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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Application of the BISON Fuel Performance Code to the FUMEX-III Coordinated Research Project

INL recently participated in FUMEX-III, an International Atomic Energy Agency sponsored fuel modeling Coordinated Research Project. A main purpose of FUMEX-III is to compare code predictions to reliable experimental data. During the same time period, the INL initiated development of a new multidimensional (2D and 3D) multiphysics nuclear fuel performance code called BISON. Interactions with international fuel modeling researchers via FUMEX-III played a significant and important role in the BISON evolution, particularly influencing the selection of material and behavioral models which are now included in the code. BISON's ability to model integral fuel rod behavior did not mature until 2011, thus the only FUMEX-III case considered was the Riso3-GE7 experiment, which includes measurements of rod outer diameter following pellet clad mechanical interaction (PCMI) resulting from a power ramp late in fuel life. BISON comparisons to the Riso3-GE7 final rod diameter measurements are quite reasonable. The INL is very interested in participation in the next Fuel Modeling Coordinated Research Project and would like to see the project initiated as soon as possible.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2012

Experimental and Analytic Study on the Core Bypass Flow in a Very High Temperature Reactor

Core bypass flow has been one of key issues in the very high temperature reactor (VHTR) design for securing core thermal margins and achieving target temperatures at the core exit. The bypass flow in a prismatic VHTR core occurs through the control element holes and the radial and axial gaps between the graphite blocks for manufacturing and refueling tolerances. These gaps vary with the core life cycles because of the irradiation swelling/shrinkage characteristic of the graphite blocks such as fuel and reflector blocks, which are main components of a core's structure. Thus, the core bypass flow occurs in a complicated multidimensional way. The accurate prediction of this bypass flow and counter-measures to minimize it are thus of major importance in assuring core thermal margins and securing higher core efficiency. Even with this importance, there has not been much effort in quantifying and accurately modeling the effect of the core bypass flow. The main objectives of this project were to generate experimental data for validating the software to be used to calculate the bypass flow in a prismatic VHTR core, validate thermofluid analysis tools and their model improvements, and identify and assess measures for reducing the bypass flow. To achieve these objectives, tasks were defined to (1) design and construct experiments to generate validation data for software analysis tools, (2) determine the experimental conditions and define the measurement requirements and techniques, (3) generate and analyze the experimental data, (4) validate and improve the thermofluid analysis tools, and (5) identify measures to control the bypass flow and assess its performance in the experiment.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2012

Vorticity Preserving Flux Corrected Transport Scheme for the Acoustic Equations [Slides]

Long term research goals are to develop an improved cell-centered Lagrangian Hydro algorithm with the following qualities: 1. Utilizes Flux Corrected Transport (FCT) to achieve second order accuracy with multidimensional physics; 2. Does not rely on the one-dimensional Riemann problem; and 3. Implements a form of vorticity control. Short term research goals are to devise and implement a 2D vorticity preserving FCT solver for the acoustic equations on an Eulerian mesh: 1. Develop a flux limiting mechanism for systems of governing equations with symmetric wave speeds; 2. Verify the vorticity preserving properties of the scheme; and 3. Compare the performance of the scheme to traditional MUSCL-Hancock and other algorithms.

42 ENGINEERING↗
DOE OSTI2012

Numerically Tracking Contact Discontinuities with an Introduction for GPU Programming

We review some of the classic numerical techniques used to analyze contact discontinuities and compare their effectiveness. Several finite difference methods (the Lax-Wendroff method, a Multidimensional Positive Definite Advection Transport Algorithm (MPDATA) method and a Monotone Upstream Scheme for Conservation Laws (MUSCL) scheme with an Artificial Compression Method (ACM)) as well as the finite element Streamlined Upwind Petrov-Galerkin (SUPG) method were considered. These methods were applied to solve the 2D advection equation. Based on our results we concluded that the MUSCL scheme produces the sharpest interfaces but can inappropriately steepen the solution. The SUPG method seems to represent a good balance between stability and interface sharpness without any inappropriate steepening. However, for solutions with discontinuities, the MUSCL scheme is superior. In addition, a preliminary implementation in a GPU program is discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Development of an Atmospheric Climate Model with Self-Adapting Grid and Physics

