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At least 649 records · Page 36

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Method for printing low-density polymer structures

A method of forming a low-density three-dimensional article is provided. The method includes printing a low-density composition on a substrate to form at least one layer comprising the low-density composition. The low-density composition includes a (P) polymer component and (M) a microsphere component in a ratio by volume (P):(M). The method also includes selectively controlling a density of the low-density composition during printing to give the at least one layer on the substrate. Selectively controlling the density of the low-density composition includes varying the ratio (P):(M) during printing. The method further includes repeating the printing and selectively controlling the density of the low-density composition to form additional layer(s), thereby forming the low-density three-dimensional article. A low-density three-dimensional article prepared in accordance with the method is also provided.

Kunc, Vlastimil↗

DOE OSTI2021

Anti-fouling chiral surfaces for membrane filtration and methods therefor

The present disclosure relates to, inter alia, a modified surface comprising an optically active monomer, a polymeric material having a surface onto which the optically active monomer is covalently bound. In one aspect, a membrane comprising an optically active monomer, a poly(aryl sulfone) membrane having a surface onto which the optically active monomer is covalently bound. The present disclosure also relates to a method of modifying a surface, the method comprising applying sufficient energy to a surface to induce covalent bonding with an optically active monomer, and contacting the optically active monomer with the surface. In one aspect, a method of modifying a surface of a poly(aryl sulfone) membrane is disclosed. In another aspect, a method of synthesizing an optically active monomer is disclosed. In one aspect, a method of filtration of chiral compounds is disclosed.

Belfort, Georges↗

DOE OSTI2022

Methods of producing cobalt nanoparticles and hollow gold nanospheres and kits for practicing same

Provided are methods of producing cobalt nanoparticles (Co NPs). The methods include combining a cobalt salt, a capping agent, and a reducing agent, under Co NP synthesis conditions including a temperature selected to produce cobalt nanoparticles of a pre-selected diameter, where the temperature and pre-selected diameter are inversely related. In certain aspects, the methods further include producing hollow gold nano spheres (HGNs) using the cobalt nanoparticles as scaffolds. Also provided are cobalt nanoparticles and hollow gold nano spheres (HGNs) produced according to the present methods. Kits that find use in practicing the methods of the present disclosure are also provided.

Pu, Ying-Chih↗

DOE OSTI2023

Assessment of the Phase-to-Ground Fault Apparent Admittance Method with Phase/Ground Boundaries to Detect Types of Electrical Faults for Protective Relays Using Signature Library and Simulated Events

Protective relays in electric power grids recognize the types of electrical faults in a few seconds. The most common detection method to detect the types of electrical faults is based on measuring the angle between the zero and negative sequence currents. However, it is not completely accurate because the phase to phase ground and phase to ground electrical faults could have the same detection conditions. Therefore, engineers need to plot the events after an electrical fault to observe the nature of the incidents in detail. In this study, the phase to ground fault apparent (PGFA) admittance method with phase/ground boundaries identified the types of electrical faults located in distribution power lines and feeders. This method was based on measuring the PGFA admittance magnitudes for the faulted and non-faulted phases, resulting in greater than zero and near zero, respectively. The PGFA admittance algorithm was built with MATLAB/ Simulink software and tested with signature library and grid simulation events. The PGFA method with phase/ground boundaries was evaluated with the confusion matrix. The measured and predicted values matched in more than 90% of the tests, and the PGFA admittance method with phase/ground boundaries presented an accuracy of 94.3% and a precision of 100%.

Piesciorovsky, Emilio↗

DOE OSTI2023

Systems and methods for active learning from sparse training data

A method for active learning using sparse training data can include training a machine learning model using less than ten first training data points to generate a candidate machine learning model. The method can include performing a Monte Carlo process to sample one or more first outputs of the candidate machine learning model. The method can include testing the one or more first outputs to determine if each of the one or more first outputs satisfy a respective convergence condition. The method can include, responsive to at least one first output not satisfying the respective convergence condition, training the candidate machine learning model using at least one second training data point corresponding to the at least one first output. The method can include, responsive to the one or more first outputs each satisfying the respective convergence condition, outputting the candidate machine learning model.

