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DOE OSTI2015

Groundwater Discharge along a Channelized Coastal Plain Stream

In the Coastal Plain of the southeastern USA, streams have commonly been artificially channelized for flood control and agricultural drainage. However, groundwater discharge along such streams has received relatively little attention. Using a combination of stream- and spring-flow measurements, spring temperature measurements, temperature profiling along the stream-bed, and geologic mapping, we delineated zones of diffuse and focused discharge along Little Bayou Creek, a channelized, first-order perennial stream in western Kentucky. Seasonal variability in groundwater discharge mimics hydraulic-head fluctuations in a nearby monitoring well and spring-discharge fluctuations elsewhere in the region, and is likely to reflect seasonal variability in recharge. Diffuse discharge occurs where the stream is incised into the semi-confined regional gravel aquifer, which is comprised of the Mounds Gravel. Focused discharge occurs upstream where the channel appears to have intersected preferential pathways within the confining unit. Seasonal fluctuations in discharge from individual springs are repressed where piping results in bank collapse. Thereby, focused discharge can contribute to the morphological evolution of the stream channel.

58 GEOSCIENCES↗

DOE OSTI2016

Hamiltonian analysis for linearly acceleration-dependent Lagrangians

We study the constrained Ostrogradski-Hamilton framework for the equations of motion provided by mechanical systems described by second-order derivative actions with a linear dependence in the accelerations. We stress out the peculiar features provided by the surface terms arising for this type of theories and we discuss some important properties for this kind of actions in order to pave the way for the construction of a well defined quantum counterpart by means of canonical methods. In particular, we analyse in detail the constraint structure for these theories and its relation to the inherent conserved quantities where the associated energies together with a Noether charge may be identified. The constraint structure is fully analyzed without the introduction of auxiliary variables, as proposed in recent works involving higher order Lagrangians. Finally, we also provide some examples where our approach is explicitly applied and emphasize the way in which our original arrangement results in propitious for the Hamiltonian formulation of covariant field theories.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2016

Relativistic perturbations in ΛCDM: Eulerian and Lagrangian approaches

We study the relativistic dynamics of a pressure-less and irrotational fluid of dark matter (CDM) with a cosmological constant (Λ), up to second order in cosmological perturbation theory. In our analysis we also account for vector and tensor perturbations and include primordial non-Gaussianity. We consider three gauges: the synchronous-comoving gauge, the Poisson gauge and the total matter gauge, where the first is the unique relativistic Lagrangian frame of reference, and the latters are convenient gauge choices for Eulerian frames. Our starting point is the metric and fluid variables in the Poisson gauge up to second order. We then perform the gauge transformations to the synchronous-comoving gauge and subsequently to the total matter gauge. Our expressions for the metrics, densities, velocities, and the gauge generators are novel and coincide with known results in the limit of a vanishing cosmological constant.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Numerical integrators for Lagrangian oceanography

Abstract. A common task in Lagrangian oceanography is to calculate a large number of drifter trajectories from a velocity field precalculated with an ocean model. Mathematically, this is simply numerical integration of an ordinary differential equation (ODE), for which a wide range of different methods exist. However, the discrete nature of the modelled ocean currents requires interpolation of the velocity field in both space and time, and the choice of interpolation scheme has implications for the accuracy and efficiency of the different numerical ODE methods. We investigate trajectory calculation in modelled ocean currents with 800 m, 4 km, and 20 km horizontal resolution, in combination with linear, cubic and quintic spline interpolation. We use fixed-step Runge–Kutta integrators of orders 1–4, as well as three variable-step Runge–Kutta methods (Bogacki–Shampine 3(2), Dormand–Prince 5(4) and 8(7)). Additionally, we design and test modified special-purpose variants of the three variable-step integrators, which are better able to handle discontinuous derivatives in an interpolated velocity field. Our results show that the optimal choice of ODE integrator depends on the resolution of the ocean model, the degree of interpolation, and the desired accuracy. For cubic interpolation, the commonly used Dormand–Prince 5(4) is rarely the most efficient choice. We find that in many cases, our special-purpose integrators can improve accuracy by many orders of magnitude over their standard counterparts, with no increase in computational effort. Equivalently, the special-purpose integrators can provide the same accuracy as standard methods at a reduced computational cost. The best results are seen for coarser resolutions (4 and 20 km), thus the special-purpose integrators are particularly advantageous for research using regional to global ocean models to compute large numbers of trajectories. Our results are also applicable to trajectory computations on data from atmospheric models.

