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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Long-time variation in magnetic structure of CeIr 3 Si 2 : Observation of a nucleation-and-growth process of magnetic domains

CeIr 3 Si 2 is the first three-dimensional uniform magnet in which the long-time variation in magnetic structure was observed. To clarify the microscopic mechanism of this magnetic structural change, time-resolved neutron scattering measurements have been reinvestigated. Clear time variations in the line widths as well as the amplitudes of magnetic Bragg diffractions have been observed in this improved instrumentation. On the notion of this observation, a nucleation-and-growth model of magnetic structural change has been presented. The numerical calculation with this model reproduces well the observation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2012

Contrast variation in spin-echo small angle neutron scattering

The principle of using contrast variation spin-echo small angle neutron scattering (SESANS) technique for colloidal structural investigation is discussed. Based on the calculations of several model systems, we find that the contrast variation SESANS technique is not sensitive in detecting the structural characteristics of colloidal suspensions consisting of particles with uniform scattering length density profiles. However, its capability of resolving the structural heterogeneity, at both intra- and inter-colloidal length scales, is clearly demonstrated. The prospect of using this new technique to investigate the structural information that is difficult to be probed by other ways is also explored.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2013

Accounting for the Variation of Driver Aggression in the Simulation of Conventional and Advanced Vehicles (Presentation)

This presentation discusses a method of accounting for realistic levels of driver aggression to higher-level vehicle studies, including the impact of variation in real-world driving characteristics (acceleration and speed) on vehicle energy consumption and different powertrains (e.g., conventionally powered vehicles versus electrified drive vehicles [xEVs]). Aggression variation between drivers can increase fuel consumption by more than 50% or decrease it by more than 20% from average. The normalized fuel consumption deviation from average as a function of population percentile was found to be largely insensitive to powertrain. However, the traits of ideal driving behavior are a function of powertrain. In conventional vehicles, kinetic losses dominate rolling resistance and aerodynamic losses. In xEVs with regenerative braking, rolling resistance and aerodynamic losses dominate. The relation of fuel consumption predicted from real-world drive data to that predicted by the industry-standard HWFET, UDDS, LA92, and US06 drive cycles was not consistent across powertrains, and varied broadly from the mean, median, and mode of real-world driving. A drive cycle synthesized by NREL's DRIVE tool accurately and consistently reproduces average real-world for multiple powertrains within 1%, and can be used to calculate the fuel consumption effects of varying levels of driver aggression.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

Assessment of Radiation Background Variation for Moving Detection Systems

The introduction points out that radiation backgrounds fluctuate across very short distances: factors include geology, soil composition, altitude, building structures, topography, and other manmade structures; and asphalt and concrete can vary significantly over short distances. Brief descriptions are given of the detection system, experimental setup, and background variation measurements. It is concluded that positive and negative gradients can greatly reduce the detection sensitivity of an MDS: negative gradients create opportunities for false negatives (nondetection), and positive gradients create a potentially unacceptable FAR (above 1%); the location of use for mobile detection is important to understand; spectroscopic systems provide more information for screening out false alarms and may be preferred for mobile use; and mobile monitor testing at LANL accounts for expected variations in the background.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Development of predictive capability of cycle-to-cycle variation in dual-fuel engines using supercomputing-based computational fluid dynamics

Cycle-to-cycle variation (CCV) in internal combustion engines is expected due to variabilities in operating conditions such as in-cylinder fuel stratification, mixing behavior, manifold air pressure, injection timing etc. Generally, engine experimental studies provide averaged quantities such as cylinder pressure, emissions, indicated mean effective pressure (IMEP) from multiple cycles and their CCV of IMEP, and CCV of cylinder pressure from consecutive cycles for a given test condition. Capturing 3D spatial distributions of relevant measures such as fuel-air mixing, temperature, turbulence levels and emissions from such experiments is challenging. Computational Fluid Dynamics (CFD) is an alternative to experiments, that can be used effectively to understand the spatial and temporal distributions of parameters of interest.Quantitative understanding of CCV in dual-fuel (DF) engines will accelerate the development and deployment of engines that provide fuel flexibility, economic advantage and emission compliance. Earlier work [1] performed to develop an understanding of the physics of CCV in large-bore, medium-speed, dual-fuel engines was partially successful. That effort entailed a sparse design of experiment (DOE) of full-cylinder geometry, closed-cycle, dual-fuel CFD simulations in which variabilities within some of the identified global parameters were exercised. The Coefficient of Variation (COV) of cylinder pressure was promising compared with experimental COV. This reinforced the understanding of which global parameters are significant contributors to CCV in DF combustion.The authors have used several high-performance supercomputing facilities in the process of developing predicting capability of CCV in large-bore engines, while operating in dual-fuel, diesel and natural gas (NG), combustion mode [1, 2]. In the current study CCV influencing parameters in a diesel-natural gas dual-fuel engine was studied using a 4th order DOE consisting of 137 concurrent cases. These full-geometry, closed-cycle, CFD simulations were run on the Mira supercomputing facility at the Argonne National Laboratory (ANL). This paper describes key learnings of that study along with a validation of the DOE. Further, the CCV prediction methodology, which is applicable to simulation-based design and testing, is described.