Sankaranarayanan, Subramanian↗

DOE OSTI2023

Anti-fouling chiral surfaces for membrane filtration and methods therefor

Belfort, Georges↗

DOE OSTI2023

Devices and methods for increasing the speed or power efficiency of a computer when performing machine learning using spiking neural networks

A method for increasing a speed and efficiency of a computer when performing machine learning using spiking neural networks. The method includes computer-implemented operations; that is, operations that are solely executed on a computer. The method includes receiving, in a spiking neural network, a plurality of input values upon which a machine learning algorithm is based. The method also includes correlating, for each input value, a corresponding response speed of a corresponding neuron to a corresponding equivalence relationship between the input value to a corresponding latency of the corresponding neuron. Neurons that trigger faster than other neurons represent close relationships between input values and neuron latencies. Latencies of the neurons represent data points used in performing the machine learning. A plurality of equivalence relationships are formed as a result of correlating. The method includes performing the machine learning using the plurality of equivalence relationships.

Vineyard, Craig Michael↗

DOE OSTI2023

Automated methods for scalable, parallelized enzymatic biopolymer synthesis and modification using microfluidic devices

Methods for the automated template-free synthesis of user-defined sequence controlled biopolymers using microfluidic devices are described. The methods facilitate simultaneous synthesis of up to thousands of uniquely addressed biopolymers from the controlled movement and combination of regents as fluid droplets using microfluidic and EWOD-based systems. In some forms, biopolymers including nucleic acids, peptides, carbohydrates, and lipids are synthesized from step-wise assembly of building blocks based on a user-defined sequence of droplet movements. In some forms, the methods synthesize uniquely addressed nucleic acids of up to 1,000 nucleotides in length. Methods for adding, removing and changing barcodes on biopolymers are also provided. Biopolymers synthesized according to the methods, and libraries and databases thereof are also described. Modified biopolymers, including chemically modified nucleotides and biopolymers conjugated to other molecules are described.

Banal, James↗

DOE OSTI2023

Adaptive, Active Learning, and Multifidelity Monte Carlo Methods in the MOOSE Stochastic Tools Module

MOOSE is an open-source computational platform for constructing multi-physics models and executing them in a massively parallel fashion. It has a stochastic tools module (STM) for forward/inverse uncertainty quantification (UQ) and surrogate modeling. This presentation details some recent developments to the STM with respect to the implementation of adaptive, active learning, and multifidelity Monte Carlo methods for forward UQ of computational models. Specifically, the adaptive Monte Carlo methods include Markov Chain Monte Carlo (MCMC)-driven algorithms like adaptive importance sampling and parallelized subset simulation for statistical QoI estimation, rare events analysis, and stochastic gradient-free optimization. The active learning methods include Gaussian Process (GP) surrogates and their training via Adam optimization, design of acquisition functions, and integration with samplers like Monte Carlo, adaptive importance, and parallelized subset simulation. These active learning methods are also designed to work in a batch mode, wherein, the required calls to the full computational model are executed in parallel whenever a user-specified batch size is met. The multifidelity methods in STM are broadly divided into two categories: hierarchical, where a defined hierarchy exists among the low-fidelity models, and peer, where all the low-fidelity models are treated equally. A GP surrogate is used to learn the differences between the low- and high-fidelity models in both multifidelity categories, and acquisition functions from the active learning classes are used to decide whether to rely on a low-fidelity model or call the expensive high-fidelity model. Alongside the software description and usage, applications are also presented to nuclear engineering computational models including a TRISO nuclear fuel particle, a reactor pressure vessel, and a heat-pipe microreactor.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Comparison of Silicon Decarbonization Methods to Reduce Process Emissions and Energy Consumption

The demand for silicon is rising due to its use in solar cells, electronics, and alloying as part of the green transition. However, these products require silicon metal (metallurgical grade silicon, >98% Si) as their main raw material before undergoing further chemical refining, usually via gas phase in methods such as the Siemens process. Silicon metal is currently produced in a submerged arc furnace via carbothermic reduction of quartz, which results in 10-12 tCO2/tSi. More than 40% of these emissions are process emissions, meaning they do not include contributions from electricity generation or transport. This talk will discuss promising methods to reduce or eliminate these process emissions. These include the use of biocarbon and carbon capture, aluminothermic reduction, electrochemical reduction with molten salts, and use of hydrogen in a modified Siemens process. Biocarbon and carbon capture is a straightforward method to reduce the net carbon emissions of the process, but long-term sustainable production of high-quality charcoal is a challenge. Aluminothermic reduction makes good use of aluminum byproducts such as dross but is perhaps limited in its applications to aluminum-silicon alloy products. Electrochemical reduction is promising as a method of producing high purity Si from quartz, without further need for the Siemens process, but is still in early stages of research and slow processing times are a concern. Finally, the use of hydrogen in a modified Siemens process would also result in a high purity Si product from quartz, however the process has considerable logistical challenges with corrosive and unstable gases at high temperatures. This talk will further expand on the potential merits and challenges of each method, while comparing their potential to reduce emissions and/or energy consumption while meeting global demand.

decarbonization↗

DOE OSTI2024

Systems and methods for application performance profiling and improvement

Methods for analyzing and improving a target computer application and corresponding systems and computer-readable mediums. A method includes receiving the target application. The method includes generating a parallel control flow graph (ParCFG) corresponding to the target application. The method includes analyzing the ParCFG by the computer system. The method includes generating and storing the modified ParCFG for the target application.

