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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Multi-rate time integration on overset meshes

Overset meshes are an effective tool for the computational fluid dynamic simulation of problems with complex geometries or multiscale spatio-temporal features. When the maximum allowable timestep on one or more meshes is significantly smaller than on the remaining meshes, standard explicit time integrators impose inefficiencies for time-accurate calculations by requiring that all meshes advance with the smallest timestep. With the targeted use of multi-rate time integrators, separate meshes can be time-marched at independent rates to avoid wasteful computation while maintaining accuracy and stability. This work applies time-explicit multi-rate integrators to the simulation of the compressible Navier-Stokes equations discretized on overset meshes using summation-by-parts (SBP) operators and simultaneous approximation term (SAT) boundary conditions. We introduce a class of multi-rate Adams-Bashforth (MRAB) schemes that offer significant stability improvements and computational efficiencies for SBP-SAT methods. We present numerical results that confirm the efficacy of MRAB integrators, outline a number of implementation challenges, and demonstrate a reduction in computational cost enabled by MRAB. We also look at the use of our method in the setting of a large-scale distributed-memory parallel implementation where we discuss concerns involving load balancing and communication efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Bayesian Optimized Deep Ensemble for Uncertainty Quantification of Deep Neural Networks: a System Safety Case Study on Sodium Fast Reactor Thermal Stratification Modeling

Deep neural networks (DNNs) are increasingly important to scientific computing and engineering system simulations. Accurate uncertainty quantification (UQ) for DNNs is critical in safety-sensitive engineering domains. Traditional Deep Ensemble (DE) methods, while easy to implement, frequently suffer from poorly calibrated uncertainty estimates and limited predictive accuracy due to reliance on fixed architectures with varied weight initializations. To address these issues, we introduce a workflow that combines Bayesian Optimization (BO) and DE. The workflow is modular, scalable, and integrates parallel BO initialized with Sobol sequences to individually optimize the hyperparameters of each ensemble member. This method enhances ensemble diversity, improves predictive accuracy, and provides reliable uncertainty estimates. We evaluate the proposed BODE approach in a sodium fast reactor thermal stratification modeling case study, where we used a densely connected convolutional neural network to predict turbulent viscosity during the reactor transient with consideration of data noise. We benchmark its performance against several optimization approaches, including baseline deep ensemble, evolutionary algorithm-optimized ensemble, ensemble formed via random search combined with greedy selection, and a BO ensemble using random initialization. Here, our results demonstrate superior performance of the developed BODE approach. In noise-free scenarios, BODE notably reduces incorrect aleatoric uncertainty and significantly enhances predictive accuracy. Under conditions of 5% and 10% Gaussian noise, BODE adaptively quantifies uncertainty proportional to data noise, achieving up to an 80% reduction in root mean square error compared to baseline methods and producing well-calibrated prediction intervals.

Bayesian optimization↗
DOE OSTI2019

Analytic band-to-trap tunneling model including band offset for heterojunction devices

In this paper, we present an analytic band-to-trap tunneling model based on the open boundary scattering approach. The new model has three major advantages: (i) It includes not only the well-known electric field effect, but more importantly, the effect of heterojunction band offset. This feature allows us to simulate both electric field and band offset enhanced carrier recombination near a heterojunction in heterostructures. (ii) Its analytic form enables straightforward implementation into a parallel Technology Computer Aided Design device and circuit simulators. (iii) The developed method can be used for any potentials which can be approximated to a good degree such that the Schrödinger equation with open boundary conditions results in piecewise analytic wave functions. Simulation results of an InGaP/GaAs heterojunction bipolar transistor (HBT) reveal that the proposed model predicts significantly increased base currents, because the tunneling of holes in the base to traps in the emitter is greatly enhanced by the emitter-base band offset. Lastly, this finding, which is not captured by existing band-to-trap tunneling models, is consistent with the experimental observation for an InGaP/GaAs HBT after neutron irradiation.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Anharmonic Bloch oscillations in a coupler composed of two parallel optical fibre arrays

