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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Introducing Enabling Computational Tools to the Climate Sciences: Multi-Resolution Climate Modeling with Adaptive Cubed-Sphere Grids

The research investigates and advances strategies how to bridge the scale discrepancies between local, regional and global phenomena in climate models without the prohibitive computational costs of global cloud-resolving simulations. In particular, the research explores new frontiers in computational geoscience by introducing high-order Adaptive Mesh Refinement (AMR) techniques into climate research. AMR and statically-adapted variable-resolution approaches represent an emerging trend for atmospheric models and are likely to become the new norm in future-generation weather and climate models. The research advances the understanding of multi-scale interactions in the climate system and showcases a pathway how to model these interactions effectively with advanced computational tools, like the Chombo AMR library developed at the Lawrence Berkeley National Laboratory. The research is interdisciplinary and combines applied mathematics, scientific computing and the atmospheric sciences. In this research project, a hierarchy of high-order atmospheric models on cubed-sphere computational grids have been developed that serve as an algorithmic prototype for the finite-volume solution-adaptive Chombo-AMR approach. The foci of the investigations have lied on the characteristics of both static mesh adaptations and dynamically-adaptive grids that can capture flow fields of interest like tropical cyclones. Six research themes have been chosen. These are (1) the introduction of adaptive mesh refinement techniques into the climate sciences, (2) advanced algorithms for nonhydrostatic atmospheric dynamical cores, (3) an assessment of the interplay between resolved-scale dynamical motions and subgrid-scale physical parameterizations, (4) evaluation techniques for atmospheric model hierarchies, (5) the comparison of AMR refinement strategies and (6) tropical cyclone studies with a focus on multi-scale interactions and variable-resolution modeling. The results of this research project demonstrate significant advances in all six research areas. The major conclusions are that statically-adaptive variable-resolution modeling is currently becoming mature in the climate sciences, and that AMR holds outstanding promise for future-generation weather and climate models on high-performance computing architectures.

58 GEOSCIENCES↗
DOE OSTI2019

MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles

Optimal engine operation during a transient driving cycle is the key to achieving greater fuel economy, engine efficiency, and reduced emissions. In order to achieve continuously optimal engine operation, engine calibration methods use a combination of static correlations obtained from dynamometer tests for steady-state operating points and road and/or track performance data. As the parameter space of control variables, design variable constraints, and objective functions increases, the cost and duration for optimal calibration become prohibitively large. In order to reduce the number of dynamometer tests required for calibrating modern engines, a large-scale simulation-driven machine learning approach is presented in this work. A parallel, fast, robust, physics-based reduced-order engine simulator is used to obtain performance and emission characteristics of engines over a wide range of control parameters under various transient driving conditions (drive cycles). We scale the simulation up to 3,906 nodes of the Theta supercomputer at the Argonne Leadership Computing Facility to generate data required to train a machine learning model. The trained model is then used to predict various engine parameters of interest, and the results are compared with those predicted by the engine simulator. Our results show that a deepneural-network-based surrogate model achieves high accuracy: Pearson product-moment correlation values larger than 0.99 and mean absolute percentage error within 1.07% for various engine parameters such as exhaust temperature, exhaust pressure, nitric oxide, and engine torque. Once trained, the deep-neural-network-based surrogate model is fast for inference: it requires about 16 mu s for predicting the engine performance and emissions for a single design configuration compared with about 0.5 s per configuration with the engine simulator. Moreover, we demonstrate that transfer learning and retraining can be leveraged to incrementally retrain the surrogate model to cope with new configurations that fall outside the training data space.

deep learning↗
DOE OSTI2014

Radial velocity curves of ellipsoidal red giant binaries in the Large Magellanic Cloud