Leidel, John D.↗

DOE OSTI2021

Relativistic coupled‐cluster and equation‐of‐motion coupled‐cluster methods

Abstract The development of relativistic coupled‐cluster (CC) and equation‐of‐motion coupled‐cluster (EOM‐CC) methods is reviewed. An emphasis is placed on recent efforts to improve the computational efficiency of CC and EOM‐CC calculations with non‐perturbative treatments of spin‐orbit coupling (SO‐CC and EOM‐CC) by partially recovering spin symmetry in the formulations. Example calculations of electronic ground state as well as valence‐excited and core‐excited states for molecules containing heavy elements are presented to demonstrate the applicability and usefulness of the SO‐CC and EOM‐CC methods. Future directions for the development of the SO‐CC and EOM‐CC methods are also discussed. This article is categorized under: Electronic Structure Theory > Ab Initio Electronic Structure Methods

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Stage-local partitioned two-step runge-kutta methods for large systems of ordinary differential equations

We introduce stage-local partitioned two-step Runge-Kutta methods are an extension of standard two-step Runge-Kutta methods, which are an alternative to the standard additive two-step Runge-Kutta methods currently existing in the literature. Furthermore, these new schemes are designed with an eye towards truly N-partitioned systems and leverage local stage approximations to make several computationally interesting approximations viable. Specifically, the focus on local stage approximations makes possible the construction of truly asynchronous schemes, in the parallel sense, possible. In addition, we show that an implicit-explicit approach to these schemes can lead to methods that require the inversion of only local nonlinear systems.

Applied Dynamical Systems↗

DOE OSTI2023

Large deformation and brittle failure calculated using the dual-domain material point method

The dual domain material point (DDMP) method is explored as a candidate to be implemented in a general purpose code to perform simulations of materials with complex geometry that undergo large history-dependent deformation and failure. To test its candidacy, we study its mesh convergence, its sensitivity to mesh orientation, and its ability to handle softening and failure of a material. Simulations of large deformation and simulations of mechanical failure are performed using both DDMP and the material point method (MPM). When cell-crossing of material points is not an issue and when there are a sufficient number of material points in each computation cell, the numerical error decreases with the square of the cell size as expected for both MPM and DDMP. DDMP has reduced error compared with MPM when there are many instances of material points crossing cell boundaries due to the continuous nature of the modified gradient of the shape functions. Simulations of a specimen under tension are also performed where the background mesh is aligned and misaligned with the tension direction. MPM displays a significant mesh-dependent stress field, DDMP shows negligible mesh dependency. Despite a mesh orientation-dependent stress field from MPM, the critical tension and failure mode from both MPM and DDMP calculations have negligible mesh dependency when using a non-local failure model. If only the failure mode is important (i.e., local stresses are unimportant), MPM with a non-local failure model is a suitable method for modeling failure with small deformations. However, if local stresses are also important or if there are large deformations with many cell-crossings before failure, DDMP should be the method that is used. A needed improvement for DDMP is identified from our numerical simulations.

