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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

A WENO-solver combined with adaptive momentum discretization for the Wigner transport equation and its application to resonant tunneling diodes

We present a novel numerical scheme for the deterministic solution of the Wigner transport equation, especially suited to deal with situations in which strong quantum effects are present. The unique feature of the algorithm is the expansion of the Wigner function in local basis functions, similar to finite element or finite volume methods. This procedure yields a discretization of the pseudo-differential operator that conserves the particle density on arbitrarily chosen grids. The high flexibility in refining the grid spacing together with the weighted essentially non-oscillatory (WENO) scheme for the advection term allows for an accurate and well-resolved simulation of the phase space dynamics. A resonant tunneling diode is considered as test case and a detailed convergence study is given by comparing the results to a non-equilibrium Green's functions calculation. The impact of the considered domain size and of the grid spacing is analyzed. The obtained convergence of the results towards a quasi-exact agreement of the steady state Wigner and Green's functions computations demonstrates the accuracy of the scheme, as well as the high flexibility to adjust to different physical situations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Data-driven optimal charging decision making for connected and automated electric vehicles: A personal usage scenario

This study introduces an optimal charging decision making framework for connected and automated electric vehicles under a personal usage scenario. This framework aims to provide charging strategies, i.e. the choice of charging station and the amount of charged energy, by considering constraints from personal daily itineraries and existing charging infrastructure. A data-driven method is introduced to establish a stochastic energy consumption prediction model with consideration of realistic uncertainties. This is performed by analyzing a large scale electric vehicle data set. A real-time updating method is designed to construct this prediction model from new consecutive data points in an adaptive way for real-world applications. Based on this energy cost prediction framework from real electric vehicle data, multistage optimal charging decision making models are introduced, including a deterministic model for average outcome decision making and a robust model for safest charging strategies. A dynamic programming algorithm is proposed to find the optimal charging strategies. Detailed simulations and case studies demonstrate the performance of the proposed algorithms to find optimal charging strategies. They also show the potential capability of connected and automated electric vehicles to reduce the range anxiety and charging infrastructure dependency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Modern Approaches to Exact Diagonalization and Selected Configuration Interaction with the Adaptive Sampling CI Method

Recent advances in selected configuration interaction methods have made them competitive with the most accurate techniques available and, hence, creating an increasingly powerful tool for solving quantum Hamiltonians. In this work, we build on recent advances from the adaptive sampling configuration interaction (ASCI) algorithm. We show that a useful paradigm for generating efficient selected CI/exact diagonalization algorithms is driven by fast sorting algorithms, much in the same way iterative diagonalization is based on the paradigm of matrix vector multiplication. We present several new algorithms for all parts of performing a selected CI, which includes new ASCI search, dynamic bit masking, fast orbital rotations, fast diagonal matrix elements, and residue arrays. The ASCI search algorithm can be used in several different modes, which includes an integral driven search and a coefficient driven search. The algorithms presented here are fast and scalable, and we find that because they are built on fast sorting algorithms they are more efficient than all other approaches we considered. After introducing these techniques, we present ASCI results applied to a large range of systems and basis sets to demonstrate the types of simulations that can be practically treated at the full-CI level with modern methods and hardware, presenting double- and triple-ζ benchmark data for the G1 data set. The largest of these calculations is Si$_2$H$_6$ which is a simulation of 34 electrons in 152 orbitals. We also present some preliminary results for fast deterministic perturbation theory simulations that use hash functions to maintain high efficiency for treating large basis sets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

State-of-the-Art Techniques for Large-Scale Stochastic Unit Commitment

Recent advances in deterministic unit commitment, both formulaic and algorithmic, along with modern algorithmic approaches for stochastic programming, have enabled the solution of stochastic unit commitment problems with hundreds of scenarios on large-scale transmission networks. In this presentation, we will give an overview of these methods, including lazy transmission constraint generation, lower-bounding techniques, and heuristics, all of which can be executed in concert with customized decomposition approaches for optimization under uncertainty. We demonstrate the effectiveness of these techniques on the TAMU Texas7K synthetic transmission network, leveraging realistic high-resolution forecasts based on NREL renewable resource availability data. The software leveraged for these demonstrations is available via the open-source software packages EGRET (for electrical grid optimization) and mpi-sppy (for optimization under uncertainty).

