membranes after just a few ALD cycles, surpassing the performance of PVDF membranes coated with hundreds of ALD cycles. This dramatic reduction in the number of ALD cycles required could enable cost-effective modification of commercial PVDF membranes at scale using spatial, roll-to-roll ALD. These modified membranes outperform reported modified PVDF membranes, with >99 % permeance recovery and <1 % irreversible loss of permeance and >98 % oil rejection from oil-water emulsions over 100 h continuous operation, making them promising for advanced water purification technologies.

Atomic layer deposition↗

Time-resolved unsteady shock motion and spectral analysis of a hypersonic shock-wave/boundary-layer interaction over a double-cone

Direct experimental measurements of double-cone shock-wave/boundary-layer interaction dynamics at Mach 7.2 were collected using a schlieren-based shock-detection algorithm at the University of Texas at San Antonio. The points of flow separation and reattachment were spatially and spectrally evaluated at 300 kHz for time-resolved characterization of the unsteadiness of this flow field. The separation bubble was found to rapidly expand and contract about a large mean length. Spectral analysis of the velocity fluctuations of this motion revealed this behavior to be a turbulent phenomenon, with spectral energy trends consistent with the Kolmogorov theory for the turbulent energy cascade.

Glasby, Ryan [ORNL]↗

Entanglement properties of disordered quantum spin chains with long-range antiferromagnetic interactions

Entanglement measures are useful tools in characterizing otherwise unknown quantum phases and indicating transitions between them. Here we examine the concurrence and entanglement entropy in quantum spin chains with random long-range couplings, spatially decaying with a power-law exponent $\textit{α}$. Using the strong disorder renormalization group (SDRG) technique, we find by analytical solution of the master equation a strong disorder fixed point, characterized by a fixed point distribution of the couplings with a finite dynamical exponent, which describes the system consistently in the regime $α > \frac{1}{2}$ . A numerical implementation of the SDRG method yields a power-law spatial decay of the average concurrence, which is also confirmed by exact numerical diagonalization. However, we find that the lowest-order SDRG approach is not sufficient to obtain the typical value of the concurrence. We therefore implement a correction scheme which allows us to obtain the leading-order corrections to the random singlet state. This approach yields a power-law spatial decay of the typical value of the concurrence, which we derive both by a numerical implementation of the corrections and by analytics. Next, using numerical SDRG, the entanglement entropy (EE) is found to be logarithmically enhanced for all $\textit{α}$, corresponding to a critical behavior with an effective central charge $\textit{c}$ = ln(2), independent of $\textit{α}$. This is confirmed by an analytical derivation. Using numerical exact diagonalization (ED), we confirm the logarithmic enhancement of the EE and a weak dependence on $\textit{α}$. For a wide range of partition size $\textit{l}$, the EE fits a critical behavior with a central charge close to $\textit{c}$ = 1, which is the same as for the clean Haldane-Shastry model with a power-law-decaying interaction with $\textit{α}$ = 2. Only for small $l \ll L$, in a range which increases with the number of spins N, we find deviations which are rather consistent with the strong disorder fixed point central charge $\textit{c}$ = ln(2). Furthermore, we find using ED that the concurrence shows power-law decay, albeit with smaller power exponents than obtained by SDRG. Finally, we also present results obtained with DMRG and find agreement with ED for sufficiently small $\textit{α}$ < 2, whereas for larger $\textit{α}$ DMRG tends to underestimate the entanglement entropy and finds a faster decaying concurrence.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