58 GEOSCIENCES↗

DOE OSTI2015

Characteristics of temperature rise in variable inductor employing magnetorheological fluid driven by a high-frequency pulsed voltage source

A variable inductor with magnetorheological (MR) fluid has been successfully applied to power electronics applications; however, its thermal characteristics have not been investigated. To evaluate the performance of the variable inductor with respect to temperature, we measured the characteristics of temperature rise and developed a numerical analysis technique. The characteristics of temperature rise were determined experimentally and verified numerically by adopting a multiphysics analysis technique. In order to accurately estimate the temperature distribution in a variable inductor with an MR fluid-gap, the thermal solver should import the heat source from the electromagnetic solver to solve the eddy current problem. To improve accuracy, the B–H curves of the MR fluid under operating temperature were obtained using the magnetic property measurement system. In addition, the Steinmetz equation was applied to evaluate the core loss in a ferrite core. The predicted temperature rise for a variable inductor showed good agreement with the experimental data and the developed numerical technique can be employed to design a variable inductor with a high-frequency pulsed voltage source.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Conservative numerical schemes with optimal dispersive wave relations: Part I. Derivation and analysis

An energy-conserving and an energy-and-enstrophy conserving numerical schemes are derived by approximating the Hamiltonian formulation of the inviscid shallow water flows based on the vorticity-divergence variables. These schemes also conserve the first-order moments such as mass and vorticity, as usual. The conservative properties of the schemes stem from the skew-symmetry and singularities of the Poisson brackets, which are carefully retained in the discrete approximations. Here, the schemes operate on unstructured orthogonal dual meshes, over bounded or unbounded domains, and they are also shown to possess the same optimal dispersive wave relations as those of the Z-grid scheme, which is a consequence of the use of the vorticity and divergence variables.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Magnetic and Transport Properties of Bi{sub 0.5−x}Pr{sub x}Ca{sub 0.5}MnO{sub 3}(0.0 ≤ x ≤ 0.50) Manganites

In this work, the effect of Pr doping at the Bi site on the critical behavior and magnetocaloric and transport properties of Bi{sub 0.5−x}Pr{sub x}Ca{sub 0.5}MnO{sub 3} (0.0 ≤ x ≤ 0.50) manganite synthesized by sol–gel method is reported. An increase in Curie temperature (T{sub C}) with an increase in Pr content is observed. The samples with x = 0.0–0.40 show first-order phase transition whereas the sample with x = 0.50 shows second-order phase transition near T{sub C}. The estimated values of critical exponents β = 0.75 and γ = 0.82 for the sample with x = 0.50 do not fall into any universality class. The sample with x = 0.50 shows maximum magnetic entropy change (|ΔS{sub m}| = (1.22 J kg{sup −1} K{sup −1}) at T{sub C} in an applied magnetic field of 40 kOe, which is associated with magnetocaloric effect (MCE). All the samples show semiconducting behavior and also follow Mott’s variable range hopping (VRH) model below the charge ordering temperature (T{sub CO}). The changes in magnetic and transport properties are correlated with the changes in the structural parameters of the samples. The results are discussed in view of the effect of Pr doping on the 6s{sup 2} lone pair character of Bi{sup 3+} ions and disorder in the samples.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Outflowing photoionized plasma in Circinus X-1 using the high-resolution X-ray spectrometer Resolve onboard XRISM and the radiative transfer code cloudy

High-resolution X-ray spectroscopy is a key to understanding the mass inflow and outflow of compact objects. Spectral lines carry information about the ionization, density, and velocity structures through their intensity ratios and profiles. They are formed in non-local thermodynamic equilibrium conditions under the intense radiation field from the compact objects; thus radiative transfer (RT) calculation is a requisite for proper interpretations. We present such a study for a low-mass X-ray binary, Circinus X-1, from which the P Cygni profile was discovered using the X-ray grating spectrometer onboard Chandra. We observed the source using the X-ray microcalorimeter onboard XRISM at an orbital phase of 0.93–0.97 and revealed many spectral features unidentified before: the higher series transitions ($n{\rightarrow }1$; $n>2$) of highly ionized (H- and He-like) S, Ca, Ar, and Fe in emission and absorption, the Fe K$\alpha$ and K$\beta$ inner-shell excitation absorption of mildly ionized (O- to Li-like) Fe, and resolved fine-structure level transitions in the Fe Ly$\alpha$ and He$\alpha$ complexes. They blend with each other at different velocity shifts on top of apparently variable continuum emission that changed its flux by an order of magnitude within a 70 ks telescope time. Despite such complexity in the observed spectra, most of them can be explained by a simple model consisting of the photoionized plasma outflowing at $\sim$300 km s$^{-1}$ and the variable blocking material in the line of sight of the incident continuum emission from the accretion disk. We demonstrate this with the aid of the RT code cloudy for the line ratio diagnostics and spectral fitting. We further constrain the physical parameters of the outflow and argue that the outflow is launched close to the outer edge of the accretion disk and can be driven radiatively by being assisted by the line force calculated using the RT simulation.