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2021

Adaptive Variational Quantum Imaginary Time Evolution Approach for Ground State Preparation

Abstract An adaptive variational quantum imaginary time evolution (AVQITE) approach is introduced that yields efficient representations of ground states for interacting Hamiltonians on near‐term quantum computers. It is based on McLachlan's variational principle applied to imaginary time evolution of variational wave functions. The variational parameters evolve deterministically according to equations of motions that minimize the difference to the exact imaginary time evolution, which is quantified by the McLachlan distance. Rather than working with a fixed variational ansatz, where the McLachlan distance is constrained by the quality of the ansatz, the AVQITE method iteratively expands the ansatz along the dynamical path to keep the McLachlan distance below a chosen threshold. This ensures the state is able to follow the quantum imaginary time evolution path in the system Hilbert space rather than in a restricted variational manifold set by a predefined fixed ansatz. AVQITE is used to prepare ground states of H 4 , H 2 O, and BeH 2 molecules, where it yields compact variational ansätze and ground state energies within chemical accuracy. Polynomial scaling of circuit depth with system size is shown through a set of AVQITE calculations of quantum spin models. Finally, quantum Lanczos calculations are demonstrated alongside AVQITE without additional quantum resource costs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Human Performance Modeling for Dynamic Human Reliability Analysis

Part of the U.S. Department of Energy’s (DOE’s) Light Water Reac- tor Sustainability (LWRS) Program, the Risk-Informed Safety Margin Charac- terization (RISMC) Pathway develops approaches to estimating and managing safety margins. RISMC simulations pair deterministic plant physics models with probabilistic risk models. As human interactions are an essential element of plant risk, it is necessary to integrate human actions into the RISMC risk framework. In this paper, we review simulation based and non simulation based human reliability analysis (HRA) methods. This paper summarizes the founda- tional information needed to develop a feasible approach to modeling human in- teractions in RISMC simulations.

human reliability analysis↗
DOE OSTI2015

Hierarchical ZnO Nanowire Growth with Tunable Orientations on Versatile Substrates Using Atomic Layer Deposition Seeding

The ability to synthesize semiconductor nanowires with deterministic and tunable control of orientation and morphology on a wide range of substrates, while high precision and repeatability are maintained, is a challenge currently faced for the development of many nanoscale material systems. Here we show that atomic layer deposition (ALD) presents a reliable method of surface and interfacial modification to guide nanowire orientation on a variety of substrate materials and geometries, including high-aspect-ratio, three-dimensional templates. We demonstrate control of the orientation and geometric properties of hydrothermally grown single crystalline ZnO nanowires via the deposition of a ZnO seed layer by ALD. The crystallographic texture and roughness of the seed layer result in tunable preferred nanowire orientations and densities for identical hydrothermal growth conditions. The structural and chemical relationship between the ALD layers and nanowires was investigated with synchrotron X-ray diffraction, high-resolution transmission electron microscopy, and X-ray photoelectron spectroscopy to elucidate the underlying mechanisms of orientation and morphology control. The resulting control parameters were utilized to produce hierarchical nanostructures with tunable properties on a wide range of substrates, including vertical micropillars, paper fibers, porous polymer membranes, and biological substrates. This illustrates the power of ALD for interfacial engineering of heterogeneous material systems at the nanoscale, to provide a highly controlled and scalable seeding method for bottom-up synthesis of integrated nanosystems.

ALD↗
DOE OSTI2025

Mapping Local Dissipation and Entropy Production in Complex and Active Fluids

While global entropy production provides a measure of irreversibility, its partitioning into contributions from local regions is key to understanding the mechanisms underlying time-reversal symmetry breaking in complex systems and active matter. Here, by analyzing local heat flows and fluxes, we propose a framework that enables the mapping of local dissipation and entropy production in a nonequilibrium system. We test this approach in simulations of fluids driven through complex environments and active systems. We connect the results across the local and global scales by showing that local dissipation and entropy production satisfy a local version of the usual (global) fluctuation theorem, which accounts for the correlations between the local region and its surroundings. Interestingly, in the case of the active fluid, our analysis reveals that these correlations are of opposite signs for the active (stochastic) and passive (deterministic) contributions to local dissipation.

