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Results for “spatial transferability”

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 793 records · Page 44

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NASA NTRS1998

Satellite-Based Investigations of the Transition from an Oceanic to Continental Transform Margin

Detailed characterization of neotectonics evolution of the Valle de San Felipe and Arroyo Grande regions in northern Baja California. Reoccupied GEOMEX GPS sites, and occupied a regional GPS (Global Positioning System) network. The Baja California peninsula in Mexico offers a unique setting for studying the kinematic evolution of a complex, active strike-slip/rift plate boundary. We are currently conducting remote sensing, geologic, and geodetic studies of this boundary. The combined data sets will yield instantaneous and time integrated views of its evolution. This proposal solicits renewed funding from NASA to support remote sensing and geologic studies. During the late Cenozoic, Baja California has been the locus of changing fault geometry that has accommodated components of the relative motion between the North America and Pacific plates. Contemporary slip between the two plates occurs in a broad zone that encompasses much of southern California and the Baja California Peninsula. The transfer of slip across this zone in southern California is relatively well understood. South of the border, the geometry and role of specific faults and structural provinces in transferring plate margin deformation across the peninsula is enigmatic. Results We use Landsat Thematic Mapper imagery of the Baja California Peninsula to identify recent and active faults, and then conduct field studies that characterize the temporal and spatial structural evolution of the plate margin. These data address questions concerning the neotectonic development of the Gulf of California, the Baja California Peninsula, and their role in evolution of the post-Miocene Pacific - North American plate boundary. Moreover, these studies provide constraints on the geometry of active faults, allowing more exact understanding of the results of ongoing NASA-supported geodetic experiments. In addition, anticipated publication of the TM scenes will provide a widely available geological data base for relatively little-known peninsula California. Achievements include development of an ArcInfo data base of Landsat and SPOT imagery, detailed field studies of Neogene structures in northeastern Baja California, and new constraint on Pacific - North America plate motion at Baja California latitudes. These results are reported in maps, manuscripts and data products which are published or near completion.

Miller, M. Meghan↗

NASA NTRS2022

A SUBLIME 3D Model for Cometary Coma Emission: The Hypervolatile-rich Comet C/2016 R2 (PanSTARRS)

The coma of comet C/2016 R2 (PanSTARRS) is one of the most chemically peculiar ever observed, in particular due to its extremely high CO/H2O and N(+2)/H2O ratios, and unusual trace volatile abundances. However, the complex shape of its CO emission lines, as well as uncertainties in the coma structure and excitation, has lead to ambiguities in the total CO production rate. We performed high-resolution, spatially, spectrally, and temporally resolved CO observations using the James Clerk Maxwell Telescope and Submillimeter Array to elucidate the outgassing behavior of C/2016 R2. Results are analyzed using a new, time-dependent, three-dimensional radiative transfer code (SUBlimating gases in LIME; SUBLIME, based on the open-source version of the LIne Modeling Engine), incorporating for the first time, accurate state-to-state collisional rate coefficients for the CO–CO system. The total CO production rate was found to be in the range of (3.8 − 7.6) × 10^(28) per s between 2018 January 13 and February 1 (at r(H) = 2.8–2.9 au), with a mean value of (5.3 ± 0.6) × 10^(28) per s. The emission is concentrated in a near-sunward jet, with a half-opening angle of ∼62° and an outflow velocity of 0.51 ± 0.01 km/s, compared to 0.25 ± 0.01 km/s in the ambient (and nightside) coma. Evidence was also found for an extended source of CO emission, possibly due to icy grain sublimation around 1.2 × 10^(5) km from the nucleus. Based on the coma molecular abundances, we propose that the nucleus ices of C/2016 R2 can be divided into a rapidly sublimating apolar phase, rich in CO, CO2, N2, and CH3OH, and a predominantly frozen (or less abundant), polar phase containing more H2O, CH4, H2CO, and HCN.