X-rays: binaries↗

DOE OSTI2018

Voltage control of unidirectional anisotropy in ferromagnet-multiferroic system

Demonstration of ultralow energy switching mechanisms is imperative for continued improvements in computing devices. Ferroelectric (FE) and multiferroic (MF) order and their manipulation promise an ideal combination of state variables to reach attojoule range for logic and memory (i.e., ~30× lower switching energy than nanoelectronics). In BiFeO 3 (BFO), the coupling between the antiferromagnetic (AFM) and FE order is robust at room temperature, scalable in voltage, stabilized by the FE order, and can be integrated into a fabrication process for a beyond-CMOS (complementary metal-oxide semiconductor) era. The presence of the AFM order and a canted magnetic moment in this system causes exchange interaction with a ferromagnet such as Co 0.9 Fe 0.1 or La 0.7 Sr 0.3 MnO 3 . Previous research has shown that exchange coupling (uniaxial anisotropy) can be controlled with an electric field. However, voltage modulation of unidirectional anisotropy, which is preferred for logic and memory technologies, has not yet been demonstrated. Here, we present evidence for electric field control of exchange bias of laterally scaled spin valves that is exchange coupled to BFO at room temperature. We show that the exchange bias in this bilayer is robust, electrically controlled, and reversible. We anticipate that magnetoelectricity at these scaled dimensions provides a powerful pathway for computing beyond modern nanoelectronics by enabling a new class of nonvolatile, ultralow energy computing elements.

36 MATERIALS SCIENCE↗

DOE OSTI2023

CPViz: Visualizing clinical pathways represented in higher-order networks

To improve clinical care practice, it is important to understand the variability of clinical pathways executed in different contexts (e.g., pathways in different geographical locations, demographics, and phenotypic groups). A common way of representing clinical pathways is through network-based representations that capture trajectories of treatment steps. However, first-order networks, which are based on the Markovian property and the de facto standard model to represent transitions between steps, often fail to capture real trajectories. This paper introduces a visual analytic tool to explore and compare pathways represented in higher-order networks. Because each higher node in the network is a subtrajectory (i.e., partial or full history of treatment steps), the tool can display true sequences of treatment steps and compute the similarity of the two networks in a space of higher-order nodes. The tool also highlights areas in which the two networks are similar and dissimilar and how a certain subtrajectory is realized differently in different pathways. The paper demonstrates the tool's usefulness by applying it to multiple antidepressant pharmacotherapy pathways for veterans diagnosed with major depressive disorder and by illustrating heterogeneity in prescription patterns across pathways.

Chae, Junghoon↗

DOE OSTI2024

In situ aerobic methane oxidation rates in a stratified lake

Abstract Microbial aerobic methane oxidation is an important sink for aquatic methane worldwide. Despite its importance to global methane fluxes, few aerobic methane oxidation rates have been obtained in freshwater or marine environments without imposing changes to the microbial community through use of ex situ methods. A novel in situ incubation method for continuous time‐series measurements was used in Jordan Lake, North Carolina, during 2020–2021, to determine reaction kinetics for aerobic methane oxidation rates across a wide range of naturally varying methane (55–1833 nM) and dissolved oxygen (DO; 28–366 μM) concentrations and temperatures (17–30°C). Methane oxidation began immediately at the start of each of 21 incubations and methane oxidation rates were 1 st order with respect to methane. The data density allowed for accurate calculation of 1 st ‐order rate constants,k, that ranged from 0.018 to 0.462 h −1 (R 2 > 0.967). Addition of ammonium (20–45 μM) to natural concentrations ranging from 0.057 to 2.4 μM did not change aerobic methane oxidation rate kinetics, suggesting that the natural population of aerobic methane oxidizers in this eutrophic lake was not nitrogen limited. Values ofkinversely correlated most strongly with initial DO concentrations (R 2 = 0.82) rather than temperature. Values forkincreased with Julian day throughout our sampling period, suggesting seasonal influences on methane oxidation via responses to geochemical changes or shifts in microbial community abundance and composition. These experiments demonstrate a high variability in the enzymatic capacity for 1 st ‐order methane oxidation rates in this eutrophic lake that is tightly and inversely coupled to oxygen concentrations. Measurements of in situ aerobic methane oxidation rate constants allow for the direct quantification and modeling of the microbial community's capacity for methane oxidation over a wide range of natural methane concentrations.

