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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 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

PythonFOAM: In-situ data analyses with OpenFOAM and Python

Here, we outline the development of a general-purpose Python-based data analysis tool for OpenFOAM. Our implementation relies on the construction of OpenFOAM applications that have bindings to data analysis libraries in Python. Double precision data in OpenFOAM is cast to a NumPy array using the NumPy C-API and Python modules may then be used for arbitrary data analysis and manipulation on flow-field information. We highlight how the proposed wrapper may be used for an in-situ online singular value decomposition (SVD) implemented in Python and accessed from the OpenFOAM solver PimpleFOAM. Here, 'in-situ' refers to a programming paradigm that allows for a concurrent computation of the data analysis on the same computational resources utilized for the partial differential equation solver. In addition, to demonstrate parallel deployments, we deploy a distributed SVD, which collects snapshot data across the ranks of a distributed simulation to compute the global left singular vectors. Crucially, both OpenFOAM and Python share the same message passing interface (MPI) communicator for this deployment which allows Python objects and functions to exchange NumPy arrays across ranks. Subsequently, we provide scaling assessments of this distributed SVD on multiple nodes of Intel Broadwell and KNL architectures for canonical test cases such as the large eddy simulations of a backward facing step and a channel flow at friction Reynolds number of 395. Finally, we demonstrate the deployment of a deep neural network for compressing the flow-field information using an autoencoder to demonstrate an ability to use state-of-the-art machine learning tools in the Python ecosystem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

MATAR: A performance portability and productivity implementation of data-oriented design with Kokkos

There is a need for simple, fast, and memory-efficient multidimensional data structures for dense and sparse storage that arise with numerical methods and in software applications. The data structures must perform equally well across multiple computer architectures, including CPUs and GPUs. For this purpose, we developed MATAR, a C++ software library that allows for simple creation and use of intricate data structures that is also portable across disparate architectures using Kokkos. Here, the performance aspect is achieved by forcing contiguous memory layout (or as close to contiguous as possible) for multidimensional and multi-size dense or sparse MATrix and ARray (hence, MATAR) types. Our results show that MATAR has the capability to improve memory utilization, performance, and programmer productivity in scientific computing. This is achieved by fitting more work into the available memory, minimizing memory loads required, and by loading memory in the most efficient order. This document describes the purpose of the work, the implementation of each of the data types, and the resulting performance both in some simple baseline test cases and in an application code.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

The effect of structural vacancies on the thermoelectric properties of (Cu₂Te){sub 1–x}(Ga₂Te₃) x

We have studied the effects of structural vacancies on the thermoelectric properties of the ternary compounds (Cu₂Te) 1–x (Ga₂Te₃) x (x=0.5, 0.55, 0.571, 0.6, 0.625, 0.667 and 0.75), which are solid solutions found in the pseudo-binary phase diagram for Cu₂Te and Ga₂Te₃, and possesses tunable structural vacancy concentrations. This materials system is not suitable due to the cost and scarcity of the constituent elements, but the vacancy behavior is well understood and will provide a valuable test case for other systems more suitable from the standpoint of cost and abundance of raw materials, which also possesses these vacancy features, but whose structural characterization is lacking at this stage. We find that the nominally defect free phase CuGaTe₂ possess the highest ZT (ZT=S²T/ρκ, where S is the Seebeck coefficient and ρ is the electrical resistivity κ is the thermal conductivity and T is the absolute temperature) which approaches 1 at 840 K and seems to continuously increase above this temperature. This result is due to the unexpectedly low thermal conductivity found for this material at high temperature. The low thermal conductivity was caused by strong Umklapp (thermally resistive scattering processes involving three phonons) phonon scattering. We find that due to the coincidentally strong scattering of carriers by the structural defects that higher concentrations of these features lead to poor electrical transport properties and decreased ZT. - Graphical abstract: Thermal conductivity and zT as a function of temperature for a series of compounds of the type (Cu₂Te) 1–x (Ga₂Te₃)x (x=0.5, 0.55, 0.571, 0.6, 0.625, 0.667 and 0.75). Highlights: • All the samples show p-type semiconducting behavior in the temperature dependence of the Seebeck and Hall coefficients. • The increased carrier concentration and the introduction of vacancies diminish the carrier mobility and power factor. • The low temperature k decreases significantly as the Ga₂Te₃ content increases due to increasing point defects. • The highest ZT ~ 1.0 at 840 K among the samples in this study was found in CuGaTe₂, which contains no vacancies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Twinning and martensitic transformations in nickel-enriched 304 austenitic steel during tensile and indentation deformations