Gubba, Sreenivasa Rao↗
DOE OSTI2019

Managing variations among nodes in parallel system frameworks

Systems, apparatuses, and methods for managing variations among nodes in parallel system frameworks. Sensor and performance data associated with the nodes of a multi-node cluster may be monitored to detect variations among the nodes. A variability metric may be calculated for each node of the cluster based on the sensor and performance data associated with the node. The variability metrics may then be used by a mapper to efficiently map tasks of a parallel application to the nodes of the cluster. In one embodiment, the mapper may assign the critical tasks of the parallel application to the nodes with the lowest variability metrics. In another embodiment, the hardware of the nodes may be reconfigured so as to reduce the node-to-node variability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Removing Rotational Variations from Shoulder Thermocouples in Friction Stir Welding

Thick plate and lower temperature friction stir welding results in good post-weld properties by welding at very low tool rotational speeds. Low speeds cause large variations in the measured shoulder temperature, making temperature control difficult. This paper describes a computationally light method to correct for oscillating measured temperatures. Data is collected over the previous two revolutions and used to build a compensation table and calculate a derivative. In simulation, measured temperature variations are reduced by a factor of five to ten. Temperature data is preserved and not cornered, nor is any time delay induced by this method, unlike with filtering methods.

Friction Stir Welding, Temperature↗
DOE OSTI2022

Reducing the cost of energy estimation in the variational quantum eigensolver algorithm with robust amplitude estimation

Quantum chemistry and materials is one of the most promising applications of quantum computing. Yet much work is still to be done in matching industry-relevant problems in these areas with quantum algorithms that can solve them. Most previous efforts have carried out resource estimations for quantum algorithms run on large-scale fault-tolerant architectures, which include the quantum phase estimation algorithm. In contrast, few have assessed the performance of near-term quantum algorithms, which include the variational quantum eigensolver (VQE) algorithm. Recently, a large-scale benchmark study [Gonthier et al. 2020] found evidence that the performance of the variational quantum eigensolver for a set of industry-relevant molecules may be too inefficient to be of practical use. This motivates the need for developing and assessing methods that improve the efficiency of VQE. In this work, we predict the runtime of the energy estimation subroutine of VQE when using robust amplitude estimation (RAE) to estimate Pauli expectation values. Under conservative assumptions, our resource estimation predicts that RAE can reduce the runtime over the standard estimation method in VQE by one to two orders of magnitude. Despite this improvement, we find that the runtimes are still too large to be practical. These findings motivate two complementary efforts towards quantum advantage: 1) the investigation of more efficient near-term methods for ground state energy estimation and 2) the development of problem instances that are of industrial value and classically challenging, but better suited to quantum computation.

Johnson, Peter D.↗
DOE OSTI2022

Fermionic approach to variational quantum simulation of Kitaev spin models

We use the variational quantum eigensolver (VQE) to simulate Kitaev spin models with and without integrability breaking perturbations, focusing in particular on the honeycomb and square-octagon lattices. These models are well known for being exactly solvable in a certain parameter regime via a mapping to free fermions. We use classical simulations to explore a novel variational ansatz that takes advantage of this fermionic representation and is capable of expressing the exact ground state in the solvable limit. We also demonstrate that this ansatz can be extended beyond this limit to provide excellent accuracy when compared to other VQE approaches. In certain cases, this fermionic representation is advantageous because it reduces by a factor of two the number of qubits required to perform the simulation. We also comment on the implications of our results for simulating non-Abelian anyons on quantum computers.

