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DOE OSTI2017

Agile Software Components and Libraries for Large-Scale Reservoir Simulations

The objective of this project was for Sandia National Laboratories (SNL) and ExxonMobil Research Company (URC) to collaborate on the development and integration of numerical algorithms and software components for the solution of partial differential equations (PDEs) that address the high fidelity simulation of reservoir flows. SNL’s role was to consult and advise URC on the best practices and use of SNL opensource software components as part of URC's work to incorporate these components into their PDE-based URC reservoir simulation code. A desired outcome was for URC to leverage SNL’s open-source software components as within their own codes, rather than to have to develop from scratch those same capabilities themselves, and for SNL to harden and mature those software components based on usage and feedback from URC.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2010

Brain-Emulating Cognition and Control Architecture (BECCA) V1.0 beta

BECCA is a learning and control method based on the function of the human brain. The goal behind its creation is to learn to control robots in unfamiliar environments in a way that is very robust, similar to the way that an infant learns to interact with her environment by trial and error. As of this release, this software contains two simulations of BECCA controlling robots: one is a one degree-of-freedom spinner robot and the other is a 7 degree-of-freedom serial link arm with a terminal gripper. In addition, the software contains code that identifies synonyms in a untagged corpus of ASCII words. This last is a demonstration of BECCA's ability to generate abstract concepts from concrete experience. The BECCA simulation is coded so as to make it extensible to new applications. It is modular, object-oriented code in which the portions of the code that are specific to one simulation are easily separable from those portions that are the constant between implementations. BECCA makes very few assumptions about the robot and environment it is learning, and so is applicable to a wide range of learning and control problems.

Rohrer, Brandon↗

DOE OSTI2015

Monte Carlo reference data sets for imaging research: Executive summary of the report of AAPM Research Committee Task Group 195

The use of Monte Carlo simulations in diagnostic medical imaging research is widespread due to its flexibility and ability to estimate quantities that are challenging to measure empirically. However, any new Monte Carlo simulation code needs to be validated before it can be used reliably. The type and degree of validation required depends on the goals of the research project, but, typically, such validation involves either comparison of simulation results to physical measurements or to previously published results obtained with established Monte Carlo codes. The former is complicated due to nuances of experimental conditions and uncertainty, while the latter is challenging due to typical graphical presentation and lack of simulation details in previous publications. In addition, entering the field of Monte Carlo simulations in general involves a steep learning curve. It is not a simple task to learn how to program and interpret a Monte Carlo simulation, even when using one of the publicly available code packages. This Task Group report provides a common reference for benchmarking Monte Carlo simulations across a range of Monte Carlo codes and simulation scenarios. In the report, all simulation conditions are provided for six different Monte Carlo simulation cases that involve common x‐ray based imaging research areas. The results obtained for the six cases using four publicly available Monte Carlo software packages are included in tabular form. In addition to a full description of all simulation conditions and results, a discussion and comparison of results among the Monte Carlo packages and the lessons learned during the compilation of these results are included. This abridged version of the report includes only an introductory description of the six cases and a brief example of the results of one of the cases. This work provides an investigator the necessary information to benchmark his/her Monte Carlo simulation software against the reference cases included here before performing his/her own novel research. In addition, an investigator entering the field of Monte Carlo simulations can use these descriptions and results as a self‐teaching tool to ensure that he/she is able to perform a specific simulation correctly. Finally, educators can assign these cases as learning projects as part of course objectives or training programs.

Sechopoulos, Ioannis↗

DOE OSTI2010

Applications of TIERRAS for underground particle cascade simulations

In this communication we present some example applications of TIERRAS, a software package for the simulation of High Energy particle cascades underground and underwater. The examples illustrate how this package can be used to study the phenomenology of particle cascades from Extended Air Showers propagated several meters underground, including the effect of the surface ''albedo'' particles that are generated when a cascade reaches ground level. These up-going particles can have a measurable effect on surface or shallow underground detectors. Finally, to show the package ability ro perform simulations of particle cascades in ice, an application for neutrino radio detection is briefly introduced.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2016

SU-F-J-195: On the Performance of Four Dual Energy CT Formalisms for Extracting Proton Stopping Powers

