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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Virtual Element Method for the Full MHD system

In this manuscript we present a novel discretization for the incompressible MHD system. Our approach follows the framework of the Virtual Element Method and offers two main advantages. The method can be implemented in unstructured meshes making it highly versatile and capable of handling a wide array of problems involving interfaces, free-boundaries or adaptive refinements on the mesh. The second advantage involves the divergence of the magnetic field, our approach guarantees that it remains solenoidal. We include a theoretical proof of the condition on the magnetic field as well as energy estimates and a well-posedness study. The latter sheds light as to the stability properties of the method.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Coupling of MCNP and ABAQUS for Reactor Multiphysics (MARM) [Slides]

Summer 2021 Internship Objectives: Learn MCNP (Monte Carlo N-Particle) transport code - Unstructured Mesh & Burnup Features, and Implement Python Code for MARM framework - MARM = MCNP and Abaqus based Reactor Multiphysics. Slides provide background and details for achieving learning objectives, as well as ideas for future work in the area.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Truchas-Flow

The truchas flow algorithm is designed for simulating the behavior of incompressible (but not necessarily constant density) fluids on unstructured meshes while allowing for solid and void phases to serve as moving boundaries. The interface between fluids, solids and void is tracked via a geometric volume of fluid approach. This allows for a single fluid formulation of the governing equations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Athena-I Modeling with MCNP6.3 [Poster]

This objective of this project is to develop a model of the Athena-I experiment by creating unstructured mesh (UM) geometries in CUBIT and to perform calculations of the models in Monte Carlo N-Particle (MCNP) transport code version 6.3 (MCNP6.3).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Oktavian Modeling and Analysis with MCNP6.3 [Poster]

Project Goals: Apply MCNP6 variance reduction (VR) techniques to improve the Oktavian benchmark experiment calculations; Verify VR methods and electron transport with MCNP6.3 unstructured mesh (UM) geometry. All MCNP calculations in this poster are for mode n, p, e.

42 ENGINEERING↗
DOE OSTI2023

Niowave Neutron Source Converter: Lead-Bismuth-Eutectic (LBE) Windowless Target Design and Evaluation

Los Alamos National Laboratory (LANL) is working with Niowave on the design and evaluation of their lead-bismuth-eutectic (LBE) windowless target (i.e., neutron source converter). Niowave plans to use 200 kW electron beam at 40 MeV beam energy to produce neutrons by photonuclear reaction with LBE. Then, the neutrons undergo fission at the surrounding uranium target assembly (UTA) to produce Molybdenum 99 (Mo-99) as a fission product, which eventually decays to Technetium-99 (Tc-99m). Tc-99m is one of important radioisotopes that is used for medical diagnostics. LANL conducted 3D multiphysics analysis for the Niowave neutron converter design and provided design assessment in thermohydraulic aspects. LANL conducted radiation transport calculations using Monte-Carlo N-Particle (MCNP) code with unstructured meshing scheme. The 3D volumetric heating profiles in the LBE and Stainless-Steel (SS) housing were imported into multiphase computational fluid dynamics (CFD) to obtain 3D temperature profiles of LBE and SS through conjugate heat transfer (CHT) analysis. The key findings are: LBE film thickness at the center of the beam is approximately 1.6 cm with a maximum LBE velocity of approximately 1.8 m/s, which is below a 2 m/s limit to avoid erosion issues on supporting structures; Heat deposition in the LBE peaks at ~1 cm depth from the LBE free-surface because of the forward interactions of electron, photon, and neutron with LBE; LBE maximum temperature is ~360 °C, which is below LBE evaporation initiative temperature, ~450 °C; LBE-SS interface temperature is ~350 °C, which is below the safety thermal limit to prevent severe corrosion on SS. The results indicate that Niowave’s neutron converter design satisfies both hydraulic and thermal criteria for safe operation. By virtue of such computational analysis, Niowave can move toward establishing an experimental setup to experimentally test their LBE neutron converter. The following sections describe the detailed work done by LANL.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Two MCNP Models for Computational Performance Benchmarking

The purpose of this report is to describe two non-trivial MCNP models and execution configurations that are suitable to characterizing computational performance. To that end, this report uses a constructive solid geometry (CSG) representation of the Oak Ridge National Laboratory (ORNL) Pool Critical Assembly (PCA) [1–3] to perform a k-eigenvalue calculation and an unstructured mesh (UM) representation of the International Commission on Radiological Protection (ICRP) publication 145 (ICRP145) male human phantom [4, 5] to perform a fixed-source calculation assuming that a 1 MeV photon source is distributed throughout the phantom’s liver.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Heating, DPA, and He production in the Proton Beam Window Assembly for the Second Target Station