This project was targeting the development of a computational approach that would allow resolving cloud processes on small-scales within the framework of the most recent version of the NASA/NCAR Finite-Volume Community Atmospheric Model (FVCAM). The FVCAM is based on the multidimensional Flux-Form Semi-Lagrangian (FFSL) dynamical core and uses a “vertically Lagrangian” finite-volume (FV) representation of the model equations with a mass-conserving re-mapping algorithm. The Lagrangian coordinate requires a remapping of the Lagrangian volume back to Eulerian coordinates to restore the original resolution and keep the mesh from developing distortions such as layers with overlapping interfaces. The main objectives of the project were, first, to develop the 3D library which allows refinement and coarsening of the model domain in spherical coordinates, and second, to develop a non-hydrostatic code for calculation of the model variables within the refined areas that could be seamlessly incorporated with the hydrostatic finite volume dynamical core when higher resolution is wanted. We also updated the aerosol simulation model in CAM in order to ready the model for the treatment of aerosol/cloud interactions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Exploratory Nuclear Reactor Safety Analysis and Visualization via Integrated Topological and Geometric Techniques

A recent trend in the nuclear power engineering field is the implementation of heavily computational and time consuming algorithms and codes for both design and safety analysis. In particular, the new generation of system analysis codes aim to embrace several phenomena such as thermo-hydraulic, structural behavior, and system dynamics, as well as uncertainty quantification and sensitivity analyses. The use of dynamic probabilistic risk assessment (PRA) methodologies allows a systematic approach to uncertainty quantification. Dynamic methodologies in PRA account for possible coupling between triggered or stochastic events through explicit consideration of the time element in system evolution, often through the use of dynamic system models (simulators). They are usually needed when the system has more than one failure mode, control loops, and/or hardware/process/software/human interaction. Dynamic methodologies are also capable of modeling the consequences of epistemic and aleatory uncertainties. The Monte-Carlo (MC) and the Dynamic Event Tree (DET) approaches belong to this new class of dynamic PRA methodologies. The major challenges in using MC and DET methodologies (as well as other dynamic methodologies) are the heavier computational and memory requirements compared to the classical ET analysis. This is due to the fact that each branch generated can contain time evolutions of a large number of variables (about 50,000 data channels are typically present in RELAP) and a large number of scenarios can be generated from a single initiating event (possibly on the order of hundreds or even thousands). Such large amounts of information are usually very difficult to organize in order to identify the main trends in scenario evolutions and the main risk contributors for each initiating event. This report aims to improve Dynamic PRA methodologies by tackling the two challenges mentioned above using: 1) adaptive sampling techniques to reduce computational cost of the analysis and 2) topology-based methodologies to interactively visualize multidimensional data and extract risk-informed insights. Regarding item 1) we employ learning algorithms that aim to infer/predict simulation outcome and decide the coordinate in the input space of the next sample that maximize the amount of information that can be gained from it. Such methodologies can be used to both explore and exploit the input space. The later one is especially used for safety analysis scopes to focus samples along the limit surface, i.e. the boundaries in the input space between system failure and system success. Regarding item 2) we present a software tool that is designed to analyze multi-dimensional data. We model a large-scale nuclear simulation dataset as a high-dimensional scalar function defined over a discrete sample of the domain. First, we provide structural analysis of such a function at multiple scales and provide insight into the relationship between the input parameters and the output. Second, we enable exploratory analysis for users, where we help the users to differentiate features from noise through multi-scale analysis on an interactive platform, based on domain knowledge and data characterization. Our analysis is performed by exploiting the topological and geometric properties of the domain, building statistical models based on its topological segmentations and providing interactive visual interfaces to facilitate such explorations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

A Brief Summary on Formalizing Parallel Tensor Distributions, Redistributions, and Algorithm Derivations

Large-scale datasets in computational chemistry typically require distributed-memory parallel methods to perform a special operation known as tensor contraction. Tensors are multidimensional arrays, and a tensor contraction is akin to matrix multiplication with special types of permutations. Creating an efficient algorithm and optimized implementation in this domain is complex, tedious, and error-prone. To address this, we develop a notation to express data distributions so that we can apply use automated methods to find optimized implementations for tensor contractions. We consider the spin-adapted coupled cluster singles and doubles method from computational chemistry and use our methodology to produce an efficient implementation. Experiments per- formed on the IBM Blue Gene/Q and Cray XC30 demonstrate impact both improved performance and reduced memory consumption.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Simplified predictive models for CO 2 sequestration performance assessment

