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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Upsampling Monte Carlo Reactor Simulation Tallies in Depleted Sodium-Cooled Fast Reactor Assemblies Using a Convolutional Neural Network

The computational demand of neutron Monte Carlo transport simulations can increase rapidly with the spatial and energy resolution of tallied physical quantities. Convolutional neural networks have been used to increase the resolution of Monte Carlo simulations of light water reactor assemblies while preserving accuracy with negligible additional computational cost. Here, we show that a convolutional neural network can also be used to upsample tally results from Monte Carlo simulations of sodium-cooled fast reactor assemblies, thereby extending the applicability beyond thermal systems. The convolutional neural network model is trained using neutron flux tallies from 300 procedurally generated nuclear reactor assemblies simulated using OpenMC. Validation and test datasets included 16 simulations of procedurally generated assemblies, and a realistic simulation of a European sodium-cooled fast reactor assembly was included in the test dataset. We show the residuals between the high-resolution flux tallies predicted by the neural network and high-resolution Monte Carlo tallies on relative and absolute bases. The network can upsample tallies from simulations of fast reactor assemblies with diverse and heterogeneous materials and geometries by a factor of two in each spatial and energy dimension. The network’s predictions are within the statistical uncertainty of the Monte Carlo tallies in almost all cases. This includes test assemblies for which burnup values and geometric parameters were well outside the ranges of those in assemblies used to train the network.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Multiple objective optimization in reliability demonstration test

Reliability demonstration tests are usually performed in product design or validation processes to demonstrate whether a product meets specified requirements on reliability. For binomial demonstration tests, the zero-failure test has been most commonly used due to its simplicity and use of minimum sample size to achieve an acceptable consumer’s risk level. However, this test can often result in unacceptably high risk for producers as well as a low probability of passing the test even when the product has good reliability. This paper explicitly explores the interrelationship between multiple objectives that are commonly of interest when planning a demonstration test and proposes structured decision-making procedures using a Pareto front approach for selecting an optimal test plan based on simultaneously balancing multiple criteria. Different strategies are suggested for scenarios with different user priorities and graphical tools are developed to help quantify the trade-offs between choices and to facilitate informed decision making. As a result, potential impacts of some subjective user inputs on the final decision are studied to offer insights and useful guidance for general applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Urban building energy modeling (UBEM) tools: A state-of-the-art review of bottom-up physics-based approaches

Regulations corroborate the importance of retrofitting existing building stocks or constructing new energy-efficient districts. There is, thus, a need for modeling tools to evaluate energy scenarios to better manage and design cities, and numerous methodologies and tools have been developed. Among them, Urban Building Energy Modelling (UBEM) tools allow the energy simulation of buildings at large scales. Choosing an appropriate UBEM tool, balancing the level of complexity, accuracy, usability, and computing needs, remains a challenge for users. The review focuses on the main bottom-up physics-based UBEM tools, comparing them from a user-oriented perspective. Five categories are used: (i) the required inputs, (ii) the reported outputs, (iii) the exploited workflow, (iv) the applicability of each tool, and (v) the potential users. Moreover, a critical discussion is proposed, focusing on interests and trends in research and development. The results highlighted major differences between UBEM tools that must be considered to choose the proper one for an application. Finally, barriers of adoption of UBEM tools include the needs of a standardized ontology, a common three-dimensional city model, a standard procedure to collect data, and a standard set of test cases. This feeds into future development of UBEM tools to support cities’ sustainability goals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