Twinning and martensitic transformation have been investigated in nickel-enriched AISI 304 stainless steel subjected to tensile and indentation deformation. Using electron backscatter diffraction (EBSD), the morphology of alpha- and epsilon-martensite and the effect of grain orientation to load axis on phase and structure transformations were analyzed in detail. It was found that the twinning occurred less frequently under indentation than under tension; also, twinning was not observed in [001] and [101] grains. In tensile tests, the martensite particles preferably formed at the deformation twins, intersections between twins, or at twin-grain boundary intersections. Conversely, martensite formation in the indentation tests was not closely associated with twinning; instead, the majority of martensite was concentrated in the dense colonies near grain boundaries. Martensitic transformation seemed to be obstructed in the [001] grains in both tensile and indentation test cases. Under a tensile stress of 800 MPa, both alpha- and epsilon-martensite were found in the microstructure, but at 1100 MPa only -martensite presented in the specimen. Under indentation, alpha- and epsilon-martensite were observed in the material regardless of stress level.

Gussev, Maxim N↗
DOE OSTI2020

SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems

Here, we propose new symplectic networks (SympNets) for identifying Hamiltonian systems from data based on a composition of linear, activation and gradient modules. In particular, we define two classes of SympNets: the LA-SympNets composed of linear and activation modules, and the G-SympNets composed of gradient modules. Correspondingly, we prove two new universal approximation theorems that demonstrate that SympNets can approximate arbitrary symplectic maps based on appropriate activation functions. We then perform several experiments including the pendulum, double pendulum and three-body problems to investigate the expressivity and the generalization ability of SympNets. The simulation results show that even very small size SympNets can generalize well, and are able to handle both separable and non-separable Hamiltonian systems with data points resulting from short or long time steps. In all the test cases, SympNets outperform the baseline models, and are much faster in training and prediction. We also develop an extended version of SympNets to learn the dynamics from irregularly sampled data. This extended version of SympNets can be thought of as a universal model representing the solution to an arbitrary Hamiltonian system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Using the automata processor for fast pattern recognition in high energy physics experiments. A proof of concept

Here, we explore the Micron Automata Processor (AP) as a suitable commodity technology that can address the growing computational needs of pattern recognition in High Energy Physics (HEP) experiments. A toy detector model is developed for which an electron track confirmation trigger based on the Micron AP serves as a test case. Although primarily meant for high speed text-based searches, we demonstrate a proof of concept for the use of the Micron AP in a HEP trigger application.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Implementation and assessment of resolution-dependent elastic incoherent neutron scattering measurements at a backscattering spectrometer for probing relaxations in complex systems

We introduce the use of a higher-order reflection, Si(333), of the silicon analyzer crystals at a backscattering spectrometer to implement analysis of the relaxation dynamics in the samples. Here, this is achieved by monitoring the temperature dependence of the neutron scattering intensity perceived as elastic when measured using Si(111), Si(311), and Si(333) reflections. As a test case, we use a sample with well-characterized, but complex, relaxation pattern, where the non-Lorentzian relaxation function is known to exhibit a set of parameters specific to each temperature point and scattering momentum transfer (Q) value. Even for this very general relaxation pattern, characterized by no constrains on the relaxation function parameters, we successfully map the relaxation times measured using the present approach onto those previously obtained from the full analysis of the quasielastic neutron scattering signal. Analysis of the Q-specific activation energies becomes possible for the Q values accessible simultaneously through Si(111), Si(311), and Si(333) reflections (in the present case, 0.9 Å –1 < Q < 1.9 Å –1 ). While the dedicated neutron spectrometers for this type of analysis are yet to be constructed, we show that the present-day backscattering spectrometers can be employed, under certain conditions, to analyze temperature dependence of the relaxation time in the samples not amenable to traditional full analysis of the quasielastic neutron scattering signal.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

High-accuracy Geant4 simulation and semi-analytical modeling of nuclear resonance fluorescence

