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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Material Characterization of Hierarchical Tunable Pore Size Polymer Foams Used in the MARBLE Mix Morphology Experiment

One of the great challenges of inertial confinement fusion and high energy density experiments is understanding the effects of mix on thermonuclear burn. The MARBLE campaign, conceived at Los Alamos National Laboratory, aims to gather new insights into this issue by utilizing unique target capsules containing polymer foams of variable pore sizes, tunable over an order of magnitude. Such capsules allow the degree of initial heterogeneity to be controlled experimentally for the first time. Here, we describe the various characterization efforts used to gain understanding of the chemical structure and behavior of the foam. Previous experiments were not sensitive to foam physical properties, and the MARBLE platform has aided in the development of techniques to measure foam properties such as deuterium content, density variation, hydrogen adsorption, and pore size and volume distribution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Spherically symmetric Einstein-aether perfect fluid models

We investigate spherically symmetric cosmological models in Einstein-aether theory with a tilted (non-comoving) perfect fluid source. We use a 1+3 frame formalism and adopt the comoving aether gauge to derive the evolution equations, which form a well-posed system of first order partial differential equations in two variables. We then introduce normalized variables. The formalism is particularly well-suited for numerical computations and the study of the qualitative properties of the models, which are also solutions of Horava gravity. We study the local stability of the equilibrium points of the resulting dynamical system corresponding to physically realistic inhomogeneous cosmological models and astrophysical objects with values for the parameters which are consistent with current constraints. In particular, we consider dust models in (β−) normalized variables and derive a reduced (closed) evolution system and we obtain the general evolution equations for the spatially homogeneous Kantowski-Sachs models using appropriate bounded normalized variables. We then analyse these models, with special emphasis on the future asymptotic behaviour for different values of the parameters. Finally, we investigate static models for a mixture of a (necessarily non-tilted) perfect fluid with a barotropic equations of state and a scalar field.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Snowflake: A Lightweight Portable Stencil DSL

Stencil computations are not well optimized by general-purpose production compilers and the increased use of multicore, manycore, and accelerator-based systems makes the optimization problem even more challenging. In this paper we present Snowflake, a Domain Specific Language (DSL) for stencils that uses a 'micro-compiler' approach, i.e., small, focused, domain-specific code generators. The approach is similar to that used in image processing stencils, but Snowflake handles the much more complex stencils that arise in scientific computing, including complex boundary conditions, higher-order operators (larger stencils), higher dimensions, variable coefficients, non-unit-stride iteration spaces, and multiple input or output meshes. Snowflake is embedded in the Python language, allowing it to interoperate with popular scientific tools like SciPy and iPython; it also takes advantage of built-in Python libraries for powerful dependence analysis as part of a just-in-time compiler. We demonstrate the power of the Snowflake language and the micro-compiler approach with a complex scientific benchmark, HPGMG, that exercises the generality of stencil support in Snowflake. By generating OpenMP comparable to, and OpenCL within a factor of 2x of hand-optimized HPGMG, Snowflake demonstrates that a micro-compiler can support diverse processor architectures and is performance-competitive whilst preserving a high-level Python implementation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

ODD (Version 0.X)

The Orthogonal Diffusion Discretizations (ODD) library is a collection of low order (Diffusion, P1, Ray Tracing, and Variable Eddington Factor (VEF)) thermal radiation solvers for orthogonal structured grids driven by an application programing interface (API).

Cleveland, Mathew↗
DOE OSTI2016

Development of hierarchical, tunable pore size polymer foams for ICF targets

In this study, one of the great challenges of inertial confinement fusion experiments is poor understanding of the effects of reactant heterogeneity on fusion reactions. The Marble campaign, conceived at Los Alamos National Laboratory, aims to gather new insights into this issue by utilizing target capsules containing polymer foams of variable pore sizes, tunable over an order of magnitude. Here, we describe recent and ongoing progress in the development of CH and CH/CD polymer foams in support of Marble. Hierarchical and tunable pore sizes have been achieved by utilizing a sacrificial porogen template within an open-celled poly(divinylbenzene) or poly(divinylbenzene-co-styrene) aerogel matrix, resulting in low-density foams (~30 mg/ml) with continuous multimodal pore networks.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Longitudinal, Lateral, Vertical, and Temporal Thermal Heterogeneity in a Large Impounded River: Implications for Cold-Water Refuges