The effects of laser beam propagation in a coupler composed of two parallel optical-fibre arrays are studied by the coupled-mode method taking into account the interaction of a given fibre with the nearest neighbours and the linear dependence of the propagation constant on the fibre number. It is shown that, due to the complex structure of each subsystem, the structure of light intensity spatial distribution becomes significantly complicated in the system under study. The occurrence of space limited transverse light diffraction is predicted. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Transferring predictions of formation energy across lattices of increasing size*

In this study, we show the transferability of graph convolutional neural network (GCNN) predictions of the formation energy of the nickel-platinum solid solution alloy across atomic structures of increasing sizes. The original dataset was generated with the large-scale atomic/molecular massively parallel simulator using the second nearest-neighbor modified embedded-atom method empirical interatomic potential. Geometry optimization was performed on the initially randomly generated face centered cubic crystal structures and the formation energy has been calculated at each step of the geometry optimization, with configurations spanning the whole compositional range. Using data from various steps of the geometry optimization, we first trained our open-source, scalable implementation of GCNN called HydraGNN on a lattice of 256 atoms, which accounts well for the short-range interactions. Using this data, we predicted the formation energy for lattices of 864 atoms and 2048 atoms, which resulted in lower-than-expected accuracy due to the long-range interactions present in these larger lattices. We accounted for the long-range interactions by including a small amount of training data representative for those two larger sizes, whereupon the predictions of HydraGNN scaled linearly with the size of the lattice. Therefore, our strategy ensured scalability while reducing significantly the computational cost of training on larger lattice sizes.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Anomalous thermal effect in Zr Te 5 observed via photothermal measurements

In this study, we explore the magneto-thermoelectric power (MTP) of $ZrTe$ 5 , a canonical Dirac semimetal, through a novel photothermal technique. Unlike conventional thermoelectric studies that rely on on-chip heaters and are limited by fabrication processes, especially for stress-sensitive materials, our approach utilizes photothermal effects to induce temperature gradients. Our experiments, applying a magnetic field approximately parallel and transverse to the photocurrent detection direction, reveal that the photothermal method efficiently and reliably extracts both diagonal and off-diagonal components of the thermoelectric coefficient of $ZrTe$ 5 . Here, we observe that the longitudinal MTP reproduces features previously reported in thermal transport studies, while the photoinduced transverse MTP confirms the anomalous Nernst effect. This photothermal measurement technique opens new avenues for investigating transport properties in a wide range of quantum materials, both in 3D and 2D systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Estimating Lossy Compressibility of Scientific Data Using Deep Neural Networks

Simulation based scientific applications generate increasingly large amounts of data on high-performance computing (HPC) systems. To allow data to be stored and analyzed efficiently, data compression is often utilized to reduce the volume and velocity of data. However, a question often raised by domain scientists is the level of compression that can be expected so that they can make more informed decisions, balancing between accuracy and performance. In this letter, we propose a deep neural network based approach for estimating the compressibility of scientific data. To train the neural network, we build both general features as well as compressor-specific features so that the characteristics of both data and lossy compressors are captured in training. Our approach is demonstrated to outperform a prior analytical model as well as a sampling based approach in the case of a biased estimation, i.e., for SZ. However, for the unbiased estimation (i.e., ZFP), the sampling based approach yields the best accuracy, despite the high overhead involved in sampling the target dataset.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Chance-Constrained Energy Management System for Power Grids With High Proliferation of Renewables and Electric Vehicles

This paper proposes a two-stage energy management system (EMS) for power grids with massive integration of electric vehicles (EVs) and renewable energy resources. The first stage economic dispatch determines the optimal operating points of charging stations and battery swapping stations (BSS) for EVs under plug-in and battery swapping modes, respectively. The proposed stochastic model predictive control (SMPC) problem in this stage is characterized through a chance-constrained optimization formulation that can effectively capture the system and the forecast uncertainties. A distributed algorithm, the alternating direction method of multipliers (ADMM), is applied to accelerate the optimization computation through parallel computing. The second stage is aimed in coordinating the EV charging mechanisms to continuously follow the first-stage solutions, i.e., the target operating points, and meeting the EV customers' charging demands captured via the Advanced Metering Infrastructure (AMI). In this work, the proposed solution offers a holistic control strategy for large-scale centralized power grids in which the aggregated individual parameters are predictable and the system dynamics do not vary sharply within a short time-interval.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Quantum Computing and Second Quantization