CO2 sequestration in deep saline formations is increasingly being considered as a viable strategy for the mitigation of greenhouse gas emissions from anthropogenic sources. In this context, detailed numerical simulation based models are routinely used to understand key processes and parameters affecting pressure propagation and buoyant plume migration following CO2 injection into the subsurface. As these models are data and computation intensive, the development of computationally-efficient alternatives to conventional numerical simulators has become an active area of research. Such simplified models can be valuable assets during preliminary CO2 injection project screening, serve as a key element of probabilistic system assessment modeling tools, and assist regulators in quickly evaluating geological storage projects. We present three strategies for the development and validation of simplified modeling approaches for CO2 sequestration in deep saline formations: (1) simplified physics-based modeling, (2) statisticallearning based modeling, and (3) reduced-order method based modeling. In the first category, a set of full-physics compositional simulations is used to develop correlations for dimensionless injectivity as a function of the slope of the CO2 fractional-flow curve, variance of layer permeability values, and the nature of vertical permeability arrangement. The same variables, along with a modified gravity number, can be used to develop a correlation for the total storage efficiency within the CO2 plume footprint. Furthermore, the dimensionless average pressure buildup after the onset of boundary effects can be correlated to dimensionless time, CO2 plume footprint, and storativity contrast between the reservoir and caprock. In the second category, statistical “proxy models” are developed using the simulation domain described previously with two approaches: (a) classical Box-Behnken experimental design with a quadratic response surface, and (b) maximin Latin Hypercube sampling (LHS) based design with a multidimensional kriging metamodel fit. For roughly the same number of simulations, the LHS-based metamodel yields a more robust predictive model, as verified by a k-fold cross-validation approach (with data split into training and test sets) as well by validation with an independent dataset. In the third category, a reduced-order modeling procedure is utilized that combines proper orthogonal decomposition (POD) for reducing problem dimensionality with trajectory-piecewise linearization (TPWL) in order to represent system response at new control settings from a limited number of training runs. Significant savings in computational time are observed with reasonable accuracy from the PODTPWL reduced-order model for both vertical and horizontal well problems – which could be important in the context of history matching, uncertainty quantification and optimization problems. The simplified physics and statistical learning based models are also validated using an uncertainty analysis framework. Reference cumulative distribution functions of key model outcomes (i.e., plume radius and reservoir pressure buildup) generated using a 97-run full-physics simulation are successfully validated against the CDF from 10,000 sample probabilistic simulations using the simplified models. The main contribution of this research project is the development and validation of a portfolio of simplified modeling approaches that will enable rapid feasibility and risk assessment for CO2 sequestration in deep saline formations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Challenges at the Frontiers of Matter and Energy: Transformative Opportunities for Discovery Science