Ellipsoidal red giant binaries are close binary systems where an unseen, relatively close companion distorts the red giant, leading to light variations as the red giant moves around its orbit. These binaries are likely to be the immediate evolutionary precursors of close binary planetary nebula and post-asymptotic giant branch and post-red giant branch stars. Due to the MACHO and OGLE photometric monitoring projects, the light variability nature of these ellipsoidal variables has been well studied. However, due to the lack of radial velocity curves, the nature of their masses, separations, and other orbital details has so far remained largely unknown. In order to improve this situation, we have carried out spectral monitoring observations of a large sample of 80 ellipsoidal variables in the Large Magellanic Cloud and we have derived radial velocity curves. At least 12 radial velocity points with good quality were obtained for most of the ellipsoidal variables. The radial velocity data are provided with this paper. Combining the photometric and radial velocity data, we present some statistical results related to the binary properties of these ellipsoidal variables.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Benchmarking the repeatability of a pneumatic cyclic neutron activation analysis facility using 16 O(n,p) 16 N for nuclear forensics

A target was prepared for cyclic neutron activation analysis by heat sealing lithium-carbonate in polyethylene. The target was cyclically irradiated 50 times using a Thermo-Scientific accelerator based deuterium–tritium fusion neutron generator. During counting periods, gamma-rays emitted by 16 N were detected using three high-purity germanium detectors acquiring data in list-mode. Total counts acquired in each spectrum were compared between the three detectors to examine variability in geometric positioning of the target and variability of the generator intensity throughout the experiment. These two effects were determined to be the primary sources of variation in the measured counts. Variation in target positioning and generator intensity were found to increase the standard deviation by 34% and 33%, respectively. Transit times to the detector were found to be slower and more variable than transit to the generator but were well below the half second threshold needed to measure short-lived radionuclides with half-lives on the order of seconds. The standard deviation in irradiation time was found to be less than 1 milliseconds. The impact on statistical variability in the measured counts was negligible relative to the two primary sources of variation. Spectra acquired from each cycle were summed together. The sum of the peak areas from the 6.1 MeV gamma-ray and its corresponding single and double escape peaks were used to measure the half-life of 16N. The result of 7.108(15) seconds derived from data suggests that the currently published value of 7.13(2) seconds has minimal systematic bias induced by background.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

A surrogate-model-based approach for estimating the first and second-order moments of offshore wind power

Power curve, the functional relationship that governs the process of converting a set of weather variables experienced by a wind turbine into electric power, is widely used in the wind industry to estimate power output for planning and operational purposes. Existing methods for power curve estimation have three main limitations: (i) they mostly rely on wind speed as the sole input, thus ignoring the secondary, yet possibly significant effects of other environmental factors, (ii) they largely overlook the complex marine environment in which offshore turbines operate, potentially compromising their value in offshore wind energy applications, and (ii) they solely focus on the first-order properties of wind power, with little (or null) information about the variation around the mean behavior, which is important for ensuring reliable grid integration, asset health monitoring, and energy storage, among others. In light of that, this study investigates the impact of several wind-and wave-related factors on offshore wind power variability, with the ultimate goal of accurately predicting its first two moments. Further, our approach couples OpenFAST—a multi-physics wind turbine simulator—with Gaussian Process (GP) regression to reveal the underlying relationships governing offshore weather-to-power conversion. We first find that a multi-input power curve which captures the combined impact of wind speed, direction, and air density, can provide double-digit improvements, in terms of prediction accuracy, relative to univariate methods which rely on wind speed as the sole explanatory variable (e.g. the standard method of bins). Wave-related variables are found not important for predicting the average power output, but interestingly, appear to be extremely relevant in describing the fluctuation of the offshore power around its mean. Tested on real-world data collected at the New York/New Jersey bight, our proposed multi-input models demonstrate a high explanatory power in predicting the first two moments of offshore wind generation, testifying their potential value to the offshore wind industry.

17 WIND ENERGY↗
DOE OSTI2015

Contactless Determination of Electrical Conductivity of One-Dimensional Nanomaterials by Solution-Based Electro-orientation Spectroscopy