This report details the analysis of the heating rates, dpa values, and He production in the Proton Beam Window (PBW), the assembly around the PBW, and the shielding above it. Neutronics calculations use a detailed Unstructured Mesh (UM) converted from the solid CAD model of the PBW design of June 2022. The rest of the geometry is taken from the CSG master model (as of June 2022). For this analysis, a proton beam profile with a footprint of ≈90 cm 2 has been used. This proton profile was created by a Ring to Second Target magnet array configuration using octupole magnets. Maximum values in the PBW (aluminum) are 5.4 J/cc/pulse heating rate, 1.1 dpa per year, and 660 appm He-production per year. These results within ~ 10% with the previous calculations from June 2020. Average heating and dpa rates have been calculated for each component in the PBW assembly and will be used to evaluate and design the cooling needs and make decisions on removable components for the PBW. It is desirable to optimize and simplify the PBW design by only replacing components that are at the end of their lifetime. Additional analysis for the energy deposition in the PBW is performed for two super-Gaussian beam profiles with a footprint of 60 cm 2 and 30 cm 2 respectively. Also, an estimate of the PBW lifetime is performed for two realistic quadrupole beam profiles.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Processing MCNP Elemental Edit Outputs

The Monte Carlo N-Particle (MCNP) transport code version 6 (also known as MCNP6) has the capability for tracking particles on unstructured mesh (UM) geometry models embedded into constructive solid geometry (CSG) cells. A UM geometry is a collection of elements representing a solid geometry. The first step of MCNP UM modeling is using other software packages to create a finite element mesh representation of a solid 3D geometry. Computer-aided design (CAD) or computer-aided manufacturing (CAM) software is typically used to create a solid geometry model, which is later imported into mesh generation software to create a UM model. The MCNP UM feature was originally designed for models generated by the Abaqus/CAE software. The MCNP code version 6.0 and later can process UM models formatted as Abaqus input files. MCNP can process a UM model consisting of several different element types including linear tetrahedral or hexahedral elements and calculate quantities of interest such as flux and energy deposition at elements. An MCNP UM simulation provides high-fidelity elemental edit (i.e., tally) outputs, which can be further used in multiphysics calculations. The MCNP UM feature was used for multiphysics simulations where quantities of interest calculated by MCNP are used as inputs for heat transfer calculations in Abaqus. MCNP6.3 can produce two types of elemental edit output (EEOUT) file formats: ASCII and HDF5. An EEOUT file type must be requested on an EMBED card while output type (flux or energy deposition) must be requested on an EMBEE card. We wrote Python3 scripts to extract energy deposition values in an ASCII or HDF5 EEOUT file and compute a heat flux profile for an Abaqus heat transfer calculation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Sandia Toolkit Manual (V.5.21.1)

This report provides documentation for the Sandia Toolkit (STK) modules. STK modules are intended to provide infrastructure that assists the development of computational engineering software such as finite-element analysis applications. STK includes modules for unstructured-mesh data structures, reading/writing mesh files, geometric proximity search, transfers, MPMD coupling support, and various other utilities. This document contains a chapter for each module, and each chapter contains overview descriptions and usage examples. Usage examples are primarily code listings which are generated from working test programs that are included in the STK code-base. A goal of this approach is to ensure that the usage examples will not fall out of date.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Sandia Toolkit Manual Version 5.15.6

This report provides documentation for the Sandia Toolkit (STK) modules. STK modules are intended to provide infrastructure that assists the development of computational engineering software such as finite-element analysis applications. STK includes modules for unstructured-mesh data structures, reading/writing mesh files, geometric proximity search, and various utilities. This document contains a chapter for each module, and each chapter contains overview descriptions and usage examples. Usage examples are primarily code listings which are generated from working test programs that are included in the STK code-base. A goal of this approach is to ensure that the usage examples will not fall out of date.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Neutronics Analysis of Heating and DPA of the Proton Beam Window and Core Vessel Nozzle Neutronics Heating Analysis for the Second Target Station