Martin Andrew Cordiner↗

DOE OSTI2024

Learning the boundary-to-domain mapping using Lifting Product Fourier Neural Operators for partial differential equations

Neural operators such as the Fourier Neural Operator (FNO) have been shown to provide resolution-independent deep learning models that can learn mappings between function spaces. For example, an initial condition can be mapped to the solution of a partial differential equation (PDE) at a future time-step using a neural operator. Despite the popularity of neural operators, their use to predict solution functions over a domain given only data over the boundary (such as a spatially varying Dirichlet boundary condition) remains unexplored. In this paper, we refer to such problems as boundary-to-domain problems; they have a wide range of applications in areas such as fluid mechanics, solid mechanics, heat transfer etc. We present a novel FNO-based architecture, named Lifting Product FNO (or LP-FNO) which can map arbitrary boundary functions defined on the lower-dimensional boundary to a solution in the entire domain. Specifically, two FNOs defined on the lower-dimensional boundary are lifted into the higher dimensional domain using our proposed lifting product layer. We demonstrate the efficacy and resolution independence of the proposed LP-FNO for the 2D Poisson equation.

Kashi, Aditya↗

DOE OSTI2023

DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-Shot Transfer the Dynamic Response of Networked Systems

This article develops a deep graph operator network (DeepGraphONet) framework that learns to approximate the dynamics of a complex system (e.g., the power grid or traffic) with an underlying subgraph structure. Here, we build our DeepGraphONet by fusing the ability of graph neural networks to exploit spatially correlated graph information and deep operator networks to approximate the solution operator of dynamical systems. The resulting DeepGraphONet can then predict the dynamics within a given short/medium-term time horizon by observing a finite history of the graph state information. Furthermore, we design our DeepGraphONet to be resolution independent. That is, we do not require the finite history to be collected at the exact/same resolution. In addition, to disseminate the results from a trained DeepGraphONet, we design a zero-shot learning strategy that enables using it on a different subgraph. Finally, empirical results on the transient stability prediction problem of power grids and traffic flow forecasting problem of a vehicular system illustrate the effectiveness of the proposed DeepGraphONet.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2012

Can Spatially Unresolved 3D Mixtures of Clouds & Aerosols Be Disentangled Using Multi-angle Polarized Radiance Observations?

No abstract available

vector radiative transfer↗

DOE OSTI2022

Applying Infrared Thermography as a Method for Online Monitoring of Turbine Blade Coolant Flow

As gas turbine engine manufacturers strive to implement condition-based operation and maintenance, there is a need for blade monitoring strategies capable of early fault detection and root-cause determination. Given the importance of blade cooling flows to turbine blade health and longevity, there is a distinct lack of methodologies for coolant flowrate monitoring. The present study addresses this identified opportunity by applying an infrared thermography system on an engine-representative research turbine to generate data-driven models for prediction of blade coolant flowrate. Thermal images were used as inputs to a linear regression and regularization algorithm to relate blade surface temperature distribution with blade coolant flowrate. Additionally, this study investigates how coolant flowrate prediction accuracy is influenced by the number and breadth of diagnostic measurements. Here, the results of this study indicate that a source of high-fidelity training data can be used to predict blade coolant flowrate within about six percent error. Furthermore, identification of prioritized sensor placement supports application of this technique across multiple sensor technologies capable of measuring blade surface temperature in operating gas turbine engines, including spatially resolved and point-based measurement techniques.

42 ENGINEERING↗

DOE OSTI2022

Complex pH-Dependent Interactions between Weak Polyelectrolyte Block Copolymer Micelles and Molecular Fluorophores