Marine & Freshwater Biology↗

DOE OSTI2018

Integrated Energy Planning with a High Share of Variable Renewable Energy Sources for a Caribbean Island

Although it can be complex to integrate variable renewable energy sources such as wind power and photovoltaics into an energy system, the potential benefits are large, as it can help reduce fuel imports, balance the trade, and mitigate the negative impacts in terms of climate change. In order to try to integrate a very large share of variable renewable energy sources into the energy system, an integrated energy planning approach was used, including ice storage in the cooling sector, a smart charging option in the transport sector, and an excess capacity of reverse osmosis technology that was utilised in order to provide flexibility to the energy system. A unit commitment and economic dispatch tool (PLEXOS) was used, and the model was run with both 5 min and 1 h time resolutions. The case study was carried out for a typical Caribbean island nation, based on data derived from measured data from Aruba. The results showed that 78.1% of the final electricity demand in 2020 was met by variable renewable energy sources, having 1.0% of curtailed energy in the energy system. The total economic cost of the modelled energy system was similar to the current energy system, dominated by the fossil fuel imports. Here, the results are relevant for many populated islands and island nations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2016

Characterized Variability of Biomass Feedstocks using The Idaho National Lab Bioenergy Feedstock Library

In order to achieve the Bioenergy Technology Office Goals of acquiring a billion tons of feedstock material that meets the physical, chemical, and cost requirements for conversion to fuel by 2030, research into understanding the overall causes of compositional, physical and conversion variability for the wide number of available biomass feedstock resources is necessary. One strategy for accomplishing this is the innovative knowledge-based management system currently being developed and maintained at Idaho National Laboratory (INL) known as the Bioenergy Feedstock Library (www.bioenergylibrary.inl.gov). The library currently has harvest, storage and logistic, analytical characterization, and/or conversion data for over 35,000 samples spanning 90 different feedstock types, 38 states, and 3 countries. These samples and data have been provided not only by the INL but from over 100 collaborating universities, feedstock supplier, national labs, and industrial partners. Of the 35,000 samples, 2300 have been characterized by one or more analytical processes including the compositional, proximate, ultimate, elemental ash, physical and conversion analyses. This presentation will demonstrate the significant research into the national understanding of biomass feedstock variability made possible by the continually updated contributions to the library that have historically only been available in the few published review papers regarding feedstock variability. Also, the benefits of contributing to this database, the analytical tools available, and the future prospects that this tool can provide research supporting sustainable and renewable energy will be presented.

09 BIOMASS FUELS↗

DOE OSTI2023

DRIPS: A framework for dimension reduction and interpolation in parameter space

Reduced-order models are often used to describe the behavior of complex systems, whose simulation with a full model is too expensive, or to extract salient features from the full model’s output. We introduce a new model-reduction framework DRIPS (dimension reduction and interpolation in parameter space) that combines the offline local model reduction with the online parameter interpolation of reduced-order bases (ROBs). The offline step of this framework relies on dynamic mode decomposition (DMD) to build a low-rank linear surrogate model, equipped with a local ROB, for quantities of interest derived from the training data generated by repeatedly solving the (nonlinear) high-fidelity model for multiple parameter points. The online step consists of the construction of a parametric reduced-order model for each target/test point in the parameter space, with the interpolation of ROBs done on a Grassman manifold and the interpolation of reduced-order operators done on a matrix manifold. The DMD component enables DRIPS to model (typically low-dimensional) quantities of interest directly, without having to access the (typically high-dimensional and possibly nonlinear) operators in a high-fidelity model that governs the dynamics of the underlying high-dimensional state variables, as required in projection-based reduced-order modeling. A series of numerical experiments suggests that DRIPS yields a model reduction, which is computationally more efficient than the commonly used projection-based proper orthogonal decomposition; it does so without requiring a prior knowledge of the governing equation for quantities of interest. Furthermore, for the nonlinear systems considered, DRIPS is more accurate than Gaussian-process interpolation (Kriging).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