Dam operations can affect mixing of the water column, thereby influencing thermal heterogeneity spatially and temporally. This occurs by restricting or eliminating connectivity in longitudinal, lateral, vertical, and temporal dimensions. We examined thermal heterogeneity across space and time and identified potential cold-water refuges for salmonids in a large impounded river in inland northwestern USA. To describe these patterns, we used thermal infrared (TIR) imagery, in situ thermographs, and high-resolution, 3-D hydraulic mapping. We explained the median water temperature and probability of occurrence of cool-water areas using generalized additive models (GAMs) at reach and subcatchment scales, and we evaluated potential cold-water refuge occurrence in relation to these patterns. We demonstrated that (1) lateral contributions from tributaries dominated thermal heterogeneity, (2) thermal variability at confluences was approximately an order of magnitude greater than of the main stem, (3) potential cold-water refuges were mostly found at confluences, and (4) the probability of occurrence of cool areas and median water temperature were associated with channel geomorphology and distance from dam. These findings highlight the importance of using multiple approaches to describe thermal heterogeneity in large, impounded rivers and the need to incorporate these types of rivers in the understanding of thermal riverscapes because of their limited representation in the literature.

Mejia, Francine H.↗
DOE OSTI2021

Machine Learning for Conservative-to-Primitive in Relativistic Hydrodynamics

The numerical solution of relativistic hydrodynamics equations in conservative form requires root-finding algorithms that invert the conservative-to-primitive variables map. These algorithms employ the equation of state of the fluid and can be computationally demanding for applications involving sophisticated microphysics models, such as those required to calculate accurate gravitational wave signals in numerical relativity simulations of binary neutron stars. This work explores the use of machine learning methods to speed up the recovery of primitives in relativistic hydrodynamics. Artificial neural networks are trained to replace either the interpolations of a tabulated equation of state or directly the conservative-to-primitive map. The application of these neural networks to simple benchmark problems shows that both approaches improve over traditional root finders with tabular equation-of-state and multi-dimensional interpolations. In particular, the neural networks for the conservative-to-primitive map accelerate the variable recovery by more than an order of magnitude over standard methods while maintaining accuracy. Neural networks are thus an interesting option to improve the speed and robustness of relativistic hydrodynamics algorithms.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Kilonovae Across the Nuclear Physics Landscape: The Impact of Nuclear Physics Uncertainties on r-process-powered Emission

Merging neutron stars produce “kilonovae”—electromagnetic transients powered by the decay of unstable nuclei synthesized via rapid neutron capture (the r-process) in material that is gravitationally unbound during inspiral and coalescence. Kilonova emission, if accurately interpreted, can be used to characterize the masses and compositions of merger-driven outflows, helping to resolve a long-standing debate about the origins of r-process material in the Universe. We explore how the uncertain properties of nuclei involved in the r-process complicate the inference of outflow properties from kilonova observations. Using r-process simulations, we show how nuclear physics uncertainties impact predictions of radioactive heating and element synthesis. For a set of models that span a large range in both predicted heating and final abundances, we carry out detailed numerical calculations of decay product thermalization and radiation transport in a kilonova ejecta with a fixed mass and density profile. The light curves associated with our models exhibit great diversity in their luminosities, with peak brightness varying by more than an order of magnitude. We also find variability in the shape of the kilonova light curves and their color, which in some cases runs counter to the expectation that increasing levels of lanthanide and/or actinide enrichment will be correlated with longer, dimmer, redder emission.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Model reduction for a power grid model

We examine the complexity of constructing reduced order models for subsets of the variables needed to represent the state of the power grid. In particular, we apply model reduction techniques to the DeMarco-Zheng power grid model. We show that due to the oscillating nature of the solutions and the absence of timescale separation between resolved and unresolved variables, the construction of accurate reduced models becomes highly non-trivial because one has to account for long memory effects. In addition, we show that a reduced model that includes even a short memory is drastically better than a memoryless model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