Jahin, Ammar↗
DOE OSTI2023

On Practical Aspects of Variational Consistency in Contact Dynamics

Usage of contact mechanics methodologies is a pervasive modeling requirement in dynamic simulations. While for some trivial problems, solutions taken from analytical geometry are available, use of a finite element framework is common to achieve formulation generality. This work explores two dynamic contact formulations: one based on the traditional node-to-segment (NTS) approach, and a variationally consistent segment-to-segment (STS) mortar formulation. The NTS formulation employed here enforces the constraints kinematically (i.e., the interpenetration is enforced to the solver tolerance), whereas the mortar approach uses Lagrange multipliers to enforce the contact constraints. Both approaches are implemented in the open-source finite element framework Multiphysics Object-Oriented Simulation Environment (MOOSE). The results highlight two relevant contact-interface-related dynamic phenomena in finite element simulations. First, stabilization of contact constraints is discussed, taking into account the evolution of the total energy in a benchmark problem. Second, the influence of finite element discretization on both of the aforementioned contact formulations is analyzed by exercising a large-deformation example with continuous relative sliding. Variationally consistent contact approaches such as the mortar formulation lead to improved energy preservation and avoid spurious excitation of the system's frequencies. This is especially relevant in settings where inertia and vibrations are of importance.

42 ENGINEERING↗
DOE OSTI2024

Noise-induced transition in optimal solutions of variational quantum algorithms

Variational quantum algorithms are promising candidates for delivering practical quantum advantage on noisy intermediate-scale quantum (NISQ) hardware. However, optimizing the noisy cost functions associated with these algorithms is challenging for system sizes relevant to quantum advantage. In this work, we investigate the effect of noise on optimization by studying a variational quantum eigensolver (VQE) algorithm calculating the ground state of a spin chain model, and we observe an abrupt transition induced by noise to the optimal solutions. We will present numerical simulations, a demonstration using an IBM quantum processor unit (QPU), and a theoretical analysis indicating the origin of this transition. Our findings suggest that careful analysis is crucial to avoid misinterpreting the noise-induced features as genuine algorithm results.

Li, Andy C.Y.↗
DOE OSTI2023

Exploring the scaling limitations of the variational quantum eigensolver with the bond dissociation of hydride diatomic molecules

Abstract Materials simulations involving strongly correlated electrons pose fundamental challenges to state‐of‐the‐art electronic structure methods but are hypothesized to be the ideal use case for quantum computing algorithms. To date, no quantum computer has simulated a molecule of a size and complexity relevant to real‐world applications, despite the fact that the variational quantum eigensolver (VQE) algorithm can predict chemically accurate total energies. Nevertheless, because of the many applications of moderately sized, strongly correlated systems, such as molecular catalysts, the successful use of the VQE stands as an important waypoint in the advancement toward useful chemical modeling on near‐term quantum processors. In this paper, we take a significant step in this direction. We lay out the steps, write, and run parallel code for an (emulated) quantum computer to compute the bond dissociation curves of the TiH, LiH, NaH, and KH diatomic hydride molecules using the VQE. TiH was chosen as a relatively simple chemical system that incorporates d orbitals and strong electron correlation. Because current VQE implementations on existing quantum hardware are limited by qubit error rates, the number of qubits available, and the allowable gate depth, recent studies using it have focused on chemical systems involving s and p block elements. Through VQE + UCCSD calculations of TiH, we evaluate the near‐term feasibility of modeling a molecule with d‐orbitals on real quantum hardware. We demonstrate that the inclusion of d‐orbitals and the use of the UCCSD ansatz, which are both necessary to capture the correct TiH physics, dramatically increase the cost of this problem. We estimate the approximate error rates necessary to model TiH on current quantum computing hardware using VQE + UCCSD and show them to likely be prohibitive until significant improvements in hardware and error correction algorithms are available.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Learning and Predicting Photonic Responses of Plasmonic Nanoparticle Assemblies via Dual Variational Autoencoders