36 MATERIALS SCIENCE↗

DOE OSTI2023

A subdivision-stabilized B-spline mixed material point method

Subjected to external loadings, polymeric materials, e.g., biological tissues, hydrogels, and elastomers, may undergo extreme, nearly incompressible, (self-)contact deformations. For numerical modeling employing mesh-based techniques such as the finite element method (FEM), these deformations pose significant challenges due to large distortions in the deformed geometry, accuracy issues stemming from volumetric locking effects, and increased computational cost from complex contact searches. As an alternative to mesh-based methods, the material point method (MPM), a continuum-based particle technique, is gaining attention for its ability to handle extreme distortions and capture no-slip contact without added cost. For nearly incompressible material behaviors, while mixed formulations can address locking effects by treating displacements and pressure as independent fields, they can suffer from numerical instabilities close to the incompressibility limit due to the violation of the inf-sup condition, leading to inaccurate nodal pressure solutions. Here we propose an efficient and stable mixed B-spline material point method with highest achievable regularity for quasi-compressible polymeric materials. Using the two-scale relation of B-splines, we introduce a subdivision-stabilization for the two-field mixed MPM and obtain numerically stable, oscillation-free nodal solutions with equal-order interpolations with optimal regularity. Building on the Eulerian-Lagrangian nature of MPM, a previously-converged solution framework is adopted to mitigate issues related to cell-crossing and numerical fracture artifact present in standard MPM. We assess the stability and accuracy of the developed mixed MPM at large deformations for soft materials through the benchmark Cook’s membrane problem. Additionally, we test the robustness of the proposed MPM by modeling several examples, including the compression and indentation of a circular block into a quasi-compressible substrate and the twisting deformation of a rectangular block. The findings demonstrate the MPM’s capabilities for modeling practical soft material applications.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Multifidelity methods for uncertainty quantification of a nonlocal model for phase changes in materials

This study is devoted to the construction of a multifidelity Monte Carlo (MFMC) method for the uncertainty quantification of a nonlocal, non-mass-conserving Cahn-Hilliard model for phase transitions with an obstacle potential. Here, we are interested in estimating the expected value of an output of interest (OoI) that depends on the solution of the nonlocal Cahn-Hilliard model. As opposed to its local counterpart, the nonlocal model captures sharp interfaces without the need for significant mesh refinement. However, the computational cost of the nonlocal Cahn-Hilliard model is higher than that of its local counterpart with similar mesh refinement, inhibiting its use for outer-loop applications such as uncertainty quantification. The MFMC method augments the desired high-fidelity, high-cost OoI with a set of lower-fidelity, lower-cost OoIs to alleviate the computational burden associated with nonlocality. Most of the computational budget is allocated to sampling the cheap surrogate models to achieve speedup, whereas the high-fidelity model is sparsely sampled to maintain accuracy. For the non-mass-conserving nonlocal Cahn-Hilliard model, the use of the MFMC method results in, for a given computational budget, about an order of magnitude reduction in the mean-squared error of the expected value of the OoI relative to that of the Monte Carlo method.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Meshfree methods on manifolds for hydrodynamic flows on curved surfaces: A Generalized Moving Least-Squares (GMLS) approach

We utilize generalized moving least squares (GMLS) to develop meshfree techniques for discretizing hydrodynamic flow problems on manifolds. We use exterior calculus to formulate incompressible hydrodynamic equations in the Stokesian regime and handle the divergence-free constraints via a generalized vector potential. This provides less coordinate-centric descriptions and enables the development of efficient numerical methods and splitting schemes for the fourth-order governing equations in terms of a system of second-order elliptic operators. Using a Hodge decomposition, we develop methods for manifolds having spherical topology. We show the methods exhibit high-order convergence rates for solving hydrodynamic flows on curved surfaces. The methods also provide general high-order approximations for the metric, curvature, and other geometric quantities of the manifold and associated exterior calculus operators. The approaches also can be utilized to develop high-order solvers for other scalar-valued and vector-valued problems on manifolds.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Maximum bound principle preserving integrating factor Runge–Kutta methods for semilinear parabolic equations

A large class of semilinear parabolic equations satisfy the maximum bound principle (MBP) in the sense that the time-dependent solution preserves for any time a uniform pointwise bound imposed by its initial and boundary conditions. Here, the MBP plays a crucial role in understanding the physical meaning and the well-posedness of the mathematical model. Investigation on numerical algorithms with preservation of the MBP has attracted increasingly attentions in recent years, especially for the temporal discretizations, since the violation of MBP may lead to nonphysical solutions or even blow-ups of the algorithms. In this paper, we study high-order MBP-preserving time integration schemes by means of the integrating factor Runge-Kutta (IFRK) method. Beginning with the space-discrete system of semilinear parabolic equations, we present the IFRK method in general form and derive the sufficient conditions for the method to preserve the MBP. In particular, we show that the classic four-stage, fourth-order IFRK scheme is MBP preserving for some typical semilinear systems although not strong stability preserving, which can be instantly applied to the Allen-Cahn type of equations. To our best knowledge, this is the first time to present a fourth-order linear numerical method preserving the MBP. In addition, convergence of these numerical schemes is proved theoretically and verified numerically, as well as their efficiency by simulations of 2D and 3D long-time evolutional behaviors. Numerical experiments are also carried out for a model which is not a typical gradient flow as the Allen-Cahn type of equations.

97 MATHEMATICS AND COMPUTING↗