Entropy↗
DOE OSTI2015

Development of ion beam figuring system with electrostatic deflection for ultraprecise X-ray reflective optics

We developed an ion beam figuring system that utilizes electrostatic deflection. The system can produce an arbitrary shape by deterministically scanning the ion beam. The scan of the ion beam, which can be precisely controlled using only an electrical signal, enables us to avoid degradation of the mirror shape caused by imperfect acceleration or deceleration of a mechanically scanning stage. Additionally, this surface figuring method can easily be combined with X-ray metrology because the workpiece remains fixed during the figuring. We evaluated the figuring accuracy of the system by fabricating a plano-elliptical mirror for X-ray focusing. A mirror with a shape error of 1.4 nm root mean square (RMS) with a maximum removal depth of 992 nm, which corresponds to figuring accuracy of 0.14% RMS, was achieved. After the second shape corrections, an elliptical shape with a shape error of approximately 1 nm peak-to-valley, 0.48 nm RMS could be fabricated. Then, the mirror surface was smoothed by a low-energy ion beam. Consequently, a micro-roughness of 0.117 nm RMS, measured by atomic force microscopy, was achieved over an area of 1 × 1 μm{sup 2}.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Development of a high-fidelity multi-physics coupling between MCNP6.2 and CTF4.0 for VVER applications

Ensuring system safety in the design, licensing, and operation phases is a priority in the nuclear industry. Performing extensive, full-scale reactor safety experiments is often prohibitive due to the large associated costs. Computational simulations offer an alternative safety analysis method, typically with significant cost reductions. Recent high-level developments in technology and increased availability of computational resources have allowed the development of high-fidelity, high-resolution multi-physics coupled codes. Such developments may be used to generate reference models for deterministic core calculations. Under this framework, the high-fidelity continuous energy Monte Carlo-based neutron transport code MCNP6.2 was coupled externally with the state-of-the-art thermal-hydraulics subchannel code, CTF4.0 for VVER (Water-Water Energetic Reactor) applications. A VVER-1000 fuel assembly model was used to demonstrate the capability of the coupled code. The converged coupled solution is compared to initial results, consisting of the first MCNP evaluation and first CTF evaluation after initialization. The VVER-1000 type assembly results are compared to other evaluations of the same assembly model. The findings indicate good agreement with expectations and reference cases, where available. The initial results of the coupled MCNP6.2/CTF4.0 calculations at the assembly level for steady-state calculations are presented in this study, which may support future work toward high-fidelity coupled full-core modeling capabilities. The multi-physics model may be further improved, enhanced, and expanded for both cycle depletion and transient applications. Such a multi-physics system will also be applicable to the VVER-1200 and other triangular lattice designs and support their deployment and operation safely and economically. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2015

Whitepaper on Uncertainty Quantification for MPACT

The MPACT code provides the ability to perform high-fidelity deterministic calculations to obtain a wide variety of detailed results for very complex reactor core models. However, MPACT currently does not have the capability to propagate the effects of input data uncertainties to provide uncertainties in the calculated results. This white paper discusses a potential method for MPACT uncertainty quantification (UQ) based on stochastic sampling.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Risk-Informed Safety Analysis for Accident Tolerant Fuels

Accident Tolerant Fuels (ATF) are being tested by different nuclear vendors and research organization and their introduction in light water reactors fleet is planned for the second half of the 2020`s. In the framework of the US-DOE Light-Water Reactor Sustainability program, Risk-Informed Safety Analysis pathway (LWRS-RISA), research activities are being conducted at the Idaho National Laboratory (INL) for developing tools and methods that can help the industry in quantifying the ATF introduction benefits. In this paper we describe the developed risk-informed methodology, the codes improvements and we present some results for selected accidental conditions. The developed methodology combines INL state-of-the-art deterministic Best Estimate tools like RELAP5-3D code, and Probabilistic Risk Analysis tools like RAVEN and SAPHIRE codes. The analyses are performed on a three-loops pressurized water reactor (PWR) simulating accidental conditions like Station Blackout and LB-LOCA and considering near-term ATF (FeCrAl and Chromium-coated clads). Finally, we show, through our methodologies, how the delta-Core Damage Frequency can be assessed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Templates for Risk Informed Assurance with Curvature Embeddings (TRACE)