For nanowires of the same composition, and even fabricated within the same batch, often exhibit electrical conductivities that can vary by orders of magnitude. Unfortunately, existing electrical characterization methods are time-consuming, making the statistical survey of highly variable samples essentially impractical. Here, we demonstrate a contactless, solution-based method to efficiently measure the electrical conductivity of 1D nanomaterials based on their transient alignment behavior in ac electric fields of different frequencies. In comparison with direct transport measurements by probe-based scanning tunneling microscopy shows that electro-orientation spectroscopy can quantitatively measure nanowire conductivity over a 5-order-of-magnitude range, 10–5–1 Ω–1 m–1 (corresponding to resistivities in the range 102–107 Ω·cm). With this method, we statistically characterize the conductivity of a variety of nanowires and find significant variability in silicon nanowires grown by metal-assisted chemical etching from the same wafer. We also find that the active carrier concentration of n-type silicon nanowires is greatly reduced by surface traps and that surface passivation increases the effective conductivity by an order of magnitude. Moreover, this simple method makes electrical characterization of insulating and semiconducting 1D nanomaterials far more efficient and accessible to more researchers than current approaches. Electro-orientation spectroscopy also has the potential to be integrated with other solution-based methods for the high-throughput sorting and manipulation of 1D nanomaterials for postgrowth device assembly.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Shape-Preserved Transformation of Biological Cells into Synthetic Hydrogel Microparticles

The synthesis of materials that can mimic the mechanical, and ultimately functional, properties of biological cells can broadly impact the development of biomimetic materials, as well as engineered tissues and therapeutics. Yet, it is challenging to synthesize, for example, microparticles that share both the anisotropic shapes and the elastic properties of living cells. Here in paper, a cell-directed route to replicate cellular structures into synthetic hydrogels such as polyethylene glycol (PEG) is described. First, the internal and external surfaces of chemically fixed cells are replicated in a conformal layer of silica using a sol–gel process. The template is subsequently removed to render shape-preserved, mesoporous silica replicas. Infiltration and cross-linking of PEG precursors and dissolution of the silica result in a soft hydrogel replica of the cellular template as demonstrated using erythrocytes, HeLa, and neuronal cultured cells. The elastic modulus can be tuned over an order of magnitude (≈10–100 kPa) though with a high degree of variability. Furthermore, synthesis without removing the biotemplate results in stimuli-responsive particles that swell/deswell in response to environmental cues. Overall, this work provides a foundation to develop soft particles with nearly limitless architectural complexity derived from dynamic biological templates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Identifying quenched jets in heavy ion collisions with machine learning

Measurements of jet substructure in ultra-relativistic heavy ion collisions suggest that the jet showering process is modified by the interaction with the quark-gluon plasma. Modifications of the hard substructure of jets can be explored with modern data-driven techniques. In this study, a machine learning approach to the identification of quenched jets is designed. Jet showering processes are simulated with a jet quenching model JEWEL and a non-quenching model PYTHIA 8. Sequential substructure variables are extracted from the jet clustering history following an angular-ordered sequence and are used in the training of a neural network built on top of a long short-term memory network. We show that this approach successfully identifies the quenching effect in the presence of the large uncorrelated background of soft particles created in heavy-ion collisions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Bounded motion design in the Earth zonal problem using differential algebra based normal form methods

Establishing long-term relative bounded motion between orbits in perturbed dynamics is a key challenge in astrodynamics to enable cluster flight with minimum propellant expenditure. Herein, we present an approach that allows for the design of long-term relative bounded motion considering a zonal gravitational model. Entire sets of orbits are obtained via high-order Taylor expansions of Poincarè return maps about reference fixed points. The high-order normal form algorithm is used to determine a change in expansion variables of the map into normal form space, in which the phase space behavior is circular and can be easily parameterized by action–angle coordinates. The action–angle representation of the normal form coordinates is then used to parameterize the original Poincarè return map and average it over a full phase space revolution by a path integral along the angle parameterization. As a result, the averaged nodal period and drift in the ascending node are obtained, for which the bounded motion conditions are straightforwardly imposed. Sets of highly accurate bounded orbits are obtained, extending over several thousand kilometers, and valid for decades.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

On the Solvability of a Boundary-Value Problem for a Second-Order Singular Quasilinear Equation

We obtain solvability conditions for a two-point boundary-value problem for a second-order quasilinear equation. The equation is singular with respect to the independent variable. The result is based on the properties of the Green operator of the corresponding linear problem. In particular, we prove its boundedness and obtain an upper estimate of its norm. Conditions of existence of a solution of the original problem are obtained from the solvability condition of an auxiliary operator equation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A splitting Chebyshev collocation method for Schrödinger–Poisson system