Quantum computers are by their nature many particle quantum systems. Both the many-particle arrangement and being quantum are necessary for the existence of the entangled states, which are responsible for the parallelism of the quantum computers. Second quantization is a very important approximate method of describing such systems. This lecture will present the general idea of the second quantization, and discuss shortly some of the most important formulations of second quantization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A Riccati Solution for the Ideal MHD Plasma Response with Applications to Real-time Stability Control

To effectuate near real-time feedback control of ideal MHD instabilities in a tokamak geometry, a rapid solution for stability analysis is a prerequisite. Toward this end, we reformulate the δW stability method with a Hamilton-Jacobi theory, elucidating analytical and numerical features of the generic tokamak ideal MHD stability problem. The plasma response matrix is demonstrated to be the solution of an ideal MHD matrix Riccati differential equation (MRDE). Since Riccati equations are prevalent in the control theory literature, such a shift in perspective brings to bear a range of numerical methods that are well-suited to the robust, fast solution of control problems. We discuss the usefulness of Riccati techniques in solving the stiff ODEs often encountered in ideal MHD stability analyses-—for example, in tokamak edge and stellarator physics. We then demonstrate the applicability of such methods to an existing 2D ideal MHD stability code—DCON—enabling its parallel operation in near real-time. Output is shown to match with high accuracy, and with wall-clock time ≪ 1s. Such speed may help enable active feedback ideal MHD stability control, especially in tokamak plasmas whose ideal MHD equilibria evolve with inductive timescale τ > 1s-—as in ITER.

Parallel↗
DOE OSTI2021

Soil and Water Chemistry and Trace Metal Extractability and Speciation in Wetland Soils from Illinois and South Carolina and Stream Sediments from Tennessee

Dataset revised on October 15, 2021. This revision adds sulfur and iron X-ray absorption near-edge structure spectra for the wetland soils and stream sediments from the field areas. It also renames the sample locations in a way that is more intuitive to readers of the companion paper that is under review. Finally, the data filenames and organization have been updated in their labeling to parallel the data sources in the associated paper. The abstract text and methods were also revised to reflect the data that was added to the dataset.Trace metals are essential for microbially-mediated biogeochemical processes occurring in anoxic wetland soils and stream bed sediments, such as denitrification, methanogenesis, and mercury methylation. Low availability of these elements may potentially inhibit key components of anaerobic carbon and nitrogen cycling and contaminant transformation. The solid-phase speciation of trace metals likely plays an important role in controlling their bioavailability. Metal speciation is well studied in contaminated soils and sediments as well as those naturally elevated in trace metals. However, less is known regarding the chemical forms of trace metals in systems having concentrations similar to geological background levels, the very settings where metal limitations may be most prevalent. We have investigated trace metal concentrations, extractability, and solid-phase speciation in three freshwater subsurface aquatic systems: marsh wetland soils, riparian wetland soils, and the sediments of a streambed.Data are provided for marsh wetland soils at Argonne National Laboratory, riparian wetland soils in the Tims Branch watershed at Savannah River National Laboratory, and stream bed sediments from East Fork Poplar Creek near Oak Ridge National Laboratory. Soil and sediment elemental abundances, mineralogy, and extractable nutrients as well as dissolved major elements, anions, trace metals, and nutrients in the overlying surface waters are provided. In addition, the results of sequential chemical extraction for the trace metals cobalt, nickel, copper, and zinc from the soils and sediment are reported as well as X-ray absorption near-edge structure (XANES) spectra in these materials are reported. To aid interpretation of these data, XANES spectra of sulfur in the soils and sediments as well as both XANES and extended X-ray absorption fine structure (EXAFS) spectra of iron in these materials are reported. The data package also includes the XANES spectra of reference standards and a potential interferent in the measurements. All data are provided in text-based CSV format with header sections indicating the data contained in each file and the corresponding units. Note that "u" is used in place of Greek lower case mu to indicate the micro prefix on units.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