Amphiphilic block copolymers with weak polyelectrolyte blocks can assemble stimulus-responsive nanostructures and interfaces. Applications of these materials in drug delivery, biomimetics, and sensing largely rely on the well-understood swelling of polyelectrolyte chains upon deprotonation, often induced by changes in pH or ionic strength. This deprotonation can also tune interfacial interactions between the polyelectrolyte blocks and surrounding solution, an effect which is less studied than morphological swelling of polyelectrolytes but can be just as critical for intended function. Here, we investigate whether the pH-driven morphological response of polyelectrolyte-bearing nanostructures also affects the interactions of these nanostructures with molecules in solution, using micelles of a short-chain polybutadiene-block-poly(acrylic acid) (pBd–pAA) as a model system. Here we introduce a Förster resonance energy transfer (FRET) approach to probe interactions between micelles and fluorescent molecular solutes as a function of solution pH. As expected, the pAA corona of these pBd–pAA micelles increases in thickness monotonically as a function of pH. However, FRET efficiency, which provides a metric of the spatial proximity of fluorescently labeled micelles and freely diffusing fluorophores, exhibits complex nonmonotonic behavior as a function of pH, indicating that the average separation of micelles and acceptor fluorophores is not strictly correlated with micelle swelling. Dialysis experiments quantify the affinity of fluorophores for micelles as a function of pH, confirming that changes in FRET are driven almost entirely by the pH-dependent affinity of the pAA block for the investigated molecular fluorophores, not simply by a shape change of the pAA corona. This study provides key insights into the interfacial interactions between weak-polyelectrolyte-bearing nanostructures and molecular solutes, of importance for the development of their stimulus-responsive applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Modeling mesoscale fission gas behavior in UO2 by directly coupling the phase field method to spatially resolved cluster dynamics

Abstract Fission gas release within uranium dioxide nuclear fuel occurs as gas atoms diffuse through grains and arrive at grain boundary (GB) bubbles; these GB bubbles grow and interconnect with grain edge bubbles; and grain edge tunnels grow and connect to free surfaces. In this study, a hybrid multi-scale/multi-physics simulation approach is presented to investigate these mechanisms of fission gas release at the mesoscale. In this approach, fission gas production, diffusion, clustering to form intragranular bubbles, and re-solution within grains are included using spatially resolved cluster dynamics in the Xolotl code. GB migration and intergranular bubble growth and coalescence are included using the phase field method in the MARMOT code. This hybrid model couples Xolotl to MARMOT using the MultiApp and Transfer systems in the MOOSE framework, with Xolotl passing the arrival rate of gas atoms at GBs and intergranular bubble surfaces to MARMOT and MARMOT passing evolved GBs and bubble surface positions to Xolotl. The coupled approach performs well on the two-dimensional simulations performed in this work, producing similar results to the standard phase field model when Xolotl does not include fission gas clustering or re-solution. The hybrid model performs well computationally, with a negligible cost of coupling Xolotl and MARMOT and good parallel scalability. The hybrid model predicts that intragranular fission gas clustering and bubble formation results in up to 70% of the fission gas being trapped within grains, causing the increase in the intergranular bubble fraction to slow by a factor of six. Re-solution has a small impact on the fission gas behavior at 1800 K but it has a much larger impact at 1000 K, resulting in a twenty-times increase in the concentration of single gas atoms within grains. Due to the low diffusion rate, this increase in mobile gas atoms only results in a small acceleration in the growth of the intergranular bubble fraction. Finally, the hybrid model accounts for migrating GBs sweeping up gas atoms. This results in faster intergranular bubble growth with smaller initial grain sizes, since the additional GB migration results in more immobile gas clusters reaching GBs.

Kim, Dong-Uk↗

NASA NTRS1984

Parametric and Nonparametric Analysis of LANDSAT TM and MSS Imagery for Detecting Submerged Plant Communities

The spatial, spectral and radiometric characteristics of LANDSAT TM and MSS imagery for detecting submerged aquatic vegetation are assessed. The problem is approached from two perspectives; purely stochastic or nonparametric in a radiative sense and theoretical in which radiative transfer equations are used to predict upwelling radiance at satellite altitude. The spectral and radiometric aspects of the theoretical approach are addressed with which a submerged plant canopy is distinguished from a surrounding bottom of sand or mud.