MSE based drilling optimization using neural network simulaton

The method disclosed receives a data stream from an MWD system and determines the response of a specific energy (SE) relationship and a rate of penetration (ROP) relationship respectively to variables controllable by the operator, in order to enable operation at a lowest SE, or a highest Rate-of-Penetration (ROP) to SE ratio. The method utilizes artificial neural networks trained by MWD data to deduce a depth-of-cut and torque based on relationships manifesting between the various data points collected, and an SE equation and a predicted ROP is evaluated over a series of probable operating points. The method continuously gathers and analyzes MWD data during the drilling operation and allows an operator to manage the controllable parameters such that operation at the lowest SE or highest ROP or ROP to SE ratio can be achieved during the drilling operation.

Zhang, Wu↗
DOE OSTI2016

NORM Concentration Determination in Common Materials in an Urban Environment

The United States government performs searches for illicit radiation sources before and during large special events, such as State of the Union addresses, presidential inaugurations, or the recent papal visit. Clear detection of sources is difficult due to natural background radiation, which can vary greatly throughout a given search area. Background radiation is mostly a function of the materials used in roadways, sidewalks, and building exteriors. A team of scientists from Oak Ridge National Laboratory (ORNL), Lawrence Livermore National Laboratory (LLNL), the Remote Sensing Laboratory (RSL), and Lawrence Berkeley National Laboratory (LBNL) is exploring the nature and variability of background count rates in order to improve the performance of searches in urban areas. A series of background measurements have been and will continue to be taken at a Military Operations on Urban Terrain (MOUT) environment at the Fort Indiantown Gap (FTIG) National Guard training facility in Pennsylvania. This site replicates a small town with asphalt roads, concrete intersections, buildings, sidewalks, curbs, and gravel parking areas. Unlike an actual urban area, traffic and pedestrian access can be easily controlled to avoid interference with measurements. This paper documents the efforts focused on characterizing the various concentrations of radioactive material found in these materials at the site. The NORM concentrations determined from this work will be incorporated in to a large-scale simulation (a 'virtual test-bed') that can be used to investigate various search techniques and algorithms, detector suite configurations, and to produce representative synthetic data for future work. A combination of measurement and simulation was used to determine the concentration of {sup 40}K and the radioactive nuclides in the uranium series and the thorium series in several materials at the FTIG MOUT site. The predicted concentrations are consistent with other published results, and they matched favorably with synthetic data generated with MCNP6 models using the determined NORM concentrations and estimates of the FTIG environment as described by available construction plans and using standard material assumptions. Future measurements will incorporate lessons learned from this study. More measurements will be made of the concrete intersections at the site. The methodology explained in this report will also be carried out on building materials in an effort to characterize NORM concentrations in these background sources. Other modeling and simulation work that should be conducted includes a robust sensitivity analysis in which the effects of small changes to the model (and measurement) system can be characterized to help bound the uncertainty on the final NORM concentration predictions. (authors)

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

A parallel-in-time algorithm for variable step multistep methods

This paper presents a multigrid reduction in time (MGRIT) algorithm for achieving time parallelism using multistep backward difference formula (BDF) methods on variably-spaced temporal grids. This MGRIT approach transforms the linear multistep methods into single step methods applied to groups of time steps. Stability considerations are addressed through lowering of the order on coarse grids. The methods are presented for fixed and variable time step formulations. Furthermore, numerical results show moderate speedups for the heat equation as well as two IEEE power grid test problems characterized by nonlinear differential algebraic equations (DAE) of index 1. The performance of MGRIT with BDF is compared to MGRIT with Runge-Kutta for both the fixed and variable step methods of the same order.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Arrhenius Rate: constant volume burn

A constant volume burn occurs for an idealized initial state in which a large volume of reactants at rest is suddenly raised to a high temperature and begins to burn. Due to the uniform spatial state, there is no fluid motion and no heat conduction. This reduces the time evolu tion to an ODE for the reaction progress variable. With an Arrhenius reaction rate, two characteristics of thermal ignition are illustrated: induction time and thermal runaway. The Frank-Kamenetskii approximation then leads to a simple expression for the adiabatic induction time. For a first order reaction, the analytic solution is derived and used to illustrate the effect of varying the activation temperature; in particular, on the induction time. In general, the ODE can be solved numerically. This is used to illustrate the effect of varying the reaction order. We note that for a first order reaction, the time evolution of the reaction progress variable has an exponential tail. In contrast, for a reaction order less than one, the reaction completes in a nite time. The reaction order also affects the induction time.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Informative and non-informative decomposition of turbulent flow fields