This reports details the analysis of the heating rates, dpa values and He production in the Proton Beam Window (PBW) assembly and the Core Vessel (CV) nozzle for the preliminary design for the Second Target Station (STS). This analysis focuses on the heating rates in the cooled beltline component of the CV. Neutronics calculations use a detailed unstructured mesh (UM) converted from a CAD model in SpaceClaim of the PBW and CV design of February 2024. The rest of the geometry is taken from the STS master model in constructive solid geometry (CSG). Heating is evaluated for four beam profiles, which are chosen as the most likely to produce the highest heating in the beltline component. The maximum energy deposition values in the beltline are 0.02-0.08 J/cc/pulse, depending on the beam profile. Detailed point cloud data of the full UM geometry is made available for mechanical analysis. The maximum dpa rate in the beltline steel is approximately 0.02 dpa/year, which corresponds to a lifetime of 3,000 years. These results are in good agreement with previous results for the CV. For the PBW itself, maximal heating, dpa, and He production rates are very similar to the previous analysis.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Coupled Target-Beam-Moderator Optimization for the Second Target Station

This report describes the results for a coupled target-beam-moderator optimization analysis for the Second Target Station (STS) at ORNL's Spallation Neutron Source. This study is a continuation of the optimization analysis for the moderators in the preliminary design of STS performed in 2022. In the 2022 analysis the dimensions of the moderators are parameterized, while the target and the proton beam profile are kept constant. In this analysis the target height and the proton beam profile are added as parameters. This allows to study the coupled effects of changing target, moderator and beam dimensions. Similar to the 2022 analysis, this work is performed with an automated optimization workflow that uses the optimization toolbox DAKOTA, parameterized geometries in CREO and SpaceClaim, the unstructured mesh generation in Attila4MC, and the particle transport code MCNP6.2©. This workflow enables an efficient optimization using high-fidelity geometries. The main conclusions of this analysis are the following: • Coupled beam-target-moderator optimization provides a few additional percent performance gain over stand-alone moderator optimization. • The moderator performance is not very sensitive to the target height (between ≈60 and ≈80 mm) as long as the beam profile is chosen adequately. • The moderator performance is sensitive to the choice of beam spatial standard deviations, even when the footprint is kept constant. • The optimal moderator radius is the same for a beam footprint of 30 cm 2 , 62.5 cm 2 , and 90 cm 2 . Also the slope of the super-gaussian beam profile does not significantly impact the optimal moderator radius. • The optimal parameters and sensitivities are very similar to the 2022 optimization analysis. These results only indicate a a difference in the optimal radius of the cylindrical moderator, however, this has been corrected in the final design moderator optimization. The main purpose of this report is to document the simulations, results and lessons learned. The most impactful results are summarized in. We also note that the target geometry used in this work is not the final design.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

A Streamlined, Open Source Neutronics Toolkit for Fusion Reactor Design (Final Report)

Final report for the DOE Fusion Energy Sciences project titled "A Streamlined, Open Source Neutronics Toolkit for Fusion Reactor Design". This project developed unstructured mesh tracking capabilities in OpenMC as well as two shutdown dose rate methodologies, and performed extensive performance improvements and validation of the open source Monte Carlo code, OpenMC.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Scientific Data Compression for Large Scale Computational Fluid Dynamics (CFD) Simulations

This Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and General Electric (GE) investigated methods for reducing the size of large computational fluid dynamics (CFD) simulation datasets using scientific data compression techniques. The work focused on adapting the MultiGrid Adaptive Reduction of Data (MGARD) compression framework and integrating it with high-performance I/O and visualization tools used in CFD workflows. MGARD uses hierarchical multilevel decomposition to enable error-controlled compression of floating-point scientific data while preserving quantities of interest. During the project, MGARD compression was integrated with the ADIOS I/O framework and visualization tools such as ParaView to enable efficient storage, transfer, and analysis of simulation data. The collaboration also explored approaches for improving compression performance for CFD data defined on unstructured meshes. Results demonstrate that scientific data compression can significantly reduce storage requirements and improve data management for large-scale CFD simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Comparing the Performance of Julia on CPUs versus GPUs and Julia-MPI versus Fortran-MPI: a case study with MPAS-Ocean (Version 7.1)

