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DOE OSTI2017

Terrain - Umbra Package v. 1.0

This library contains modules that read terrain files (e.g., OpenFlight, Open Scene Graph IVE, GeoTIFF Image) and to read and manage ESRI terrain datasets. All data is stored and managed in Open Scene Graph (OSG). Terrain system accesses OSG and provides elevation data, access to meta-data such as soil types and enables linears, areals and buildings to be placed in a terrain, These geometry objects include boxes, point, path, and polygon (region), and sector modules. Utilities have been made available for clamping objects to the terrain and accessing LOS information. This assertion includes a managed C++ wrapper code (TerrainWrapper) to enable C# applications, such as OpShed and UTU, to incorporate this library.

Oppel, Fred↗

DOE OSTI2011

COLORS OF A SECOND EARTH. II. EFFECTS OF CLOUDS ON PHOTOMETRIC CHARACTERIZATION OF EARTH-LIKE EXOPLANETS

As a test bed for future investigations of directly imaged terrestrial exoplanets, we present the recovery of the surface components of the Earth from multi-band diurnal light curves obtained with the EPOXI spacecraft. We find that the presence and longitudinal distribution of ocean, soil, and vegetation are reasonably well reproduced by fitting the observed color variations with a simplified model composed of a priori known albedo spectra of ocean, soil, vegetation, snow, and clouds. The effect of atmosphere, including clouds, on light scattered from surface components is modeled using a radiative transfer code. The required noise levels for future observations of exoplanets are also determined. Our model-dependent approach allows us to infer the presence of major elements of the planet (in the case of the Earth, clouds, and ocean) with observations having signal-to-noise ratio (S/N) {approx}> 10 in most cases and with high confidence if S/N {approx}> 20. In addition, S/N {approx}> 100 enables us to detect the presence of components other than ocean and clouds in a fairly model-independent way. Degradation of our inversion procedure produced by cloud cover is also quantified. While cloud cover significantly dilutes the magnitude of color variations compared with the cloudless case, the pattern of color changes remains. Therefore, the possibility of investigating surface features through light-curve fitting remains even for exoplanets with cloud cover similar to Earth's.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Development of a Hybrid Lagrangian–Eulerian Model to Describe Spark-Ignition Processes at Engine-Like Turbulent Flow Conditions

With the engine technology moving toward more challenging (highly dilute and boosted) operation, spark-ignition processes play a key role in determining flame propagation and completeness of the combustion process. On the computational side, there is plenty of spark-ignition models available in literature and validated under conventional, stoichiometric spark ignition (SI) operation. Nevertheless, these models need to be expanded and developed on more physical grounds since at challenging operation they are not truly predictive. This paper reports on the development of a dedicated model for the spark-ignition event at nonquiescent, engine-like conditions, performed in the commercial CFD code converge. The developed methodology leverages previous findings that have expanded the use and improved the accuracy of Eulerian-type energy deposition models. In this work, the Eulerian energy deposition is coupled at every computational time-step with a Lagrangian-type evolution of the spark channel. Typical features such as spark channel elongation, stretch, and attachment to the electrodes are properly described to deliver realistic energy deposition along the channel during the entire ignition process. The numerical results are validated against schlieren images from an optical constant volume chamber and show the improvement in the simulation of the spark channel during the entire ignition event, with respect to the most commonly used energy deposition approach. Further developmental pathways are discussed to provide more physics-based features from the developed ignition model in the future.

42 ENGINEERING↗

DOE OSTI2020

Interactive Mobility Data Landscape

This is the companion repository to NREL's Interactive Mobility Landscape App (SEE: DOE Code ID 61291. NREL SWR-21-10). The Interactive Mobility Data Landscape is a project intended as a map through the previously uncharted, growing mobility data science field. This attempts to categorize most of the data sources, specifications, and tools in the field of mobility. It has been built by the National Renewable Energy Laboratory's (NREL) Mobility, Behavior, and Advanced Powertrains (MBAP) research group. The software for the interactive landscape is located at https://github.com/NREL/mobility_landscapeapp This repo includes all of the data and images specific to the Interactive Mobility Data Landscape.