Purpose: Dual energy CT can predict stopping power ratios (SPR) for ion therapy treatment planning. Several approaches have been proposed recently, however accuracy and practicability in a clinical workflow are unaddressed. The aim of this work is to provide a fair comparison of available approaches in a human-like phantom to find the optimal method for tissue characterization in a clinical situation. Methods: The SPR determination accuracy is investigated using simulated DECT images. A virtual human-like phantom is created containing 14 different standard human tissues. SECT (120 kV) and DECT images (100 kV and 140 kV Sn) are simulated using the software ImaSim. The single energy CT (SECT) stoichiometric calibration method and four recently published calibration-based DECT methods are implemented and used to predict the SPRs from simulated images. The difference between SPR predictions and theoretical SPR are compared pixelwize. Mean, standard deviation and skewness of the SPR difference distributions are used as measures for bias, dispersion and symmetry. Results: The average SPR differences and standard deviations are (0.22 ± 1.27)% for SECT, and A) (−0.26 ± 1.30)%, B) (0.08 ± 1.12)%, C) (0.06 ± 1.15)% and D) (−0.05 ± 1.05)% for the four DECT methods. While SPR prediction using SECT is showing a systematic error on SPR, the DECT methods B, C and D are unbiased. The skewness of the SECT distribution is 0.57%, and A) −0.19%, B) −0.56%, C) −0.29% and D) −0.07% for DECT methods respectively. Conclusion: The here presented DECT methods B, C and D outperform the commonly used SECT stoichiometric calibration. These methods predict SPR accurately without a bias and within ± 1.2% (68th percentile). This indicates that DECT potentially improves accuracy of range predictions in proton therapy. A validation of these findings using clinical CT images of real tissues is necessary.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Fault detection in PV systems

A practical fault detection approach for PV systems intended for online implementation is developed. The fault detection model here is built using artificial neural network. initially the photovoltaic system is simulated using MATLAB software and output power is collected for various combinations of irradiance and temperature. Data is first collected for normal operating condition and then four types of faults are simulated and data are collected for faulty conditions. Four faults are considered here and they are: Line to Line faults with a small voltage difference, Line to line faults with a large voltage difference, degradation fault and open-circuit fault. This data is then used to train the neural network and to develop the fault detection model. The fault detection model takes irradiance, temperature and power as the input and accurately gives the type of fault in the PV system as the output. This system is a generalized one as any PV module datasheet can be used to simulate the Photovoltaic system and also this fault detection system can be implemented online with the use of data acquisition system.

14 SOLAR ENERGY↗

DOE OSTI2021

Air Blast Mesh Sensitivity and Pressure Mapping Study

Nose cone structural and aerodynamic characteristics are essential for intelligent design of aircraft, spacecraft, and ballistic systems. Finite element analysis can be used to help understand the structural integrity and flight characteristics of different nose cones. A mesh sensitivity study was undertaken for a particular nose cone geometry that was used in tests at LANL facilities in order to confirm the integrity of the meshed geometry. A simple cone that best matched closed-form theoretical solutions was modeled, and received good correlation to the theory. Complexity was then added back to the nose cone. Parameters applied to the simple cone were then implemented in the nose cone geometry giving assurance of accuracy after the geometry was changed. Nose cone results averaged 6.3% error for radial displacement when compared with the theoretical. Hoop stress averaged 6.0% error and meridional stress averaged 5.7% error at the finest mesh level. Meshes showed signs of convergence when compared to all three theoretical solutions. Finally, pressure time-history data from LANL computational fluid dynamics simulations was applied to the surface of the final nose cone geometry. The pressure data was interpolated from pressure gauge locations onto nearby meshed elements, which allowed for FEA software to run simulations on the cone with the pressure data as a loading condition. The pressure mapping resulted in the ability to understand the nose cone’s rigid body motion that in turn can inform design of future nose cones.

42 ENGINEERING↗

DOE OSTI2018

Modeling of Crucial Process Parameters for the Continuous Improvement of Special Steels at the Stomana Plant

In this study, a heat transfer model that takes under consideration the micro-segregation phenomena and has been extensively deployed for solidification studies concerning the plant casters is also applied to show the casting speed and superheat effects upon internal soundness. Τhe solidification path of C45R, 42CrMo4, S355 grades was simulated using Thermocalc{sup ®} and DICTRA software in order to examine the phases formed in crucial process temperature windows for the casting. Coupled Heat Transfer/Simple Micro-Segregation Model software has been deployed to simulate the effect of cooling rate on solidus temperature, and solid fraction during solidification. The solidus temperature results computed by the HT/SM and DICTRA were found to be in good agreement. However, the differences in the predicted solidification fractions may derive from the fact that DICTRA uses an average cooling rate whereas the heat transfer SM model takes into account also the local cooling rates in the solidification progress.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Exascale Dislocation Simulator

ExaDiS, or Exascale Dislocation Simulator, is a set of software modules written to enable numerical simulations of large groups of moving and interacting dislocations, line defects in crystals responsible for crystal plasticity. By tracking time evolution of sufficiently large dislocation ensembles, ExaDiS predicts plasticity response and plastic strength of crystalline materials. ExaDiS is built around a library of core functions for Discrete Dislocation Dynamics method specifically written to perform efficient computations on GPUs.