Abstract. Some programming languages are easy to develop at the cost of slow execution, while others are fast at runtime but much more difficult to write. Julia is a programming language that aims to be the best of both worlds – a development and production language at the same time. To test Julia's utility in scientific high-performance computing (HPC), we built an unstructured-mesh shallow water model in Julia and compared it against an established Fortran-MPI ocean model, the Model for Prediction Across Scales–Ocean (MPAS-Ocean), as well as a Python shallow water code. Three versions of the Julia shallow water code were created: for single-core CPU, graphics processing unit (GPU), and Message Passing Interface (MPI) CPU clusters. Comparing identical simulations revealed that our first version of the Julia model was 13 times faster than Python using NumPy, where both used an unthreaded single-core CPU. Further Julia optimizations, including static typing and removing implicit memory allocations, provided an additional 10–20× speed-up of the single-core CPU Julia model. The GPU-accelerated Julia code was almost identical in terms of performance to the MPI parallelized code on 64 processes, an unexpected result for such different architectures. Parallelized Julia-MPI performance was identical to Fortran-MPI MPAS-Ocean for low processor counts and ranges from 2× faster to 2× slower for higher processor counts. Our experience is that Julia development is fast and convenient for prototyping but that Julia requires further investment and expertise to be competitive with compiled codes. We provide advice on Julia code optimization for HPC systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The effects of spatial and temporal resolution of gridded meteorological forcing on watershed hydrological responses

Abstract. Meteorological forcing plays a critical role in accurately simulating the watershed hydrological cycle. With the advancement of high-performance computing and the development of integrated watershed models, simulating the watershed hydrological cycle at high temporal (hourly to daily) and spatial resolution (tens of meters) has become efficient and computationally affordable. These hyperresolution watershed models require high resolution of meteorological forcing as model input to ensure the fidelity and accuracy of simulated responses. In this study, we utilized the Advanced Terrestrial Simulator (ATS), an integrated watershed model, to simulate surface and subsurface flow and land surface processes using unstructured meshes at the Coal Creek Watershed near Crested Butte (Colorado). We compared simulated watershed hydrologic responses including streamflow and distributed variables such as evapotranspiration, snow water equivalent (SWE), and groundwater table driven by three publicly available, gridded meteorological forcings (GMFs) – Daily Surface Weather and Climatological Summaries (Daymet), the Parameter-elevation Regressions on Independent Slopes Model (PRISM), and the North American Land Data Assimilation System (NLDAS). By comparing various spatial resolutions (ranging from 400 m to 4 km) of PRISM, the simulated streamflow only becomes marginally worse when spatial resolution of meteorological forcing is coarsened to 4 km (or 30 % of the watershed area). However, the 4 km-resolution has much worse performance than finer resolution in spatially distributed variables such as SWE. Using the temporally disaggregated PRISM, we compared models forced by different temporal resolutions (hourly to daily), and sub-daily resolution preserves the dynamic watershed responses (e.g., diurnal fluctuation of streamflow) that are absent in results forced by daily resolution. Conversely, the simulated streamflow shows better performance using daily resolution compared to that using sub-daily resolution. Our findings suggest that the choice of GMF and its spatiotemporal resolution depends on the quantity of interest and its spatial and temporal scale, which may have important implications for model calibration and watershed management decisions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

FunMC^2: A Filter for Uncertainty Visualization of Marching Cubes on Multi-Core Devices

Visualization is an important tool for scientists to extract understanding from complex scientific data. Scientists need to understand the uncertainty inherent in all scientific data in order to interpret the data correctly. Uncertainty visualization has been an active and growing area of research to address this challenge. Algorithms for uncertainty visualization can be expensive, and research efforts have been focused mainly on structured grid types. Further, support for uncertainty visualization in production tools is limited. In this paper, we adapt an algorithm for computing key metrics for visualizing uncertainty in Marching Cubes (MC) to multi-core devices and present the design, implementation, and evaluation for a Filter for uncertainty visualization of Marching Cubes on Multi-Core devices (FunMC2). FunMC2 accelerates the uncertainty visualization of MC significantly, and it is portable across multi-core CPUs and GPUs. Evaluation results show that FunMC2 based on OpenMP runs around 11× to 41× faster on multi-core CPUs than the corresponding serial version using one CPU core. FunMC2 based on a single GPU is around 5× to 9× faster than FunMC2 running by OpenMP. Moreover, FunMC2 is flexible enough to process ensemble data with both structured and unstructured mesh types. Furthermore, we demonstrate that FunMC2 can be seamlessly integrated as a plugin into ParaView, a production visualization tool for post-processing.

Wang, Jay↗