FIVE TRANSFORMATIVE OPPORTUNITIES FOR DISCOVERY SCIENCE As a result of this effort, it has become clear that the progress made to date on the five Grand Challenges has created a springboard for seizing five new Transformative Opportunities that have the potential to further transform key technologies involving matter and energy. These five new Transformative Opportunities and the evidence supporting them are discussed in this new report, “Challenges at the Frontiers of Matter and Energy: Transformative Opportunities for Discovery Science.” Mastering Hierarchical Architectures and Beyond-Equilibrium Matter Complex materials and chemical processes transmute matter and energy, for example from CO2 and water to chemical fuel in photosynthesis, from visible light to electricity in solar cells and from electricity to light in light emitting diodes (LEDs) Such functionality requires complex assemblies of heterogeneous materials in hierarchical architectures that display time-dependent away-from-equilibrium behaviors. Much of the foundation of our understanding of such transformations however, is based on monolithic single- phase materials operating at or near thermodynamic equilibrium. The emergent functionalities enabling next-generation disruptive energy technologies require mastering the design, synthesis, and control of complex hierarchical materials employing dynamic far-from-equilibrium behavior. A key guide in this pursuit is nature, for biological systems prove the power of hierarchical assembly and far- from-equilibrium behavior. The challenges here are many: a description of the functionality of hierarchical assemblies in terms of their constituent parts, a blueprint of atomic and molecular positions for each constituent part, and a synthesis strategy for (a) placing the atoms and molecules in the proper positions for the component parts and (b) arranging the component parts into the required hierarchical structure. Targeted functionality will open the door to significant advances in the harvesting, transforming (e.g., reducing CO2, splitting water, and fixing nitrogen), storing, and use of energy to create new materials, manufacturing processes, and technologies—the lifeblood of human societies and economic growth. Beyond Ideal Materials and Systems: Understanding the Critical Roles of Heterogeneity, Interfaces, and Disorder Real materials, both natural ones and those we engineer, are usually a complex mixture of compositional and structural heterogeneities, interfaces, and disorder across all spatial and temporal scales. It is the fluctuations and disorderly states of these heterogeneities and interfaces that often determine the system’s properties and functionality. Much of our fundamental scientific knowledge is based on “ideal” systems, meaning materials that are observed in “frozen” states or represented by spatially or temporally averaged states. Too often, this approach has yielded overly simplistic models that hide important nuances and do not capture the complex behaviors of materials under realistic conditions. These behaviors drive vital chemical transformations such as catalysis, which initiates most industrial manufacturing processes, and friction and corrosion, the parasitic effects of which cost the U.S. economy billions of dollars annually. Expanding our scientific knowledge from the relative simplicity of ideal, perfectly ordered, or structurally averaged materials to the true complexity of real-world heterogeneities, interfaces, and disorder should enable us to realize enormous benefits in the materials and chemical sciences, which translates to the energy sciences, including solar and nuclear power, hydraulic fracturing, power conversion, airframes, and batteries. Harnessing Coherence in Light and Matter Quantum coherence in light and matter is a measure of the extent to which a wave field vibrates in unison with itself at neighboring points in space and time. Although this phenomenon is expressed at the atomic and electronic scales, it can dominate the macroscopic properties of materials and chemical reactions such as superconductivity and efficient photosynthesis. In recent years, enormous progress has been made in recognizing, manipulating, and exploiting quantum coherence. This progress has already elucidated the role that symmetry plays in protecting coherence in key materials, taught us how to use light to manipulate atoms and molecules, and provided us with increasingly sophisticated techniques for controlling and probing the charges and spins of quantum coherent systems. With the arrival of new sources of coherent light and electron beams, thanks in large part to investments by the U.S. Department of Energy’s Office of Basic Energy Sciences (BES), there is now an opportunity to engineer coherence in heterostructures that incorporate multiple types of materials and to control complex, multistep chemical transformations. This approach will pave the way for quantum information processing and next-generation photovoltaic cells and sensors. Revolutionary Advances in Models, Mathematics, Algorithms, Data, and Computing Science today is benefiting from a convergence of theoretical, mathematical, computational, and experimental capabilities that put us on the brink of greatly accelerating our ability to predict, synthesize, and control new materials and chemical processes, and to understand the complexities of matter across a range of scales. Imagine being able to chart a path through a vast sea of possible new materials to find a select few with desired properties. Instead of the time-honored forward approach, in which materials with desired properties are found through either trial-and-error experiments or lucky accidents, we have the opportunity to inversely design and create new materials that possess the properties we desire. The traditional approach has allowed us to make only a tiny fraction of all the materials that are theoretically possible. The inverse design approach, through the harmonious convergence of theoretical, mathematical, computational, and experimental capabilities, could usher in a virtual cornucopia of new materials with functionalities far beyond what nature can provide. Similarly, enhanced mathematical and computational capabilities significantly enhance our ability to extract physical and chemical insights from vastly larger data streams gathered during multimodal and multidimensional experiments using advanced characterization facilities. Exploiting Transformative Advances in Imaging Capabilities across Multiple Scales Historically, improvements in imaging capabilities have always resulted in improved understanding of scientific phenomena. A prime challenge today is finding ways to reconstruct raw data, obtained by probing and mapping matter across multiple scales, into analyzable images. BES investments in new and improved imaging facilities, most notably synchrotron x-ray sources, free-electron lasers, electron microscopes, and neutron sources, have greatly advanced our powers of observation, as have substantial improvements in laboratory- scale technologies. Furthermore, BES is now planning or actively discussing exciting new capabilities. Taken together, these advances in imaging capabilities provide an opportunity to expand our ability to observe and study matter from the 3D spatial perspectives of today to true “4D” spatially and temporally resolved maps of dynamics that allow quantitative predictions of time-dependent material properties and chemical processes. The knowledge gained will impact data storage, catalyst design, drug delivery, structural materials, and medical implants, to name just a few key technologies. ENABLING SUCCESS Seizing each of these five Transformative Opportunities, as well as accelerating further progress on Grand Challenge research, will require specific, targeted investments from BES in the areas of synthesis, meaning the ability to make the materials and architectures that are envisioned; instrumentation and tools, a category that includes theory and computation; and human capital, the most important asset for advancing the Grand Challenges and Transformative Opportunities. While “Challenges at the Frontiers of Matter and Energy: Transformative Opportunities for Discovery Science” could be viewed as a sequel to the original Grand Challenges report, it breaks much new ground in its assessment of the scientific landscape today versus the scientific landscape just a few years ago. In the original Grand Challenges report, it was noted that if the five Grand Challenges were met, our ability to direct matter and energy would be measured only by the limits of human imagination. This new report shows that, prodded by those challenges, the scientific community is positioned today to seize new opportunities whose impacts promise to be transformative for science and society, as well as dramatically accelerate progress in the pursuit of the original Grand Challenges.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Future of Electron Scattering and Diffraction