Ackleson, S. G.↗

NASA NTRS1998

Comparison of measurements by the NASA/GSFC scanning raman lidar and the DOE/ARM CART raman lidar

Latent heat transfer through evaporation and condensation of water vapor is the most important energy transport mechanism in the atmosphere. In addition, water vapor is the most active greenhouse gas. Any global warming scenario must take accurate account of the spatial and temporal variation of water vapor in order to account for both of these effects. Due to the great importance of water vapor in atmospheric radiation studies, specific intensive operations periods (IOPs) have been hosted by the Department of Energy's Atmospheric Radiation Measurements (ARM) program. One of the goals of these IOPs has been to determine the quality of and explain any discrepancies among a wide variety of water vapor measuring instruments. Raman lidar systems developed by NASA/Goddard Space Flight Center and DOE/Sandia National Laboratories have participated in the two Water Vapor IOPs (WVIOPs) held at the Southern Great Plains (SGP) Cloud and Radiation Testbed Site (CART) site during 1996 (WVIOP1) and 1997 (WVIOP2). Detailed comparisons of these two systems is ongoing but this effort has already resulted in numerous improvements in design and data analysis for both lidar systems.

Whiteman, David↗

NASA NTRS1998

Monte Carlo Calculations of Polarized Microwave Radiation Emerging from Cloud Structures

The last decade has seen tremendous growth in cloud dynamical and microphysical models that are able to simulate storms and storm systems with very high spatial resolution, typically of the order of a few kilometers. The fairly realistic distributions of cloud and hydrometeor properties that these models generate has in turn led to a renewed interest in the three-dimensional microwave radiative transfer modeling needed to understand the effect of cloud and rainfall inhomogeneities upon microwave observations. Monte Carlo methods, and particularly backwards Monte Carlo methods have shown themselves to be very desirable due to the quick convergence of the solutions. Unfortunately, backwards Monte Carlo methods are not well suited to treat polarized radiation. This study reviews the existing Monte Carlo methods and presents a new polarized Monte Carlo radiative transfer code. The code is based on a forward scheme but uses aliasing techniques to keep the computational requirements equivalent to the backwards solution. Radiative transfer computations have been performed using a microphysical-dynamical cloud model and the results are presented together with the algorithm description.

Kummerow, Christian↗

NASA NTRS2019

HelioSwarm: Swarm Mission Design in High Altitude Orbit for Heliophysics

Resolving the complex three-dimensional turbulent structures that characterize the solar wind requires contemporaneous spatially and temporally distributed measurements. HelioSwarm is a mission concept that will deploy multiple, co-orbiting satellites to use the solar wind as a natural laboratory for understanding the fundamental, universal process of plasma turbulence. The HelioSwarm transfer trajectory and science orbit use a lunar gravity assist to deliver the ESPA-class nodes attached to a large data transfer hub to a P/2 lunar resonant orbit. Once deployed in the science orbit, the free-flying, propulsive nodes use simple Cartesian relative motion patterns to establish baseline separations both along and across the solar wind flow direction.

lunar resonant orbit↗

NASA NTRS2019

HelioSwarm: Swarm Mission Design in High Altitude Orbit for Heliophysics

lunar resonant orbit↗

NASA NTRSDate not supplied

Microbial Communities in Microgravity: Simulation in Lab and on the Computer

Microorganisms grow differently in spaceflight than they do on Earth. While much remains unexplained about how microgravity affects microbial growth, one dominant hypothesis is that the lack of density-driven convection in the liquid growth environment makes mixing diffusion-limited, and therefore slower. This is supported by evidence that individual microbial strains experience starvation and acid stress in microgravity. However, if it is true, then microgravity would also have measurable effects on microbes in mixed communities, because many interspecies interactions involve the exchange of soluble metabolites through the medium (cross-feeding). Specifically, cooperative cross-feeding communities would grow more slowly in microgravity, and cooperation would be less stable on evolutionary timescales. Here we describe our efforts to test this hypothesis by simulating microgravity in silico and in the lab, using a model system of Eschericia coli and Salmonella enterica that grow only when they can exchange methionine and acetate. We created CAMDLES (CFD-DEM Artificial Microgravity Developments for Living Ecosystem Simulation) as an extension of CFDEM®coupling software, to carry out computational modeling of biological flows, growth, and mass transfer in microgravity and also in laboratory artificial microgravity devices (rotating wall vessels, RWV). Using CAMDLES, we found distinct differences in growth rates between RWV and true microgravity, and we were able to identify several features, such as spatial distribution, biofilm formation, and product yield parameters, that influence the degree to which RWV growth recapitulates microgravity growth. In addition, we report on the development of a laboratory system for monitoring growth rates and species ratios of the community in RWVs, using fluorescent strains. Pairing CAMDLES with the laboratory model system allows us to generate quantitative predictions about the effects of spaceflight on organisms that will be essential to sustaining human space exploration in the long term.