A fast non-Fourier method for Landau-fluid operators

An efficient and versatile non-Fourier method for the computation of Landau-fluid (LF) closure operators [Hammett and Perkins, Phys. Rev. Lett. 64, 3019 (1990)] is presented, based on an approximation by a sum of modified-Helmholtz-equation solves (SMHS) in configuration space. This method can yield fast-Fourier-like scaling of the computational time requirements and also provides a very compact data representation of these operators, even for plasmas with large spatial nonuniformity. As a result, the method can give significant savings compared with direct application of “delocalization kernels” [e.g., Schurtz et al., Phys. Plasmas 7, 4238 (2000)], both in terms of computational cost and memory requirements. The method is of interest for the implementation of Landau-fluid models in situations where the spatial nonuniformity, particular geometry, or boundary conditions render a Fourier implementation difficult or impossible. Systematic procedures have been developed to optimize the resulting operators for accuracy and computational cost. The four-moment Landau-fluid model of Hammett and Perkins has been implemented in the BOUT++ code using the SMHS method for LF closure. Excellent agreement has been obtained for the one-dimensional plasma density response function between driven initial-value calculations using this BOUT++ implementation and matrix eigenvalue calculations using both Fourier and SMHS non-Fourier implementations of the LF closures. The SMHS method also forms the basis for the implementation, which has been carried out in the BOUT++ code, of the parallel and toroidal drift-resonance LF closures. The method is a key enabling tool for the extension of gyro-Landau-fluid models [e.g., Beer and Hammett, Phys. Plasmas 3, 4046 (1996)] to codes that treat regions with strong profile variation, such as the tokamak edge and scrapeoff-layer.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Development of formalisms based on locally coupled open subsystems for calculations in molecular electronic structure and dynamics

Quantum embedding theories model a collection of interacting molecules as a set of subsystems, where each can be treated with a particular electronic structure method (wave function or density functional theory, for example); these theories can lead to computationally efficient and accurate algorithms. Motivated by challenges in the field, we previously described a formalism (that models two subsystems), which we call “locally coupled open subsystems” (LCOS), for the computation of ground-state energies, fractional electron-occupation numbers of the subsystems, and the size-consistent limit of subsystem dissociation. In this work we present the full (nonrelativistic) LCOS theory and the following extensions of our previous work: (i) the framework to study systems composed of multiple subsystems and a procedure to spin-adapt the auxiliary wave function that describes the partitioned system, so that its spin state matches that of the real system of interest; (ii) potential functionals and ideas to employ machine learning for the computation of ground-state densities and energies; (iii) formulation of two LCOS ground-state approaches where the fragments are assigned Kohn-Sham wave functions; and (iv) a time-dependent (TD) extension of these two ground-state formalisms in which the state of the subsystems evolves according to a unitary propagation; from this evolution we can extract TD electron populations of the fragments, for instance. We also discuss potential applications of the TD LCOS theory to linear photoabsorption and Raman spectroscopy. As a result, the developments presented in this work can lead to ground-state and TD electronic structure calculations where the computational scaling can be controlled, depending on the level of theory and the accuracy desired to model each one of the subsystems and their coupling.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2016

Multiscale analysis and computation for flows in heterogeneous media

Our work in this project is aimed at making fundamental advances in multiscale methods for flow and transport in highly heterogeneous porous media. The main thrust of this research is to develop a systematic multiscale analysis and efficient coarse-scale models that can capture global effects and extend existing multiscale approaches to problems with additional physics and uncertainties. A key emphasis is on problems without an apparent scale separation. Multiscale solution methods are currently under active investigation for the simulation of subsurface flow in heterogeneous formations. These procedures capture the effects of fine-scale permeability variations through the calculation of specialized coarse-scale basis functions. Most of the multiscale techniques presented to date employ localization approximations in the calculation of these basis functions. For some highly correlated (e.g., channelized) formations, however, global effects are important and these may need to be incorporated into the multiscale basis functions. Other challenging issues facing multiscale simulations are the extension of existing multiscale techniques to problems with additional physics, such as compressibility, capillary effects, etc. In our project, we explore the improvement of multiscale methods through the incorporation of additional (single-phase flow) information and the development of a general multiscale framework for flows in the presence of uncertainties, compressible flow and heterogeneous transport, and geomechanics. We have considered (1) adaptive local-global multiscale methods, (2) multiscale methods for the transport equation, (3) operator-based multiscale methods and solvers, (4) multiscale methods in the presence of uncertainties and applications, (5) multiscale finite element methods for high contrast porous media and their generalizations, and (6) multiscale methods for geomechanics. Below, we present a brief overview of each of these contributions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Optimization of Monte Carlo transport simulations in stochastic media