Shankari, Kalyanaraman↗

DOE OSTI2025

Enhanced Monte Carlo Simulations for Electron Energy Loss Mitigation in Real-Space Nanoimaging of Thick Biological Samples and Microchips

High-resolution imaging using Transmission Electron Microscopy (TEM) is essential for applications such as grain boundary analysis, microchip defect characterization, and biological imaging. However, TEM images are often compromised by electron energy spread and other factors. In TEM mode, where the objective and projector lenses are positioned downstream of the sample, electron–sample interactions cause energy loss, which adversely impacts image quality and resolution. This study introduces a simulation tool to estimate the electron energy loss spectrum (EELS) as a function of sample thickness, covering electron beam energies from 300 keV to 3 MeV. Leveraging recent advances in MeV-TEM/STEM technology, which includes a state-of-the-art electron source with 2-picometer emittance, an energy spread of 3 × 10 -5 , and optimized beam characteristics, we aim to minimize energy spread. By integrating EELS capabilities into the BNL Monte Carlo (MC) simulation code for thicker samples, we evaluate electron beam parameters to mitigate energy spread resulting from electron–sample interactions. Based on our simulations, we propose an experimental procedure for quantitively distinguishing between elastic and inelastic scattering. The findings will guide the selection of optimal beam settings, thereby enhancing resolution for nanoimaging of thick biological samples and microchips.

36 MATERIALS SCIENCE↗

DOE OSTI2012

Steady state RANS simulations of temperature fluctuations in single phase turbulent mixing

Single phase turbulent mixing in nuclear power plant circuits where a strong temperature gradient is present is known to precipitate pipe failure due to thermal fatigue. Experiments in a square mixing channel offer the opportunity to study the phenomenon under simple and easily reproducible boundary conditions. Measurements of this kind have been performed extensively at the Paul Scherrer Inst. in Switzerland with a high density of instrumentation in the Generic Mixing Experiment (GEMIX). As a fundamental mixing phenomena study closely related to the thermal fatigue problem, the experimental results from GEMIX are valuable for the validation of CFD codes striving to accurately simulate both the temperature and velocity fields in single phase turbulent mixing. In the experiments two iso-kinetic streams meet at a shallow angle of 3 degrees and mix in a straight channel of square cross-section under various degrees of density, temperature, and viscosity stratification over a range of Reynolds numbers ranging from 5*10{sup 3} to 1*10{sup 5}. Conductivity measurements, using wire-mesh and wall sensors, as well as optical measurements, using particle image velocimetry, were conducted with high temporal and spatial resolutions (up to 2.5 kHz and 1 mm in the case of the wire mesh sensor) in the mixing zone, downstream of a splitter plate. The present paper communicates the results of RANS modeling of selected GEMIX tests. Steady-state CFD calculations using a RANS turbulence model represent an inexpensive method for analyzing large and complex components in commercial nuclear reactors, such as the downcomer and reactor pressure vessel heads. Crucial to real world applicability, however, is the ability to model turbulent heat fluctuations in the flow; the Turbulent Heat Flux Transport model developed by ANSYS CFX is capable, by implementation of a transport equation for turbulent heat fluxes, of readily modeling these values. Furthermore, the closure of the turbulent heat flux transport equation evokes a transport equation for the variance of the enthalpy. It is therefore possible to compare the modeled fluctuations of the liquid temperature directly with the scalar fluctuations recorded experimentally with the wire-mesh. Combined with a working Turbulent Heat Flux Transport model, complex mixing problems in large geometries could be better understood. We aim for the validation of Reynolds Stress based RANS simulations extended by the Turbulent Heat Flux Transport model by modeling the GEMIX experiments in detail. Numerical modeling has been performed using both BSL and SSG Reynolds Stress Models in a test matrix comprising experimental trials at the GEMIX facility. We expand on the turbulent mixing RANS CFD results of (Manera 2009) in a few ways. In the GEMIX facility we introduce density stratification in the flow while removing the characteristic large scale vorticity encountered in T-junctions and therefore find better conditions to check the diffusive conditions in the model. Furthermore, we study the performance of the model in a very different, simpler scalar fluctuation spectrum. The paper discusses the performance of the model regarding the dissipation of the turbulent kinetic energy and dissipation of the enthalpy variance. A novel element is the analyses of cases with density stratification. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2013