BASS: An R Package for Fitting and Performing Sensitivity Analysis of Bayesian Adaptive Spline Surfaces

In this work, we present the R package BASS as a tool for nonparametric regression. The primary focus of the package is fitting fully Bayesian adaptive spline surface (BASS) models and performing global sensitivity analyses of these models. The BASS framework is similar to that of Bayesian multivariate adaptive regression splines (BMARS) from Denison, Mallick, and Smith (1998), but with many added features. The software is built to efficiently handle significant amounts of data with many continuous or categorical predictors and with functional response. Under our Bayesian framework, most priors are automatic but these can be modified by the user to focus on parsimony and the avoidance of overfitting. If directed to do so, the software uses parallel tempering to improve the reversible jump Markov chain Monte Carlo (RJMCMC) methods used to perform inference. We discuss the implementation of these features and present the performance of BASS in a number of analyses of simulated and real data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Deconvolute individual genomes from metagenome sequences through short read clustering

Metagenome assembly from short next-generation sequencing data is a challenging process due to its large scale and computational complexity. Clustering short reads by species before assembly offers a unique opportunity for parallel downstream assembly of genomes with individualized optimization. However, current read clustering methods suffer either false negative (under-clustering) or false positive (over-clustering) problems. Here we extended our previous read clustering software, SpaRC, by exploiting statistics derived from multiple samples in a dataset to reduce the under-clustering problem. Using synthetic and real-world datasets we demonstrated that this method has the potential to cluster almost all of the short reads from genomes with sufficient sequencing coverage. The improved read clustering in turn leads to improved downstream genome assembly quality.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Ion manipulation device

An ion manipulation method and device is disclosed. The device includes a pair of substantially parallel surfaces. An array of inner electrodes is contained within, and extends substantially along the length of, each parallel surface. The device includes a first outer array of electrodes and a second outer array of electrodes. Each outer array of electrodes is positioned on either side of the inner electrodes, and is contained within and extends substantially along the length of each parallel surface. A DC voltage is applied to the first and second outer array of electrodes. A RF voltage, with a superimposed electric field, is applied to the inner electrodes by applying the DC voltages to each electrode. Ions either move between the parallel surfaces within an ion confinement area or along paths in the direction of the electric field, or can be trapped in the ion confinement area.

42 ENGINEERING↗
DOE OSTI2014

Ion manipulation device with electrical breakdown protection

An ion manipulation method and device is disclosed. The device includes a pair of substantially parallel surfaces. An array of inner electrodes is contained within, and extends substantially along the length of, each parallel surface. The device includes a first outer array of electrodes and a second outer array of electrodes. Each outer array of electrodes is positioned on either side of the inner electrodes, and is contained within and extends substantially along the length of each parallel surface. A DC voltage is applied to the first and second outer array of electrodes. A RF voltage, with a superimposed electric field, is applied to the inner electrodes by applying the DC voltages to each electrode. Ions either move between the parallel surfaces within an ion confinement area or along paths in the direction of the electric field, or can be trapped in the ion confinement area. The surfaces are housed in a chamber, and at least one electrically insulative shield is coupled to an inner surface of the chamber for increasing a mean-free-path between two adjacent electrodes in the chamber.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Vacuum chamber for ion manipulation device

An ion manipulation method and device is disclosed. The device includes a pair of substantially parallel surfaces. An array of inner electrodes is contained within, and extends substantially along the length of, each parallel surface. The device includes a first outer array of electrodes and a second outer array of electrodes. Each outer array of electrodes is positioned on either side of the inner electrodes, and is contained within and extends substantially along the length of each parallel surface. A DC voltage is applied to the first and second outer array of electrodes. A RF voltage, with a superimposed electric field, is applied to the inner electrodes by applying the DC voltages to each electrode. Ions either move between the parallel surfaces within an ion confinement area or along paths in the direction of the electric field, or can be trapped in the ion confinement area. A predetermined number of pairs of surfaces are disposed in one or more chambers, forming a multiple-layer ion mobility cyclotron device.

36 MATERIALS SCIENCE↗