microbiology↗

NASA NTRS2024

Time-dependent Photoionization Modeling of Warm Absorbers: High-resolution Spectra and Response to Flaring Light Curves

Time-dependent photoionization modeling of warm absorber (WA) outflows in active galactic nuclei can play an important role in understanding the interaction between WAs and the central black hole. The WA may be out of the equilibrium state because of the variable nature of the central continuum. In this paper, with the help of time dependent photoionization modeling, we study how the WA gas properties change with time and how it reacts to changing radiation fields. Incorporating a flaring incident light curve, we investigate the behavior of WAs using a photoionization code that simultaneously and consistently solves the time-dependent equations of level population, heating and cooling, and radiative transfer. We simulate the physical processes in the gas clouds, such as ionization, recombination, heating, cooling, and the transfer of ionizing radiation through the cloud and present high-resolution time-resolved absorption spectra. We demonstrate that time-dependent radiative transfer is important and the calculations that omit this effect quantitatively and systematically underestimate the absorption. Time-dependent photoionization models provide crucial insights into the characteristics of WAs and can be used to constrain their density and spatial distribution.

Astrophysicists↗

NASA NTRS2024

Time-Dependent Photoionization Modeling of Warm Absorbers: High-Resolution Spectra and Response to Flaring Light Curves

Dev R. Sadaula↗

DOE OSTI2021

Machine Learning Analysis of Hydrologic Exchange Flows and Transit Time Distributions in a Large Regulated River

Hydrologic exchange between river channels and adjacent subsurface environments is a key process that influences water quality and ecosystem function in river corridors. High-resolution numerical models were often used to resolve the spatial and temporal variations of exchange flows, which are computationally expensive. In this study, we adopt Random Forest (RF) and Extreme Gradient Boosting (XGB) approaches for deriving reduced order models of hydrologic exchange flows and associated transit time distributions, with integrated field observations (e.g., bathymetry) and hydrodynamic simulation data (e.g., river velocity, depth). The setup allows an improved understanding of the influences of various physical, spatial, and temporal factors on the hydrologic exchange flows and transit times. The predictors also contain those derived using hybrid clustering, leveraging our previous work on river corridor system hydromorphic classification. The machine learning-based predictive models are developed and validated along the Columbia River Corridor, and the results show that the top parameters are the thickness of the top geological formation layer, the flow regime, river velocity, and river depth; the RF and XGB models can achieve 70% to 80% accuracy and therefore are effective alternatives to the computational demanding numerical models of exchange flows and transit time distributions. Each machine learning model with its favorable configuration and setup have been evaluated. The transferability of the models to other river reaches and larger scales, which mostly depends on data availability, is also discussed.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Optical Waveguiding Charge-Transfer Cocrystals: Examining the Impact of Molecular Rotations on Their Photoluminescence

Here, we present the first example of a binary optical waveguiding (OWG) cocrystal with large anisotropy featuring a fluorinated acceptor molecule (CPP-TFPN, 1) with on-plane rotational dynamics, confirmed by solid-state NMR ( 19 F T 1 ) and theoretical calculations. Spatially resolved microphotoluminescence and variable-temperature photoluminescence experiments allowed us to examine the OWG performance and photophysical properties of both single crystals and bulk microcrystalline samples. A comparison with an analogous cocrystal containing a regioisomeric acceptor (CPP-TFTN, 2) revealed that the photoluminescence characteristics of 1 are associated with the rotational motions of the acceptor, offering insights into how the molecular motion changes this property.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