On the image in RHIC of AGS spin vector via the AtR

This note re-visits the transport of the spin nn0 vector, from its periodic orientation in the AGS, to the downstream end of the Blue and Yellow RHIC ring injection kickers via the AGS extraction system and the AtR line. The goal is to verify the optimal injection energy into RHIC, in matter of spin matching. This is done using newly computed OPERA 3-D field maps of the AGS cold snake, including a new setting of the cold snake solenoid as discussed in a companion Tech. Note (C-A/AP/485, July 2013), together with the machinery of the AGS and AtR models developed in the stepwise ray-tracing code Zgoubi. Computing tools and methods employed in these investigations are discussed as well, in order to facilitate possible further checks or investigations.

43 PARTICLE ACCELERATORS↗

DOE OSTI2015

A virtual simulator designed for collision prevention in proton therapy

Purpose: In proton therapy, collisions between the patient and nozzle potentially occur because of the large nozzle structure and efforts to minimize the air gap. Thus, software was developed to predict such collisions between the nozzle and patient using treatment virtual simulation. Methods: Three-dimensional (3D) modeling of a gantry inner-floor, nozzle, and robotic-couch was performed using SolidWorks based on the manufacturer’s machine data. To obtain patient body information, a 3D-scanner was utilized right before CT scanning. Using the acquired images, a 3D-image of the patient’s body contour was reconstructed. The accuracy of the image was confirmed against the CT image of a humanoid phantom. The machine components and the virtual patient were combined on the treatment-room coordinate system, resulting in a virtual simulator. The simulator simulated the motion of its components such as rotation and translation of the gantry, nozzle, and couch in real scale. A collision, if any, was examined both in static and dynamic modes. The static mode assessed collisions only at fixed positions of the machine’s components, while the dynamic mode operated any time a component was in motion. A collision was identified if any voxels of two components, e.g., the nozzle and the patient or couch, overlapped when calculating volume locations. The event and collision point were visualized, and collision volumes were reported. Results: All components were successfully assembled, and the motions were accurately controlled. The 3D-shape of the phantom agreed with CT images within a deviation of 2 mm. Collision situations were simulated within minutes, and the results were displayed and reported. Conclusions: The developed software will be useful in improving patient safety and clinical efficiency of proton therapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Sci-Sat AM: Radiation Dosimetry and Practical Therapy Solutions - 05: Not all geometries are equivalent for magnetic field Fano cavity tests

The coupling of MRI and radiation treatment systems for the application of magnetic resonance guided radiation therapy necessitates a reliable magnetic field capable Monte Carlo (MC) code. In addition to the influence of the magnetic field on dose distributions, the question of proper calibration has arisen due to the several percent variation of ion chamber and solid state detector responses in magnetic fields when compared to the 0 T case (Reynolds et al., Med Phys, 2013). In the absence of a magnetic field, EGSnrc has been shown to pass the Fano cavity test (a rigorous benchmarking tool of MC codes) at the 0.1 % level (Kawrakow, Med.Phys, 2000), and similar results should be required of magnetic field capable MC algorithms. To properly test such developing MC codes, the Fano cavity theorem has been adapted to function in a magnetic field (Bouchard et al., PMB, 2015). In this work, the Fano cavity test is applied in a slab and ion-chamber-like geometries to test the transport options of an implemented magnetic field algorithm in EGSnrc. Results show that the deviation of the MC dose from the expected Fano cavity theory value is highly sensitive to the choice of geometry, and the ion chamber geometry appears to pass the test more easily than larger slab geometries. As magnetic field MC codes begin to be used for dose simulations and correction factor calculations, care must be taken to apply the most rigorous Fano test geometries to ensure reliability of such algorithms.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Evaluating Automated Face Identity-Masking Methods with Human Perception and a Deep Convolutional Neural Network