This paper presents an accurate and efficient approach to optimize radiation transport simulations in a stochastic medium of high heterogeneity, like the Very High Temperature Gas-cooled Reactor (VHTR) configurations packed with TRISO fuel particles. Based on a fast nearest neighbor search algorithm, a modified fast Random Sequential Addition (RSA) method is first developed to speed up the generation of the stochastic media systems packed with both mono-sized and poly-sized spheres. A fast neutron tracking method is then developed to optimize the next sphere boundary search in the radiation transport procedure. In order to investigate their accuracy and efficiency, the developed sphere packing and neutron tracking methods are implemented into an in-house continuous energy Monte Carlo code to solve an eigenvalue problem in VHTR unit cells. Comparison with the MCNP benchmark calculations for the same problem indicates that the new methods show considerably higher computational efficiency. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Application Note: Mixed Signal Simulation with Xyce™

This application note describes how the Xyce circuit simulator can be coupled with external simulators via either a Python-based interface that leverages the Python ctypes foreign function library or via the Verilog Procedural Interface (VPI). It also documents the usage of these interfaces on RHEL6 and RHEL7, with Python 2.6 or 2.7. These interfaces are still under development and may change in the future. So, a key purpose of this application note is to solicit feedback on these interfaces from both internal Sandia Xyce users and other performers on the DARPA Posh Open Source Hardware (POSH) program.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A Characteristic-Based Spectral Element Method for Moving-Domain Problems

Here, we introduce a characteristic-based numerical procedure for simulating incompressible flows in domains with moving boundaries. Our approach utilizes an operator-integration-factor splitting technique to help produce an efficient and stable numerical scheme. Using the spectral element method and an arbitrary Lagrangian-Eulerian formulation, we explore flows where the convective acceleration effects are non-negligible. Several examples, ranging from laminar to turbulent flows, are considered. Comparisons with a standard, semi-implicit time-stepping procedure illustrate the improved performance of our scheme.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Patient Positioning Based on a Radioactive Tracer Implanted in Patients With Localized Prostate Cancer: A Performance and Safety Evaluation

Purpose: To evaluate the performance and safety of a radiation therapy positioning system (RealEye) based on tracking a radioactive marker (Tracer) implanted in patients with localized prostate cancer. Methods and Materials: We performed a single-arm multi-institutional trial in 20 patients. The iridium-192 ({sup 192}Ir)-containing Tracer was implanted in the patient together with 4 standard gold seed fiducials. Patient prostate-related symptoms were evaluated with the International Prostate Symptom Score (IPSS) questionnaire. Computed tomography (CT) was performed for treatment planning, during treatment, and after treatment to evaluate the migration stability of the Tracer. At 5 treatment sessions, cone beam CT was performed to test the positioning accuracy of the RealEye. Results: The Tracer was successfully implanted in all patients. No device or procedure-related adverse events occurred. Changes in IPSS scores were limited. The difference between the mean change in Tracer-fiducial distance and the mean change in fiducial-fiducial distance was -0.39 mm (95% confidence interval [CI] upper boundary, -0.22 mm). The adjusted mean difference between Tracer position according to RealEye and the Tracer position on the CBCT for all patients was 1.34 mm (95% CI upper boundary, 1.41 mm). Conclusions: Implantation of the Tracer is feasible and safe. Migration stability of the Tracer is good. Prostate patients can be positioned and monitored accurately by using RealEye.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Towards quantum machine learning with tensor networks

Machine learning is a promising application of quantum computing, but challenges remain for implementation today because near-term devices have a limited number of physical qubits and high error rates. Motivated by the usefulness of tensor networks for machine learning in the classical context, we propose quantum computing approaches to both discriminative and generative learning, with circuits based on tree and matrix product state tensor networks, that could already have benefits with such near-term devices. The result is a unified framework in which classical and quantum computing can benefit from the same theoretical and algorithmic developments, and the same model can be trained classically then transferred to the quantum setting for additional optimization. Tensor network circuits can also provide qubit-efficient schemes in which, depending on the architecture, the number of physical qubits required scales only logarithmically with, or independently of the input or output data sizes. Here, we demonstrate our proposals with numerical experiments, training a discriminative model to perform handwriting recognition using a hybrid quantum-classical optimization procedure that could be carried out on quantum hardware today, and testing the noise resilience of the trained model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Optimally tuned starting point for single-shot GW calculations of solids