Not all the information in a turbulent field is relevant for understanding particular regions or variables in the flow. Here, we present a method for decomposing a source field into its informative Φ I (x, t) and residual Φ R (x, t) components relative to another target field. The method is referred to as informative and non-informative decomposition (IND). All the necessary information for physical understanding, reduced-order modelling and control of the target variable is contained in Φ I (x, t), whereas Φ R (x, t) offers no substantial utility in these contexts. The decomposition is formulated as an optimisation problem that seeks to maximise the time-lagged mutual information of the informative component with the target variable while minimising the mutual information with the residual component. The method is applied to extract the informative and residual components of the velocity field in a turbulent channel flow, using the wall shear stress as the target variable. We demonstrate the utility of IND in three scenarios: (i) physical insight into the effect of the velocity fluctuations on the wall shear stress; (ii) prediction of the wall shear stress using velocities far from the wall; and (iii) development of control strategies for drag reduction in a turbulent channel flow using opposition control. In case (i), IND reveals that the informative velocity related to wall shear stress consists of wall-attached high- and low-velocity streaks, collocated with regions of vertical motions and weak spanwise velocity. This informative structure is embedded within a larger-scale streak–roll structure of residual velocity, which bears no information about the wall shear stress. In case (ii), the best-performing model for predicting wall shear stress is a convolutional neural network that uses the informative component of the velocity as input, while the residual velocity component provides no predictive capabilities. Finally, in case (iii), we demonstrate that the informative component of the wall-normal velocity is closely linked to the observability of the target variable and holds the essential information needed to develop successful control strategies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Quantifying the effect of nighttime interactions between roots and canopy physiology and their control of water and carbon cycling on feedbacks between soil moisture and terrestrial climatology under variable environmental conditions

The primary objective of this project is to characterize and quantify how the temporal variability of hydraulic redistribution (HR) and its physiological regulation in unmanaged and complex forests is affecting current water and carbon exchange and predict how future climate scenarios will affect these relationships and potentially feed back to the climate. Specifically, a detailed study of ecosystem water uptake and carbon exchange in relation to root functioning was proposed in order to quantify the mechanisms controlling temporal variability of soil moisture dynamic and HR in three active AmeriFlux sites, and to use published data of two other inactive AmeriFlux sites. Furthermore, data collected by our research group at the Duke Free Air CO2 enrichment (FACE) site was also being utilized to further improve our ability to forecast future environmental impacts of elevated CO2 concentration on soil moisture dynamic and its effect on carbon sequestration and terrestrial climatology. The overarching objective being to forecast, using a soil:plant:atmosphere model coupled with a biosphere:atmosphere model, the impact of root functioning on land surface climatology. By comparing unmanaged sites to plantations, we also proposed to determine the effect of land use change on terrestrial carbon sequestration and climatology through its effect on soil moisture dynamic and HR. Our simulations of HR by roots indicated that in some systems HR is an important mechanism that buffers soil water deficit, affects energy and carbon cycling; thus having significant implications for seasonal climate. HR maintained roots alive and below 70% loss of conductivity and our simulations also showed that the increased vapor pressure deficit at night under future conditions was sufficient to drive significant nighttime transpiration at all sites, which reduced HR. This predicted reduction in HR under future climate conditions played an important regulatory role in land atmosphere interactions by affecting whole ecosystem carbon and water balance. Under future climatic scenarios, HR was reduced thus affecting negatively plant water use and carbon assimilation. The discrepancy between the predicted and actual surface warming and atmospheric water vapor caused by the persistence of evapotranspiration during the dry season, increasing energy transfer in the form of latent heat. Under those simulations, we also evaluated how the hydraulic properties of soil and xylem limited the rate of carbon uptake, and carbon net ecosystem exchange. The multilayered hydraulically driven soil vegetation atmosphere carbon and water transfer model was designed to represent processes common to vascular plants, so that ecosystem atmosphere exchange could be captured by the same processes at different sites. Those models shown to be well suited for investigating the impact of drought on forest ecosystems because of its explicit treatment of water transport to leaves. This modeling work also confirmed that unmanaged, mixed hardwood site are more resilient to climatic variations than an adjacent pine plantation, but that future climatic conditions will reverse this trends.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A regression algorithm for accelerated lattice QCD that exploits sparse inference on the D-Wave quantum annealer