Bertin, Nicolas↗

DOE OSTI2015

Combined Impact of Heterogeneous Lifetime and Gettering on Solar Cell Performance

We couple numerical process and device simulations to provide a framework for understanding the combined effects of as-grown wafer impurity distribution, processing parameters, and solar cell architecture. For this study, we added the Impurity-to-Efficiency simulator to Synopsys’ Sentaurus Process software using the Alagator Scripting Language. Our results quantify how advanced processing can eliminate differences in efficiency due to different as-grown impurity concentrations and due to different area fractions of defective wafer regions. We identify combinations of as-grown impurity distributions and process parameters that produce solar cells limited by point defects and those that are limited by precipitated impurities. Gettering targeted at either point defect or precipitate reduction can then be designed and applied to increase cell efficiency. We also visualize the post-processing iron and total recombination distributions in 2D maps of the wafer cross-section. PV researchers and companies can input their initial iron distributions and processing parameters into our software and couple the resulting process simulation results with a solar cell device design of interest to conduct their own analyses. The Alagator scripts we developed are freely available online at http://pv.mit.edu/impurity-to-efficiency-i2e-simulator-for-sentaurus-tcad/.

14 SOLAR ENERGY↗

DOE OSTI2023

M-Star ® Software Test and Verification

Savannah River Mission Completion (SRMC) currently manages the risk for retained hydrogen in the Defense Waste Processing Facility (DWPF) vessels by implementing a Retained Hydrogen Program. The current program relies on Sludge Batch (SB) 8 Gas Chromatograph data and on conservative assumptions concerning gas release and retention to determine allowable vessel Quiescent time (Q-time). The authors identified M-Star ® CFD as a software that could simulate processes such as fluid flow, heat transfer, species transport, chemical reactions, particle transport, and retained hydrogen gas release. Preliminary simulation results suggest that more realistic assumptions on gas retention and release may be feasible for the DWPF retained hydrogen program. Because of the desire to use the M-Star ® software to perform analyses that support nuclear safety, SRMC has requested Savannah River National Laboratory (SRNL) to upgrade the software classification level of M-Star ® CFD from level D to level A to perform analyses that support nuclear safety.

08 HYDROGEN↗

DOE OSTI2012

Visual Analytics for Roof Savings Calculator Ensembles

The Roof Savings Calculator (RSC) has been deployed for DOE as an industry-consensus, web-based tool for easily running complex building energy simulations. These simulations allow both homeowners and experts to determine building-specific cost and energy savings for modern roof and attic technologies. Using a database of over 3 million RSC simulations for different combinations of parameters, we have built a visual analytics tool to assist in the exploration and identification of features in the data. Since the database contains multiple variables, both categorical and continuous, we employ a coordinated multi-view approach that allows coordinated feature exploration through multiple visualizations at once. The main component of our system, a parallel coordinates view, has been adapted to handle large-scale, mixed data types as are found in RSC simulations. Other visualizations include map coordinated plots, high dynamic range (HDR) line plot rendering, and an intuitive user interface. We demonstrate these techniques with several use cases that have helped identify software and parametric simulation issues.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Low Size, Weight, and Power Neuromorphic Computing to Improve Combustion Engine Efficiency

Neuromorphic computing offers one path forward for AI at the edge. However, accessing and effectively utilizing a neuromorphic hardware platform is non-trivial. In this work, we present a complete pipeline for neuromorphic computing at the edge, including a small, inexpensive, low-power, FPGA-based neuromorphic hardware platform, a training algorithm for designing spiking neural networks for neuromorphic hardware, and a software framework for connecting those components. We demonstrate this pipeline on a real-world application, engine control for a spark-ignition internal combustion engine. We illustrate how we connect engine simulations with neuromorphic hardware simulations and training software to produce hardware-compatible spiking neural networks that perform engine control to improve fuel efficiency. We present initial results on the performance of these spiking neural networks and illustrate that they outperform open-loop engine control. We also give size, weight, and power estimates for a deployed solution of this type.