Nuclear resonance fluorescence (NRF) is a photonuclear interaction that enables highly isotope-specific measurements in both pure and applied physics scenarios. High-accuracy design and analysis of NRF measurements in complex geometries is aided by Monte Carlo simulations of photon physics and transport, motivating Jordan and Warren (2007) to develop the G4NRF codebase for NRF simulation in Geant4. In this work, we enhance the physics accuracy of the G4NRF code and perform improved benchmarking simulations. The NRF cross section calculation in G4NRF, previously a Gaussian approximation, has been replaced with a full numerical integration for improved accuracy in thick-target scenarios. A high-accuracy semi-analytical model of expected NRF count rates in a typical NRF measurement is then constructed and compared against G4NRF simulations for both simple homogeneous and more complex heterogeneous geometries. Furthermore, agreement between rates predicted by the semi-analytical model and G4NRF simulation is found at a level of ~1% in simple test cases and ~3% in more realistic scenarios, improving upon the ~20% level of the initial benchmarking study and establishing a highly-accurate NRF framework for Geant4.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Simulations of single- and two-phase shock tubes and gravity-driven wave problems with the RELAP-7 nuclear reactor system analysis code

Propagation of pressure waves in single and two-phase flows is of key interest for nuclear safety analyses, reactor design, construction and life extension of nuclear reactors. In this work, we assess the capabilities of RELAP-7, the next-generation nuclear reactor system code, to solve single- and two-phase flows developing pressure waves. RELAP-7 is based on a 7-equation, two-phase flow model, with distinct phasic pressures. It is well established that these hyperbolic conservation laws can develop shocks and discontinuities and thus, require a stabilization numerical method. The all-Mach flow Entropy Viscosity Method (EVM) is now employed in RELAP-7 as a viscous stabilization approach. In this paper, we briefly describe the implementation of the EVM and use open-literature test cases that exhibit pressure waves to show that the entropy viscosity method performs adequately for these single- and two-phase cases.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Weighted delta-tracking in scattering media

In this work, we expand the weighted delta-tracking routine to include a treatment for scattering. The weighted delta-tracking routine adds survival biasing to normal delta-tracking, improving problem figure of merit. In the original formulation of this method, only absorption events were considered. We have expanded the method to include scattering and investigated the method’s effectiveness with two test cases: a pressurized water reactor pin cell and a fast reactor pin cell. We compare the figure of merit for calculating infinite flux and total cross-section while incrementally changing the amount of weighted delta-tracking used. Thus, we find that this new weighted delta-tracking (WDT) routine has strong potential to improve the efficiency of fast reactor calculations, and may be useful for light water reactor calculations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A layered 2D computational framework: Theory and applications to nuclear fuel behavior

Nuclear fuel performance computer codes have been developed over the last 50 years to analyze fuel behavior under various operating conditions. Traditionally, these codes used quasi-two-dimensional (also commonly known as 1.5D) representations of the fuel rod, which model the rod using a set of one-dimensional axisymmetric models that represent the behavior at specific axial positions on the rod. Modern fuel performance codes have the ability to investigate full three-dimensional (3D) effects and couple to other physics-based codes for true multiphysics simulations. However, with increasing complexity comes increasing computational costs. Many phenomena of interest involve azimuthally-varying behavior that cannot be represented using the aforementioned quasi-two-dimensional approach, but do not require the use of a full 3D model. To efficiently address these problems, there is a need for a computational framework that provides a compromise between the quasi-two-dimensional and full 3D models. In this paper, we present a new quasi-three-dimensional approach that represents the fuel as a set of 2D planar models that represent the behavior of the fuel cross-section at various axial positions. Presented here are the theory behind the methodology, test cases to illustrate proper implementation, and practical applications of its use in the BISON fuel performance code for a variety of cases in nuclear fuel analysis, such as fuel fracture, axial fuel relocation, and cladding distension and oxidation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Solid-to-Fluid Radiative Heat Transfer Modeling for System Analysis Module

System Analysis Module (SAM) is a system-level thermal hydraulics code being developed at Argonne National Laboratory for advanced nuclear reactor analysis. In addition to a wide range of interests from the advanced reactor design community, SAM has also been adopted by the United States Nuclear Regulatory Commission's suite of codes purposed for advanced reactor licensing. Nevertheless, the code is still under active development and new capabilities are being added to address various modeling and simulation challenges for advanced reactor analysis. One such phenomenon important to the thermal behavior of some advanced reactor concepts is radiative heat transfer (radHT). Conditions, such as high temperatures and long optical paths, increase the radiative contributions from solids and coolants alike. This paper discusses the development of radHT modeling in SAM and describes the new capabilities provided for thermal analysis. Depending on the geometry and temperatures of the system at hand, as well as the coolant in question, thermal transfer due to thermal radiation will vary dramatically. Therefore, the ability to model variable radiative systems was maintained as a priority during development of SAM radHT modeling. This newly developed simulation feature provides a flexible solid-to-fluid radiative heat transfer framework necessary for SAM to perform accurate analysis for advanced reactor designs. Test cases are also presented and shown to match analytical solutions, which demonstrate the radHT model's efficacy.