We propose a regression algorithm that utilizes a learned dictionary optimized for sparse inference on a D-Wave quantum annealer. In this regression algorithm, we concatenate the independent and dependent variables as a combined vector, and encode the high-order correlations between them into a dictionary optimized for sparse reconstruction. On a test dataset, the dependent variable is initialized to its average value and then a sparse reconstruction of the combined vector is obtained in which the dependent variable is typically shifted closer to its true value, as in a standard inpainting or denoising task. Here, a quantum annealer, which can presumably exploit a fully entangled initial state to better explore the complex energy landscape, is used to solve the highly non-convex sparse coding optimization problem. The regression algorithm is demonstrated for a lattice quantum chromodynamics simulation data using a D-Wave 2000Q quantum annealer and good prediction performance is achieved. The regression test is performed using six different values for the number of fully connected logical qubits, between 20 and 64. The scaling results indicate that a larger number of qubits gives better prediction accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Advanced Characterization of Fuel Pellet and Cladding from the HB Robinson NPP

In order to predict the behavior and response of materials to extreme environments of a nuclear reactor, comprehensive models have been developed over the past several decades. These models are typically validated against carefully designed single or double variable experiments which may be systematically altered in order build up a model of behavior under the conditions being examined. In reality, this does not necessarily compare well with the real application situations whereby the behavior of the material under irradiation is determined by multiple variables changing simultaneously e.g. temperature, irradiation flux and fluence, and pressure. This is particularly so when considering the highly dynamic environment of a fuel assembly within a nuclear power plant (NPP) core. This work aims to provide near-atomic resolution microstructural and chemical data of a fuel/clad specimen that has been used in service within a commercial NPP. Atom probe tomography and S/TEM characterization of the cladding showed the presence of small scale clusters containing Fe and Cr, along with segregation of these minor solutes to dislocations. SEM examination of the fuel pellet showed relatively large fission gas bubbles that had burst due to the metallographic polishing process to prepare the sample for examination. SEM-EDX mapping revealed the presence of Xr bubbles located beneath the surface that still retained their Xe concentration. Metallic precipitates were also observed. S/TEM-EDX of the fuel material also revealed the presence of small metallic precipitates. In addition, small Xe bubbles were also found to be present in the material, lying along grain boundaries only. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Effects of root litter traits on soil organic matter dynamics depend on decay stage and root branching order

Decaying roots contribute disproportionately to soil organic carbon (SOC) stocks in many ecosystems, yet few studies have examined the afterlife effects of root trait variability on the formation, stability and mineralization of SOC. In order to assess how root functional class (absorptive vs. transport) and mycorrhizal association (ectomycorrhizal vs. arbuscular mycorrhizal fungi) affect both formation and loss of SOC, we decayed lower-order absorptive (orders 1–2) and higher-order transport (orders 3–4) roots from six arbuscular mycorrhizal (AM) and six ectomycorrhizal (EcM) associated hardwood tree species in an isotopically distinct soil under laboratory conditions. We found that root litter effects on SOC differed significantly among root functional classes and mycorrhizal types. AM absorptive and transport roots (high in nitrogen and soluble compounds) contributed more to the formation of new mineral-associated organic carbon (MAOC), but also contributed to higher mineralization of native SOC (via priming effects). Moreover, absorptive AM roots formed MAOC gradually and rates saturated, whereas ECM roots formed MAOC more quickly, despite slower decay (via greater formation efficiency). Altogether, these findings demonstrate how different combinations of root traits might contribute to soil C balance, and that failure to account for the chemical and morphological heterogeneity of roots, as well their interactions with microbes over time, may lead to incorrect projections of SOC stocks and turnover.

59 BASIC BIOLOGICAL SCIENCES↗