Face de-identification (or “masking”) algorithms have been developed in response to the prevalent use of video recordings in public places. Here, we evaluated the success of face identity masking for human perceivers and a deep convolutional neural network (DCNN). Eight de-identification algorithms were applied to videos of drivers’ faces, while they actively operated a motor vehicle. These masks were pre-selected to be applicable to low-quality video and to maintain coarse information about facial actions. Humans studied high-resolution images to learn driver identities and were tested on their recognition of active drivers in low-resolution videos. Faces in the videos were either unmasked or were masked by one of the eight algorithms. When participants were tested immediately after learning (Experiment 1), all masks reduced identification, with six of eight masks reducing identification to extremely poor performance. In a second experiment, two of the most effective masks were tested after a delay of 7 or 28 days. The delay did not further reduce identification of the masked faces. In all masked conditions, participants maintained stringent decision criteria, with low confidence in recognition, further indicating the effectiveness of the masks. Next, the DCNN performed an identity-matching task between high-resolution images and masked videos—a task analogous to that done by humans. The pattern of accuracy for the DCNN mirrored some, but not all, aspects of human performance, highlighting the need to test the effectiveness of identity masking for both humans and machines. The DCNN was also tested on its ability to match identity between masked and unmasked versions of the same video, based only on the face. DCNN performance for the eight masks offers insight into the nature of the information in faces that is coded in these networks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

CIEL ∗ Ch color map for visualization and analysis of sea ice motion

The International Commission on Illumination (CIE) designed its color space to be perceptually uniform so that a given numerical change in the color code corresponds to perceived change in color. This color encoding is demon- strated to be advantageous in scientific visualization and analysis of vector fields. The specific application is analysis of ice motion in the Arctic where patterns in smooth monthly-averaged ice motion are seen. Furthermore, fractures occurring in the ice cover result in discontinuities in the ice motion. This vector jump in displacement can also be visualized. We then analyze modeled and observed fractures through the use of a metric on the color space, and image amplitude and phase metrics. Amplitude and phase met- rics arise from image registration that is accomplished by sampling images using space filling curves, thus reducing the image registration problem to the more reliable functional alignment problem. We demonstrate this through an exploration of the metrics to compare model runs to an observed ice crack.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Combining different 3-D global and regional seismic wave propagation solvers towards box tomography in the deep Earth

SUMMARY In previous publications, we presented a general framework, which we called ‘box tomography’, that allows the coupling of any two different numerical seismic wave propagation solvers, respectively outside and inside a target region, or ‘box’. The goal of such hybrid wavefield computations is to reduce the cost of computations in the context of full-waveform inversion for structure within the target region, when sources and/or receivers are located at large distances from the box. Previously, we had demonstrated this approach with sources and receivers outside the target region in a 2-D acoustic spherical earth model, and demonstrated and applied this methodology in the 3-D spherical elastic Earth in a continental scale inversion in which all stations were inside the target region. Here we extend the implementation of the approach to the case of a 3-D global elastic earth model in the case where both sources and stations are outside the box. We couple a global 3-D solver, SPECFEM3D_GLOBE, for the computation of the wavefield and Green’s functions in a reference 3-D model, with a regional 3-D solver, RegSEM, for the computation of the wavefield within the box, by means of time-reversal mirrors. We briefly review key theoretical aspects, showing in particular how only the displacement is needed to be stored at the boundary of the box. We provide details of the practical implementation, including the geometrical design of the mirrors, how we deal with different sizes of meshes in the two solvers, and how we address memory-saving through the use of B-spline compression of the recorded wavefield on the mirror. The proposed approach is numerically efficient but also versatile, since adapting it to other solvers is straightforward and does not require any changes in the solver codes themselves, as long as the displacement can be recovered at any point in time and space. We present benchmarks of the hybrid computations against direct computations of the wavefield between a source and an array of stations in a realistic geometry centred in the Yellowstone region, with and without a hypothetical plume within the ‘box’, and with a 1-D or a 3-D background model, down to a period of 20 s. The ultimate goal of this development is for applications in the context of imaging of remote target regions in the deep mantle, such as, for example, Ultra Low Velocity Zones.