Radiative heat transfer↗
DOE OSTI2022

Development of machine learning framework for interface force closures based on bubble tracking data

Interfacial force closures in the two-fluid model play a critical role for the predictive capabilities of void fraction distribution. However, the practices of interfacial force modeling have long been challenged by the inherent physical complexity of the two-phase flows. The rapidly expanding computational capabilities in the recent years have made high-fidelity data from the interface-captured direct numerical simulation become more available, and hence potential for data-driven interfacial force modeling has prevailed. In this work, we established a data-driven modeling framework integrated to the HZDR multiphase Eulerian-Eulerian framework for computational fluid dynamics simulations. The data-driven framework is verified in a benchmark problem, where a feedforward neural network managed to capture the non-linear mapping between bubble Reynolds number and drag coefficient and reproduce the void distribution resulting from the baseline model in the test case. The second focus is on utilizing the bubble tracking data set to form a closure for the bubble drag in the turbulent bubbly flow, in which the drag coefficient is set to be correlated with the bubble Reynolds number and the Eötvös number. Pseudo-steady state filtering in the Frenet Frame was carried out to obtain the drag coefficient from the turbulent bubbly flow data. The performance of the data-driven drag model is also examined through a case study, where improvement of model’s prediction near-wall is regarded necessary. In conclusion, discussion and further plans of investigation are provided.

42 ENGINEERING↗
DOE OSTI2026

Numerical implementation of the MOOSE subchannel module (SCM) algorithm

The subchannel module of moose (SCM), previously referred to as Pronghorn-SC, is a subchannel code designed to resolve single-phase flow fields and calculate the relevant flow variables, in nuclear reactor fuel-pin assemblies. The assemblies it models are: water-cooled, with bare fuel-pins in a square ducted, quadrilateral lattice and liquid-metal/water-cooled, with bare/wire-wrapped fuel pins in a hexagonal ducted, triangular lattice. Previous publications have presented the development, validation and verification of SCM. This work presents an comparative overview of the different solvers implemented within SCM. This includes the solver algorithm description, and a performance comparison. The two test cases chosen to demonstrate the solver performance were taken from the PSBT enthalpy mixing benchmark and the ORNL-19 pin benchmark.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

A nonhydrostatic formulation for MPAS-Ocean

The Model for Prediction Across Scales-Ocean (MPAS-Ocean) is an open-source, global ocean model and is one component of a family of climate models within the MPAS framework, including atmosphere, sea-ice, and land-ice models. Here, in this work, a new formulation for the ocean model is presented that solves the nonhydrostatic, incompressible Boussinesq equations on an unstructured, staggered, z-level grid. The introduction of this nonhydrostatic capability is necessary for the resolution of internal wave dynamics and large eddy simulations. Compared to the standard, hydrostatic formulation, a nonhydrostatic pressure solver and a vertical momentum equation are added, where the PETSc (Portable Extensible Toolkit for Scientific Computation) library is used for the inversion of a large sparse system for the nonhydrostatic pressure. Numerical results on a stratified seiche, internal solitary wave, overflow and lock-exchange test cases are presented, and the parallel efficiency of the code is evaluated using up to 1024 processors.

3D Poisson equation↗
DOE OSTI2025

A nonhydrostatic formulation for MPAS-Ocean

The Model for Prediction Across Scales-Ocean (MPAS-Ocean) is an open-source, global ocean model and is one component of a family of climate models within the MPAS framework, including atmosphere, sea-ice, and land-ice models. Here, in this work, a new formulation for the ocean model is presented that solves the nonhydrostatic, incompressible Boussinesq equations on an unstructured, staggered, z-level grid. The introduction of this nonhydrostatic capability is necessary for the resolution of internal wave dynamics and large eddy simulations. Compared to the standard, hydrostatic formulation, a nonhydrostatic pressure solver and a vertical momentum equation are added, where the PETSc (Portable Extensible Toolkit for Scientific Computation) library is used for the inversion of a large sparse system for the nonhydrostatic pressure. Numerical results on a stratified seiche, internal solitary wave, overflow and lock-exchange test cases are presented, and the parallel efficiency of the code is evaluated using up to 1024 processors.

3D Poisson equation↗