The dependence of ab initio many-body perturbation theory within the $\textit{GW}$ approximation on the eigensystem used in calculating quasiparticle corrections limits this method's predictive power. Here, we investigate the accuracy of the recently developed Wannier-localized optimally tuned screened range-separated hybrid (WOT-SRSH) functional as a generalized Kohn-Sham starting point for single-shot $\textit{GW}$ ($G_0W_0$) calculations for a range of semiconductors and insulators. Comparison to calculations based on well-established functionals, namely, PBE, PBE0, and HSE, as well as to self-consistent GW schemes and to experiment, shows that band gaps computed via $G_0W_0$@WOT-SRSH have a level of precision and accuracy that is comparable to that of more advanced methods such as quasiparticle self-consistent $\textit{GW}$ and eigenvalue self-consistent $\textit{GW}$. In this work, we also find that $G_0W_0$@WOT-SRSH improves the description of states deeper in the valence band manifold. Finally, we show that $G_0W_0$@WOT-SRSH significantly reduces the sensitivity of computed band gaps to ambiguities in the underlying WOT-SRSH tuning procedure.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Endovascular Management of Iatrogenic Native Renal Arterial Pseudoaneurysms

Purpose: Our purpose was to evaluate iatrogenic renal pseudoaneurysms, endovascular treatment, and outcomes. Methods: This retrospective study (2003-2011) reported the technical and clinical outcomes of endovascular therapy for renal pseudoaneurysms in eight patients (mean age, 46 (range 24-68) years). Renal parenchymal loss evaluation was based on digital subtraction angiography and computed tomography. Results: We identified eight iatrogenic renal pseudoaneurysm patients with symptoms of hematuria, pain, and hematoma after renal biopsy (n = 3), surgery (n = 3), percutaneous nephrolithotomy (n = 1), and endoscopic shock-wave lithotripsy (n = 1). In six patients, the pseudoaneurysms were small-sized (<20 mm) and peripherally located and were treated solely with coil embolization (n = 5). In one patient, coil embolization was preceded by embolization with 500-700 micron embospheres to control active bleeding. The remaining two patients had large-sized ({>=}50 mm), centrally located renal pseudoaneurysms treated with thrombin {+-} coils. Technical success with immediate bleeding cessation was achieved in all patients. There were no procedure-related deaths or complications (mean follow-up, 23.5 (range, 1-67) months). Conclusions: Treatment of renal pseudoaneurysms using endovascular approach is a relatively safe and viable option regardless of location (central or peripheral) and size of the lesions with minimal renal parenchymal sacrifice.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Hybrid geological modeling: Combining machine learning and multiple-point statistics

Accurately modeling and constructing a geologically realistic subsurface model remains an outstanding problem as the morphology controls the flow behaviors. Particularly, one of the pattern-based methods, namely cross-correlation based simulation, has been proved to be an effective way to reconstruct a realistic model, at both small and large scales. However, conditioning to point data in the large-scale problems is still a crucial issue in these algorithms, since there is always a trade-off between the quality of the realizations and the degree of point data reproduction. Specifically, it is not practical to build a training image (TI) which includes all the possibilities and variabilities. Therefore, finding a pattern that can represent the point data and, at the same time, preserving the connectivities is difficult. This leads to producing highly-connected realizations with a significant mismatch or poor models with a reasonable degree of point data reproduction. To accurately reproduce the densely distributed hard data, pixel-based methods can also produce some unrealistic artifacts around the hard data. In this paper, to overcome this challenge, however, we use pattern-based methods as they often produce more disconnected geobodies when dealing with dense hard data, and proposed a hybrid algorithm using the pattern-based methods and convolutional neural network (CNN). The trained CNN model is utilized to improve the quality of conditioning to point data for the original realizations generated by the pattern-based algorithm. As such, the mismatch locations are identified, and the same regions are used in the training of CNN to mimic the procedure through which a missing region can be filled. To evaluate the performance of the proposed hybrid algorithm, it is tested on cases with different dimensions and different numbers of facies. Then, the newly improved realizations are compared with the initial realizations generated by the pattern-based algorithm. The comparison is also conducted by the flow simulation test. And it indicates that the proposed hybrid algorithm can better reproduce the point data, while the connectivities are better preserved.