Geochemistry & Geophysics↗

DOE OSTI2023

Annular Metallic Nuclear Fuel Informatics at 50 nm Resolution

U-10wt.% Zr (U-10Zr) based metallic fuel is the leading candidate for next-generation sodium cooled fast reactor in United States. Advanced post-irradiation characterization (from sub-nanometer to micrometer) helps to understand fuel microstructure and property change during irradiation, benefiting fuel qualification for commercial application. With high velocity image data generating method, an automatic way to extract the microstructural information quantitively can better serve the needs from post irradiation characterization. A trained machine learning model, named Decision Tree, is employed to categorize pores caused by fission gas release and to aid phase identification. This work presents a showcase of this approach on different irradiated U-10Zr metallic fuels. This quantitative data offers insights into the fission product migration and potentially thermal conductivity degradation. This information from machine learning will be fed into fuel design code for better prediction of fuel performance.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2015

RVA: A Plugin for ParaView 3.14

RVA is a plugin developed for the 64-bit Windows version of the ParaView 3.14 visualization package. RVA is designed to provide support in the visualization and analysis of complex reservoirs being managed using multi-fluid EOR techniques. RVA, for Reservoir Visualization and Analysis, was developed at the University of Illinois at Urbana-Champaign, with contributions from the Illinois State Geological Survey, Department of Computer Science and National Center for Supercomputing Applications. RVA was designed to utilize and enhance the state-of-the-art visualization capabilities within ParaView, readily allowing joint visualization of geologic framework and reservoir fluid simulation model results. Particular emphasis was placed on enabling visualization and analysis of simulation results highlighting multiple fluid phases, multiple properties for each fluid phase (including flow lines), multiple geologic models and multiple time steps. Additional advanced functionality was provided through the development of custom code to implement data mining capabilities. The built-in functionality of ParaView provides the capacity to process and visualize data sets ranging from small models on local desktop systems to extremely large models created and stored on remote supercomputers. The RVA plugin that we developed and the associated User Manual provide improved functionality through new software tools, and instruction in the use of ParaView-RVA, targeted to petroleum engineers and geologists in industry and research. The RVA web site (http://rva.cs.illinois.edu) provides an overview of functions, and the development web site (https://github.com/shaffer1/RVA) provides ready access to the source code, compiled binaries, user manual, and a suite of demonstration data sets. Key functionality has been included to support a range of reservoirs visualization and analysis needs, including: sophisticated connectivity analysis, cross sections through simulation results between selected wells, simplified volumetric calculations, global vertical exaggeration adjustments, ingestion of UTChem simulation results, ingestion of Isatis geostatistical framework models, interrogation of joint geologic and reservoir modeling results, joint visualization and analysis of well history files, location-targeted visualization, advanced correlation analysis, visualization of flow paths, and creation of static images and animations highlighting targeted reservoir features.

Source record↗

DOE OSTI2015

RVA. 3-D Visualization and Analysis Software to Support Management of Oil and Gas Resources