We investigate recovery of geometric structure from networks embedded in manifolds with spatially varying curvature, extending the constant-curvature framework of Lubold et al. (2023). Our work supports cascade risk assessment in critical infrastructure through the Templates for Risk-informed Assurance with Curvature Embeddings (TRACE) framework. Simulations on a bi-modal Gaussian surface show that constant-curvature methods yield weighted averages shaped by clique patterns, while hierarchical clustering identifies distinct regimes. Localized estimation, however, reveals boundary contamination in transitional regions. To address heterogeneity, we develop distance metrics for graphs with edge and node features, proving their metric validity, and validate them via deterministic graph generation from canonical tilings. We further propose a diffusion-based anomaly detection approach that treats networks as glued manifolds, using curvature discontinuities to detect structural anomalies. Employing the carré-du-champ operator and scalar curvature, we achieve robust anomaly discrimination, demonstrated on the Singapore Water Treatment (SWaT) dataset with joint network-traffic and sensor features. Integration with TRACE reveals how curvature shapes cascade dynamics: positive curvature impedes, while negative curvature accelerates propagation. This geometric perspective provides interpretable risk metrics and visualization tools for critical infrastructure managers. While full validation remains ongoing, our contributions establish a rigorous foundation for geometric analysis of network resilience and cascade vulnerability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Deterministic and Statistical Modeling of a New Thermal Breakout Technology for Measuring the Maximum Horizontal In-Situ Stress

The current state-of-the-art technology for in-situ stress measurements involves an integrated approach that combines borehole breakout observations, drilling-induced tensile fractures, and hydraulic fracturing tests (i.e., “mini-fracs”). This methodology has achieved wide application in the oil and gas industry but has several limitations that often prevent successful in-situ stress measurements. One major limitation is that breakouts do not appear in all boreholes and are generally only a natural occurrence that cannot easily be controlled. Because borehole breakouts are used to directly measure the maximum horizontal in-situ stress magnitude, the absence of borehole breakouts presents a major data gap for in-situ stress measurements. In response to this data gap, a new US Department of Energy (US DOE)-sponsored thermal breakout technology that will provide a method for thermally inducing borehole breakouts and allow the consistent measurement of the maximum horizontal stress magnitude is currently in development. This thermal breakout technology involves heating the borehole and increasing the thermoelastic compressive stress in the rock until a breakout develops, which can be directly correlated to the maximum horizontal stress magnitude. The first step in this project was an analytical modeling study of the thermal breakout process. Based on the Kirsch solution (Kirsch 1898), a deterministic and statistical analysis was performed on the pertinent parameters that influence the maximum horizontal stress calculation. As a result of the analysis, the findings indicate that the thermal breakout technology is feasible and provides improved accuracy and/or an enhanced ability to measure the maximum horizontal stress. Future work as part of this US DOE-sponsored project includes additional validation through more detailed numerical modeling, laboratory testing, and field testing of the thermal breakout technology.

58 GEOSCIENCES↗
DOE OSTI2020

Deterministic Discrete Fracture Network (DFN) Model for the EGS Collab Project on the 4850 Level of the Sanford Underground Research Facility (SURF)

The EGS Collab is conducting hydraulic fracture stimulation and fluid circulation experiments in the Sanford Underground Research Facility (SURF) located in Lead, South Dakota. A total of eight ~60m-long subhorizontal boreholes were drilled from the 4850 Level (~1.5 km below the ground surface) into the crystalline rock of this former mine. Six of these holes are used for geophysical monitoring, one is used for hydraulic fracture stimulation, and the remaining hole was designed as a production borehole that receives water from the injection well via the induced and natural fracture system. The primary goal of creating the discrete fracture network model is to show that these modeling methods are critical for the development of enhanced geothermal systems (EGS). This includes the prediction of rock behavior during fracturing and during an extended period of water flow between the parallel injection and production boreholes. Understanding the results from the induced fracturing and flow is complicated by the presence of significant natural fractures that interact with the stimulation and/or flow pathways. The delineation and characterization of natural fractures is thus an important part of the project, and therefore a model of the Discrete Fracture Network (DFN) was developed on a deterministic basis. The DFN was populated using observations and interpretations integrated from drift (horizontal passageways that allow access in the underground) fracture mapping, analysis of core recovered from the eight boreholes, borehole televiewer logs and videos, and observations of flow between and within boreholes and in the drift. The natural fracture system is dominated by a pervasive northwest-trending, steeply dipping shear system that is identifiable in the drifts and the core. Hydraulic fracture stimulation, flow/tracer circulation tests, and geophysical monitoring revealed that the behavior of the injected water, and perhaps the growth of induced fractures, has been significantly influenced by the existing fractures identified in the DFN.