We develop a splitting Chebyshev collocation (SCC) method for the time-dependent Schrödinger–Poisson (SP) system arising from theoretical analysis of quantum plasmas. By means of splitting technique in time, the time-dependant SP system is first reduced to uncoupled Schrödinger and Poisson equations at every time step. The space variables in Schrödinger and Poisson equations are next represented by high-order Chebyshev polynomials, and the resulting system are discretized by the spectral collocation method. Finally, matrix diagonalization technique is applied to solve the fully discretized system in one dimension, two dimensions and three dimensions, respectively. The newly proposed method not only achieves spectral accuracy in space but also reduces the computer-memory requirements and the computational time in comparison with conventional solver. Numerical results confirm the spectral accuracy and efficiency of this method, and indicate that the SCC method could be an efficient alternative method for simulating the dynamics of quantum plasmas.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Acceleration of diffusive molecular dynamics simulations through mean field approximation and subcycling time integration

Highlights: • Two numerical methods are presented to accelerate DMD simulations. • The methods greatly reduce the computation time without much loss of accuracy. • The accelerated DMD model is capable of simulating long-term mass transport at atomistic length scales. • Demonstrated for the propagation of an atomically sharp phase boundary over 30 seconds. - Abstract: Diffusive Molecular Dynamics (DMD) is a class of recently developed computational models for the simulation of long-term diffusive mass transport at atomistic length scales. Compared to previous atomistic models, e.g., transition state theory based accelerated molecular dynamics, DMD allows the use of larger time-step sizes, but has a higher computational complexity at each time-step due to the need to solve a nonlinear optimization problem at every time-step. This paper presents two numerical methods to accelerate DMD based simulations. First, we show that when a many-body potential function, e.g., embedded atom method (EAM), is employed, the cost of DMD is dominated by the computation of the mean of the potential function and its derivatives, which are high-dimensional random variables. To reduce the cost, we explore both first- and second-order mean field approximations. Specifically, we show that the first-order approximation, which uses a point estimate to calculate the mean, can reduce the cost by two to three orders of magnitude, but may introduce relatively large error in the solution. We show that adding an approximate second-order correction term can significantly reduce the error without much increase in computational cost. Second, we show that DMD can be significantly accelerated through subcycling time integration, as the cost of integrating the empirical diffusion equation is much lower than that of the optimization solver. To assess the DMD model and the numerical approximation methods, we present two groups of numerical experiments that simulate the diffusion of hydrogen in palladium nanoparticles. In particular, we show that the computational framework is capable of capturing the propagation of an atomically sharp phase boundary over a time window of more than 30 seconds. The effects of the proposed numerical methods on solution accuracy and computation time are also assessed quantitatively.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A general metriplectic framework with application to dissipative extended magnetohydrodynamics

General equations for conservative yet dissipative (entropy producing) extended magnetohydrodynamics are derived from two-fluid theory. Keeping all terms generates unusual cross-effects, such as thermophoresis and a current viscosity that mixes with the usual velocity viscosity. While the Poisson bracket of the ideal version of this model has already been discovered, we determine its metriplectic counterpart that describes the dissipation. This is done using a new and general thermodynamic point of view to derive dissipative brackets, a means of derivation that is natural for understanding and creating dissipative dynamics without appealing to underlying kinetic theory orderings. Finally, the formalism is used to study dissipation in the Lagrangian variable picture where, in the context of extended magnetohydrodynamics, non-local dissipative brackets naturally emerge.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Understanding Electron Transport in Disk-Shaped Triphenylene-Tris(naphthaleneimidazole)s through Structural Modification and Theoretical Investigation