58 GEOSCIENCES↗
DOE OSTI2016

Bifurcation dynamics of the tempered fractional Langevin equation

Tempered fractional processes offer a useful extension for turbulence to include low frequencies. In this paper, we investigate the stochastic phenomenological bifurcation, or stochastic P-bifurcation, of the Langevin equation perturbed by tempered fractional Brownian motion. However, most standard tools from the well-studied framework of random dynamical systems cannot be applied to systems driven by non-Markovian noise, so it is desirable to construct possible approaches in a non-Markovian framework. We first derive the spectral density function of the considered system based on the generalized Parseval's formula and the Wiener-Khinchin theorem. Then we show that it enjoys interesting and diverse bifurcation phenomena exchanging between or among explosive-like, unimodal, and bimodal kurtosis. Therefore, our procedures in this paper are not merely comparable in scope to the existing theory of Markovian systems but also provide a possible approach to discern P-bifurcation dynamics in the non-Markovian settings.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

One-Dimensional Models for Time-Dependent Transport in Solid Cylinders

We present a set of 1-D transport models for solid cylinders of material irradiated with particles on the axial ends. The models are based on 1-D models originally developed for evacuated ducts with reflecting walls. The goal of this work is to show that 1-D models can be used for time-dependent transport in solid cylinders. A future use of this approach will apply these models to the high-energy density physics problem of a Marshak wave propagating down a cylindrical foam or other experiments. The models we present use a Galerkin procedure to project the radial dependence of the 3-D transport equation onto an expansion in terms of polynomials. Results demonstrate that for steady state problems with low scattering ratios, a three-basis function expansion can adequately capture the 3-D solution as computed via Monte Carlo. Smaller number of basis functions did not result in adequate solutions. As the radius of the cylinder increases, the 1-D model is more effective. Results from time dependent problems indicate that the 1-D models move particles too fast down the cylinder at early times, but are accurate on the order of 10 or more mean-free times. Here, our results indicate that 1-D models may be effective in modeling 2-D Marshak waves, but further work is necessary to answer this question.

42 ENGINEERING↗
DOE OSTI2019

Application Note: Mixed Signal Simulation with Xyce 6.11

This application note describes how Release 6.11 of the Xyce circuit simulator can be coupled with external simulators via either a Python-based interface that leverages the Python ctypes foreign function library or via the Verilog Procedural Interface (VPI). It also documents the usage of these interfaces on RHEL6 and RHEL7, with Python 2.6 or 2.7. These interfaces are still under development and may change in the future. So, a key purpose of this application note is to solicit feedback on these interfaces from both internal Sandia Xyce users and other performers on the DARPA Posh Open Source Hardware (POSH) program.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Maximum Likelihood Expectation-Maximization Algorithms Applied to Localization and Identification of Radioactive Sources with Recent Coded Mask Gamma Cameras

In this work, we tested Maximum likelihood expectation-maximization (MLEM) algorithms optimized for gamma imaging applications on two recent coded mask gamma cameras. We respectively took advantage of the characteristics of the GAMPIX and Caliste HD-based gamma cameras: noise reduction thanks to mask/anti-mask procedure but limited energy resolution for GAMPIX, high energy resolution for Caliste HD. One of our short-term perspectives is the test of MAPEM algorithms integrating specific prior values for the data to reconstruct adapted to the gamma imaging topic. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