40 EE - Geothermal Technologies Office (EE-4G)↗
DOE OSTI2017

Hypothesis testing of scientific Monte Carlo calculations

The steadily increasing size of scientific Monte Carlo simulations and the desire for robust, correct, and reproducible results necessitates rigorous testing procedures for scientific simulations in order to detect numerical problems and programming bugs. However, the testing paradigms developed for deterministic algorithms have proven to be ill suited for stochastic algorithms. In this paper we demonstrate explicitly how the technique of statistical hypothesis testing, which is in wide use in other fields of science, can be used to devise automatic and reliable tests for Monte Carlo methods, and we show that these tests are able to detect some of the common problems encountered in stochastic scientific simulations. We argue that hypothesis testing should become part of the standard testing toolkit for scientific simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

SU-F-T-111: Investigation of the Attila Deterministic Solver as a Supplement to Monte Carlo for Calculating Out-Of-Field Radiotherapy Dose

Purpose: To use the Attila deterministic solver as a supplement to Monte Carlo for calculating out-of-field organ dose in support of epidemiological studies looking at the risks of second cancers. Supplemental dosimetry tools are needed to speed up dose calculations for studies involving large-scale patient cohorts. Methods: Attila is a multi-group discrete ordinates code which can solve the 3D photon-electron coupled linear Boltzmann radiation transport equation on a finite-element mesh. Dose is computed by multiplying the calculated particle flux in each mesh element by a medium-specific energy deposition cross-section. The out-of-field dosimetry capability of Attila is investigated by comparing average organ dose to that which is calculated by Monte Carlo simulation. The test scenario consists of a 6 MV external beam treatment of a female patient with a tumor in the left breast. The patient is simulated by a whole-body adult reference female computational phantom. Monte Carlo simulations were performed using MCNP6 and XVMC. Attila can export a tetrahedral mesh for MCNP6, allowing for a direct comparison between the two codes. The Attila and Monte Carlo methods were also compared in terms of calculation speed and complexity of simulation setup. A key perquisite for this work was the modeling of a Varian Clinac 2100 linear accelerator. Results: The solid mesh of the torso part of the adult female phantom for the Attila calculation was prepared using the CAD software SpaceClaim. Preliminary calculations suggest that Attila is a user-friendly software which shows great promise for our intended application. Computational performance is related to the number of tetrahedral elements included in the Attila calculation. Conclusion: Attila is being explored as a supplement to the conventional Monte Carlo radiation transport approach for performing retrospective patient dosimetry. The goal is for the dosimetry to be sufficiently accurate for use in retrospective epidemiological investigations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

A Framework for Error-Bounded Approximate Computing, with an Application to Dot Products

Approximate computing techniques, which trade off the computation accuracy of an algorithm for better performance and energy efficiency, have been successful in reducing computation and power costs in several domains. However, error sensitive applications in high-performance computing are unable to benefit from existing approximate computing strategies that are not developed with guaranteed error bounds. While approximate computing techniques can be developed for individual high-performance computing applications by domain specialists, this often requires additional theoretical analysis and potentially extensive software modification. Hence, the development of low-level error-bounded approximate computing strategies that can be introduced into any high-performance computing application without requiring additional analysis or significant software alterations is desirable. In this paper, we provide a contribution in this direction by proposing a general framework for designing error-bounded approximate computing strategies and apply it to the dot product kernel to develop \bf qdot---an error-bounded approximate dot product kernel. Following the introduction of qdot, here we perform a theoretical analysis that yields a deterministic bound on the relative approximation error introduced by qdot. Empirical tests are performed to illustrate the tightness of the derived error bound and to demonstrate the effectiveness of qdot on a synthetic dataset, as well as two scientific benchmarks---the conjugate gradient (CG) and power methods. In some instances, using qdot for the dot products in CG can result in many components being quantized to half precision without increasing the iteration count required for convergence to the same solution as CG using a double precision dot product.

97 MATHEMATICS AND COMPUTING↗