Deep multiscale model learning

The objective of this article is to design novel multi-layer neural networks for multiscale simulations of flows taking into account the observed fine data and physical modeling concepts. Our approaches use deep learning techniques combined with local multiscale model reduction methodologies to predict flow dynamics. Using reduced-order model concepts is important for constructing robust deep learning architectures since the reduced-order models provide fewer degrees of freedom. We consider flow dynamics in porous media as multi-layer networks in this work. More precisely, the solution (e.g., pressures and saturation) at the time instant n+1 depends on the solution at the time instant n and input parameters, such as permeability fields, forcing terms, and initial conditions. One can regard the solution as a multi-layer network, where each layer, in general, is a nonlinear forward map and the number of layers relates to the internal time steps. We will rely on rigorous model reduction concepts to define unknowns and connections between layers. It is critical to use reduced-order models for this purpose, which will identify the regions of influence and the appropriate number of variables. Furthermore, due to the lack of available observed fine data, the reduced-order model can provide us sufficient inexpensive data as needed. The designed deep neural network will be trained using both coarse simulation data which is obtained from the reduced-order model and observed fine data. We will present the main ingredients of our approach and numerical examples. Numerical results show that using deep learning with data generated from multiscale models as well as available observed fine data, we can obtain an improved forward map which can better approximate the fine scale model.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Resistance controllability and variability improvement in a TaO{sub x}-based resistive memory for multilevel storage application

In order to obtain reliable multilevel cell (MLC) characteristics, resistance controllability between the different resistance levels is required especially in resistive random access memory (RRAM), which is prone to resistance variability mainly due to its intrinsic random nature of defect generation and filament formation. In this study, we have thoroughly investigated the multilevel resistance variability in a TaO{sub x}-based nanoscale (<30 nm) RRAM operated in MLC mode. It is found that the resistance variability not only depends on the conductive filament size but also is a strong function of oxygen vacancy concentration in it. Based on the gained insights through experimental observations and simulation, it is suggested that forming thinner but denser conductive filament may greatly improve the temporal resistance variability even at low operation current despite the inherent stochastic nature of resistance switching process.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

Natural variability and antioxidant properties of commercially cultivated switchgrass extractives

Optimum utilization of lignocellulosic bioenergy feedstock, such as switchgrass, via recovery of value-added phytochemicals and reduction of waste streams will increase the economic feasibility as well as the sustainability of integrated biorefineries. Switchgrass contains significant amount of overlooked non-structural components, i.e. extractives, which encompasses phenolic derivatives with potential antimicrobial and antioxidant properties. As a perennial crop, it is essential to assess the natural variability of switchgrass extractives content and composition in order to determine the feasibility of its extraction and utilization. In this study, three switchgrass cultivars were harvested from four commercial farms in their second and third growing seasons. Their respective extractive fractions were characterized for the presence of free sugars, total phenolics (TPC), hydroxycinnamic acid (HCAs) derivatives, total flavonols, and other important phytochemicals. Biomass harvested in the third growing season displayed greater amounts of TPC, HCAs, and total flavonols. Environmental conditions such as total precipitation, and cultural practices including harvest time, played important roles in the accumulation of TPC. As a result, the switchgrass extractives also exhibited DPPH (2,2-diphenyl-1-picrylhydrazyl) radical scavenging activity with a positive correlation between inhibiting concentration (IC 50 ) value and total flavonols content.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

An artificial neural network framework for reduced order modeling of transient flows

Here, this paper proposes a supervised machine learning framework for the non-intrusive model order reduction of unsteady fluid flows to provide accurate predictions of non-stationary state variables when the control parameter values vary. Our approach utilizes a training process from full-order scale direct numerical simulation data projected on proper orthogonal decomposition (POD) modes to achieve an artificial neural network (ANN) model with reduced memory requirements. This data-driven ANN framework allows for a nonlinear time evolution of the modal coefficients without performing a Galerkin projection. Our POD-ANN framework can thus be considered an equation-free approach for latent space dynamics evolution of nonlinear transient systems and can be applied to a wide range of physical and engineering applications. Within this framework we introduce two architectures, namely sequential network (SN) and residual network (RN), to train the trajectory of modal coefficients. We perform a systematic analysis of the performance of the proposed reduced order modeling approaches on prediction of a nonlinear wave-propagation problem governed by the viscous Burgers equation, a simplified prototype setting for transient flows. We find that the POD-ANN-RN yields stable and accurate results for test problems assessed both within inside and outside of the database range and performs significantly better than the standard intrusive Galerkin projection model. Finally, our results show that the proposed framework provides a non-intrusive alternative to the evolution of transient physics in a POD basis spanned space, and can be used as a robust predictive model order reduction tool for nonlinear dynamical systems.

42 ENGINEERING↗