In this work, the application of machine learning is demonstrated for rapid and accurate extraction of plasmonic particles cluster geometries from hyperspectral image data via a dual variational autoencoder (dual-VAE). In this approach, the information is shared between the latent spaces of two VAEs acting on the particle shape data and spectral data, respectively, but enforcing a common encoding on the shape-spectra pairs. It is shown that this approach can establish the relationship between the geometric characteristics of nanoparticles and their far-field photonic responses, demonstrating that hyperspectral darkfield microscopy can be used to accurately predict the geometry (number of particles, arrangement) of a multiparticle assemblies below the diffraction limit in an automated fashion with high fidelity (for monomers (0.96), dimers (0.86), and trimers (0.58). This approach of building structure-property relationships via shared encoding is universal and should have applications to a broader range of materials science and physics problems in imaging of both molecular and nanomaterial systems.

variational autoencoder↗
DOE OSTI2012

L{sup {infinity}} Variational Problems with Running Costs and Constraints

Various approaches are used to derive the Aronsson-Euler equations for L{sup {infinity}} calculus of variations problems with constraints. The problems considered involve holonomic, nonholonomic, isoperimetric, and isosupremic constraints on the minimizer. In addition, we derive the Aronsson-Euler equation for the basic L{sup {infinity}} problem with a running cost and then consider properties of an absolute minimizer. Many open problems are introduced for further study.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Variational data augmentation for a learning-based granular predictive model of power outages

As the trend in climate change continues, extreme weather events are expected to occur with increasing frequency and severity and pose a significant threat to the electric power infrastructure. Regardless of the efforts a utility puts towards hardening the grid, storm-induced damage to the utility assets such as cables and distributed energy resources (DERs) that are particularly vulnerable to such events is unavoidable. Access to a highly granular, in space and time, outage forecasting tool with long lead times (i.e., days ahead) will enhance the efficiency of service restoration efforts. Here, in this study, we propose to develop and implement a multi-model framework as an operational tool based on a granular and multi-day outage forecasting model using operational numerical weather prediction model forecasts and detailed component outage information. An innovative two-layered recurrent neural network, i.e., a long-short-term-memory (LSTM)-based variational autoencoder (VAE) framework and a sliding window are used to address the uneven distribution of different types of weather events and make better use of the time-series data. Case studies are performed to demonstrate the performance of the new framework.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Thermodynamical properties of liquid lanthanides-A variational approach

Thermodynamical properties like Entropy (S), Internal energy (E) and Helmholtz free energy (F) of liquid lanthanides using a variation principle based on the Gibbs-Bogoliubuv (GB) inequality with Percus Yevick hard sphere reference system have been reported in the present investigation. To describe electron-ion interaction we have used our newly constructed parameter free model potential along with Sarkar et al. local field correction function. Lastly, we conclude that our newly constructed model potential is capable to explain the thermodynamical properties of liquid lanthanides.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Iterative Joint Ptychography-Tomography with Total Variation Regularization

In order to determine the 3D structure of a thick sample, researchers have recently combined ptychography (for high resolution) and tomography (for 3D imaging) in a single experiment. 2-step methods are usually adopted for reconstruction, where the ptychography and tomography problems are often solved independently. In this paper, we provide a novel model and ADMM-based algorithm to jointly solve the ptychography-tomography problem iteratively, also employing total variation regularization. The proposed method permits large scan stepsizes for the ptychography experiment, requiring less measurements and being more robust to noise with respect to other strategies, while achieving higher reconstruction quality results.

2-step methods↗
DOE OSTI2024

Elliptically-Contoured Tensor-variate Distributions with Application to Image Learning

Statistical analysis of tensor-valued data has largely used the tensor-variate normal (TVN) distribution that may be inadequate for data arising from distributions with heavier or lighter tails. We study a general family of elliptically contoured (EC) TV distributions and derive its characterizations, moments, marginal, and conditional distributions. We describe procedures for maximum likelihood estimation from data that are (1) uncorrelated draws from an EC distribution, (2) from a scale mixture of the TVN distribution, and (3) from an underlying but unknown EC distribution, for which we extend Tyler’s robust estimator. A detailed simulation study highlights the benefits of choosing an EC distribution over the TVN for heavier-tailed data. We develop TV classification rules using discriminant analysis and EC errors and show that they better predict cats and dogs from images in the Animal Faces-HQ dataset than the TVN-based rules. A novel tensor-on-tensor regression and TV analysis of variance (TANOVA) framework under EC errors is also demonstrated to better characterize gender, age, and ethnic origin than the usual TVN-based TANOVA in the celebrated labeled faces of the wild dataset.

97 MATHEMATICS AND COMPUTING↗