A free software application, RVA, has been developed as a plugin to the US DOE-funded ParaView visualization package, to provide support in the visualization and analysis of complex reservoirs being managed using multi-fluid EOR techniques. RVA, for Reservoir Visualization and Analysis, was developed as an open-source plugin to the 64 bit Windows version of ParaView 3.14. RVA was developed at the University of Illinois at Urbana-Champaign, with contributions from the Illinois State Geological Survey, Department of Computer Science and National Center for Supercomputing Applications. RVA was designed to utilize and enhance the state-of-the-art visualization capabilities within ParaView, readily allowing joint visualization of geologic framework and reservoir fluid simulation model results. Particular emphasis was placed on enabling visualization and analysis of simulation results highlighting multiple fluid phases, multiple properties for each fluid phase (including flow lines), multiple geologic models and multiple time steps. Additional advanced functionality was provided through the development of custom code to implement data mining capabilities. The built-in functionality of ParaView provides the capacity to process and visualize data sets ranging from small models on local desktop systems to extremely large models created and stored on remote supercomputers. The RVA plugin that we developed and the associated User Manual provide improved functionality through new software tools, and instruction in the use of ParaView-RVA, targeted to petroleum engineers and geologists in industry and research. The RVA web site (http://rva.cs.illinois.edu) provides an overview of functions, and the development web site (https://github.com/shaffer1/RVA) provides ready access to the source code, compiled binaries, user manual, and a suite of demonstration data sets. Key functionality has been included to support a range of reservoirs visualization and analysis needs, including: sophisticated connectivity analysis, cross sections through simulation results between selected wells, simplified volumetric calculations, global vertical exaggeration adjustments, ingestion of UTChem simulation results, ingestion of Isatis geostatistical framework models, interrogation of joint geologic and reservoir modeling results, joint visualization and analysis of well history files, location-targeted visualization, advanced correlation analysis, visualization of flow paths, and creation of static images and animations highlighting targeted reservoir features.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

RVA: A Plugin for ParaView 3.14

Source record↗

DOE OSTI2021

Quasiclassical Computations of Compton-Scattered Spectra

Quality X-ray sources are crucial to fundamental physics research, medical radiology, humanities research, and materials science. While synchrotron radiation (SR) facilities produce the state-of-the-art emissions with respect to brilliance and frequency tunability, the great expense required to build, maintain, and operate these structures greatly limits their accessibility to researchers. Much of the research conducted at SR facilities, however, may be conducted with inverse Compton sources (ICS). Accelerator-based Compton scattering light sources generate high-energy, high-brilliance emissions. Compton scattering is the process by which a photon scatters o? an electron. ICS offer an affordable, in-lab alternative to SR facilities. Even though SR facilities produce greater intensity emissions, Compton sources provide the same frequency tunability at the intensities suitable for the purposes of many researchers currently fighting for time at SR facilities, i.e., ICS provides intensities suitable for contrast imaging, X-ray fluorescence, X-ray-diffraction, and X-ray spectroscopy. The focus of this work is to create computational models to simulate Compton-scattered spectra. These models have been used to build a theoretical basis for methods of improving the quality of future Compton sources and to preform diagnostic analysis of existing light sources. The theoretical basis of each model is derived from first principles. The numerical methods employed by each model are defined. A full description of the various functionalities of each code will be addressed. Furthermore, an in-depth analysis of spectral bandwidth sources is discussed. The complex physics arising from an extremely high-intensity, nonlinear laser pulse is explored in detail. Methods of frequency modulation of the incident laser, i.e., a method for correcting the nonlinear broadening effects on the scattered spectrum, will also be discussed. The work will conclude with a an exploration of the ongoing research efforts regarding regimes of operation outside of the limits of these current models.

Johnson, Erik↗

DOE OSTI2016

Comparison of 3-D Bubble Reconstruction Algorithms

Obtaining 3-D bubble shape is important in studying bubble dynamics in two-phase flows, as well as in conducting high-fidelity measurement for code validation applications. With accurate shape information of individual bubbles, the time-averaged parameters including void fraction, interfacial area concentration and Sauter mean diameter can be obtained precisely. Such information will also benefit the development of advanced models of hydrodynamic forces such as the drag force and lift force, which are sensitive to the shape and size of bubbles. Limited by the optical property of gas-liquid system, the silhouette of a bubble along the camera axis is what one can obtain from typical high speed imaging systems. Assumptions have to be made to obtain the 3-D bubble shape from the 2-D images. In this paper, three different bubble reconstruction algorithms, using one or two camera views, are examined to assess their performances in the reconstruction process. (authors)

42 ENGINEERING↗