We report that disk-shaped molecules with large aromatic π-surfaces are a class of organic semiconductors in which the charge-carrier transport properties could be greatly facilitated by preferred intermolecular stacking of the π-surfaces. The optical and electronic properties are not only determined by the core aromatic structure of these disk-shaped molecules but are also strongly dependent on the side chains, which directly impact the molecular self-assembly behavior in condensed phases. Triphenylene-tris(naphthaleneimidazole) (TP-TNI) is a recently reported n-type semiconductor featuring a large π-core and branched side chains, with an electron-transporting mobility reaching 10 -4 cm 2 V -1 s -1 . To further improve material performance, a detailed study is needed to understand the dependence of carrier transport properties on both the core electronic structure and side chain. Here, we present the detailed synthesis and characterization of a TP-TNI derivative bearing linear side chains, which has demonstrated a field-effect electron-transport mobility of up to 1.3 × 10 -3 cm 2 V -1 s -1 . The more than 1 order improvement in electron-transport properties over the branched side chain homologue can be correlated to ordered twisted packing in the thin film, as revealed by in situ variable temperature grazing incidence wide-angle X-ray scattering studies. In conclusion, in-depth theoretical understanding of the frontier orbitals, reorganization energies, and charge-transfer integrals of TP-TNI molecules has provided further insight into the relationship between the molecular stacking geometry and charge-transport properties.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Mesoscale Phase-Field Modeling of Charge Transport in Nanocomposite Electrodes for Lithium-ion Batteries

A phase-field model is developed to investigate the influence of microstructure, thermodynamic and kinetic properties, and charging conditions on charged particle transport in nanocomposite electrodes. Two sets of field variables are used to describe the microstructure. One is comprised of the order parameters describing size, orientation and spatial distributions of nanoparticles, and the other is comprised of the concentrations of mobile species. A porous nanoparticle microstructure filled with electrolyte is taken as a model system to test the phase-field model. Inhomogeneous and anisotropic dielectric constants and mobilities of charged particles, and stresses associated with lattice deformation due to Li-ion insertion/extraction are considered in the model. Iteration methods are used to find the elastic and electric fields in an elastically and electrically inhomogeneous medium. The results demonstrate that the model is capable of predicting charge separation associated with the formation of a double layer at the electrochemical interface between solid and electrolyte, and the effect of microstructure, inhomogeneous and anisotropic thermodynamic and kinetic properties, charge rates, and stresses on voltage versus current density and capacity during charging and discharging.

Hu, Shenyang Y↗
DOE OSTI2016

Electrochemical activity of Fe-MIL-100 as a positive electrode for Na-ion batteries

Here we investigate the electrochemical activity of metal-organic frameworks (MOFs) as positive electrodes for Na-ion batteries in coin cell configurations. The performance of Fe-MIL-100 material is highly dependent on the choice of sodium salt source, and electrolyte system. The overall capacity fades over many cycles, however the high Coulombic efficiency is maintained. This can be correlated with inaccessibility of active sites for Na intercalation, due to the increase of extra carbonaceous material inside the pores. High resolution synchrotron powder X-ray and pair distribution function analyses of the as-made and cycled electrodes reveal the structure maintains the long-range order with progressive cycling. This finding suggests that careful consideration of all variables in battery components, and especially electrolyte selection can lead to greatly improved performances.

Sava Gallis, Dorina F.↗
DOE OSTI2015

Magnetohydrodynamic equilibria with incompressible flows: Symmetry approach

We identify and discuss a family of azimuthally symmetric, incompressible, magnetohydrodynamic plasma equilibria with poloidal and toroidal flows in terms of solutions of the Generalized Grad Shafranov (GGS) equation. These solutions are derived by exploiting the incompressibility assumption, in order to rewrite the GGS equation in terms of a different dependent variable, and the continuous Lie symmetry properties of the resulting equation and, in particular, a special type of “weak” symmetries.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Intramolecular hydrogen bonding analysis

The quasi-atomic orbital (QUAO) bonding analysis is used to study intramolecular hydrogen bonding (IMHB) in salicylic acid and an intermediate that is crucial to the synthesis of aspirin. The bonding analysis rigorously explores IMHB through directly accessing information that is intrinsic to the molecular wave function, thereby bypassing the need for intrinsically biased methods. The variables that affect the strength of IMHB are determined using kinetic bond orders, QUAO populations, and QUAO hybridizations. Important properties include both the interatomic distance between hydrogen and oxygen participating in the IMHB and the hybridization on the oxygen. The bonding analysis further shows that each intramolecular hydrogen bond is a four-electron three-center bond. Here, the bonding analysis is used to understand how aromatic reactivity changes due to the effect of functional groups on the aromatic ring.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