Schuman, Catherine↗

DOE OSTI2021

Cosmogenic background rejection for the sterile neutrino search with the Short-Baseline Neutrino Program Far Detector

As the Short-Baseline Neutrino Program Far Detector, the ICARUS liquid argon time-projection chamber will operate at shallow depth and therefore be exposed to the full surface flux of cosmic rays. This poses a problematic background to the sterile neutrino search. A direct way to reject this background is to surround the cryostat with a detector capable of tagging incident cosmic muons with high efficiency, the Cosmic Ray Tagging System (CRT). I have worked to develop the CRT detector hardware and the simulation and reconstruction software. This system, currently undergoing installation, is approximately 25% commissioned and taking data. I have developed a powerful method for cosmogenic background rejection utilizing the CRT and Photon Detection System. Results from a simulation based study, informed by CRT data, demonstrate the power of the technique in rejecting cosmogenic events with little adverse impact on the neutrino sample.

Hilgenberg, Christopher Michael↗

DOE OSTI2020

Quantum Approximate Optimization Algorithm in Python

This is software in Python that simulates the quantum approximate optimization algorithm (QAOA) as described in https://arxiv.org/abs/1411.4028. The software has customization that speeds up simulation and provides interface to the Rapid Optimization Library (ROL) developed at Sandia. SAND2019-8935 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

von Winckel, Gregory↗

DOE OSTI2013

Vertical-Axis Wind Turbine Mesh Generator

VAWTGen is a mesh generator for creating a finite element beam mesh of arbitrary vertical-axis wind turbines (VAWT). The software accepts input files specifying tower and blade structural and aerodynamic descriptions and constructs a VAWT using a minimal set of inputs. VAWTs with an arbitrary number of blades can be constructed with or without a central tower. Strut connections between the tower and blades can be specified in an arbitrary manner. The software also facilitates specifying arbitrary joints between structural components and concentrated structural tenns (mass and stiffness). The output files which describe the VAWT configuration are intended to be used with the Offshore Wind ENergy Simulation (OWENS) Toolkit software for structural dynamics analysis of VAWTs. Furthermore, VAWTGen is useful for visualizing output from the OWENS analysis software.

Owens, BrianC.↗

DOE OSTI2016

Quantifying the Impact of Nanoparticle Coatings and Non-uniformities on XPS Analysis: Gold/silver Core-shell Nanoparticles

Abstract or short description: Spectral modeling of photoelectrons can serve as a valuable tool when combined with X-ray photoelectron spectroscopy (XPS) analysis. Herein, a new version of the NIST Simulation of Electron Spectra for Surface Analysis (SESSA 2.0) software, capable of directly simulating spherical multilayer NPs, was applied to model citrate stabilized Au/Ag-core/shell nanoparticles (NPs). The NPs were characterized using XPS and scanning transmission electron microscopy (STEM) to determine the composition and morphology of the NPs. The Au/Ag-core/shell NPs were observed to be polydispersed in size, non-circular, and contain off-centered Au-cores. Using the average NP dimensions determined from STEM analysis, SESSA spectral modeling indicated that washed Au/Ag-core shell NPs were stabilized with a 0.8 nm l

Core-shell nanoparticles↗

DOE OSTI2017

Process Modeling and Validation for Metal Big Area Additive Manufacturing

Metal Big Area Additive Manufacturing (mBAAM) is a new additive manufacturing (AM) technology based on the metal arc welding. A continuously fed metal wire is melted by an electric arc that forms between the wire and the substrate, and deposited in the form of a bead of molten metal along the predetermined path. Objects are manufactured one layer at a time starting from the base plate. The final properties of the manufactured object are dependent on its geometry and the metal deposition path, in addition to depending on the basic welding process parameters. Computational modeling can be used to accelerate the development of the mBAAM technology as well as a design and optimization tool for the actual manufacturing process. We have developed a finite element method simulation framework for mBAAM using the new features of software ABAQUS. The computational simulation of material deposition with heat transfer is performed first, followed by the structural analysis based on the temperature history for predicting the final deformation and stress state. In this formulation, we assume that two physics phenomena are coupled in only one direction, i.e. the temperatures are driving the deformation and internal stresses, but their feedback on the temperatures is negligible. The experiment instrumentation (measurement types, sensor types, sensor locations, sensor placements, measurement intervals) and the measurements are presented. The temperatures and distortions from the simulations show good correlation with experimental measurements. Ongoing modeling work is also briefly discussed.

Simunovic, Srdjan↗