The ability to correlate the atomic- and nanoscale-structure of condensed matter with physical properties (e.g., mechanical, electrical, catalytic, and optical) and functionality forms the core of many disciplines. Directing and controlling materials at the quantum-, atomic-, and molecular-levels creates enormous challenges and opportunities across a wide spectrum of critical technologies, including those involving the generation and use of energy. The workshop identified next generation electron scattering and diffraction instruments that are uniquely positioned to address these grand challenges. The workshop participants identified four key areas where the next generation of such instrumentation would have major impact: A – Multidimensional Visualization of Real Materials B – Atomic-scale Molecular Processes C – Photonic Control of Emergence in Quantum Materials D – Evolving Interfaces, Nucleation, and Mass Transport Real materials are comprised of complex three-dimensional arrangements of atoms and defects that directly determine their potential for energy applications. Understanding real materials requires new capabilities for three-dimensional atomic scale tomography and spectroscopy of atomic and electronic structures with unprecedented sensitivity, and with simultaneous spatial and energy resolution. Many molecules are able to selectively and efficiently convert sunlight into other forms of energy, like heat and electric current, or store it in altered chemical bonds. Understanding and controlling such process at the atomic scale require unprecedented time resolution. One of the grand challenges in condensed matter physics is to understand, and ultimately control, emergent phenomena in novel quantum materials that necessitate developing a new generation of instruments that probe the interplay among spin, charge, orbital, and lattice degrees of freedom with intrinsic time- and length-scale resolutions. Molecules and soft matter require imaging and spectroscopy with high spatial resolution without damaging their structure. The strong interaction of electrons with matter allows high-energy electron pulses to gather structural information before a sample is damaged. Electron ScatteringImaging, diffraction, and spectroscopy are the fundamental capabilities of electron-scattering instruments. The DOE BES-funded TEAM (Transmission Electron Aberration-corrected Microscope) project achieved unprecedented sub-atomic spatial resolution in imaging through aberration-corrected transmission electron microscopy. To further advance electron scattering techniques that directly enable groundbreaking science, instrumentation must advance beyond traditional two-dimensional imaging. Advances in temporal resolution, recording the full phase and energy spaces, and improved spatial resolution constitute a new frontier in electron microscopy, and will directly address the BES Grand Challenges, such as to “control the emergent properties that arise from the complex correlations of atomic and electronic constituents” and the “hidden states” “very far away from equilibrium”. Ultrafast methods, such as the pump-probe approach, enable pathways toward understanding, and ultimately controlling, the chemical dynamics of molecular systems and the evolution of complexity in mesoscale and nanoscale systems. Central to understanding how to synthesize and exploit functional materials is having the ability to apply external stimuli (such as heat, light, a reactive flux, and an electrical bias) and to observe the resulting dynamic process in situ and in operando, and under the appropriate environment (e.g., not limited to UHV conditions). To enable revolutionary advances in electron scattering and science, the participants of the workshop recommended three major new instrumental developments: A. Atomic-Resolution Multi-Dimensional Transmission Electron Microscope: This instrument would provide quantitative information over the entire real space, momentum space, and energy space for visualizing dopants, interstitials, and light elements; for imaging localized vibrational modes and the motion of charged particles and vacancies; for correlating lattice, spin, orbital, and charge; and for determining the structure and molecular chemistry of organic and soft matter. The instrument will be uniquely suited to answer fundamental questions in condensed matter physics that require understanding the physical and electronic structure at the atomic scale. Key developments include stable cryogenic capabilities that will allow access to emergent electronic phases, as well as hard/soft interfaces and radiation- sensitive materials. B. Ultrafast Electron Diffraction and Microscopy Instrument: This instrument would be capable of nano-diffraction with 10 fs temporal resolution in stroboscopic mode, and better than 100 fs temporal resolution in single shot mode. The instrument would also achieve single- shot real-space imaging with a spatial/temporal resolution of 10 nm/10 ps, representing a thousand fold improvement over current microscopes. Such a capability would be complementary to x-ray free electron lasers due to the difference in the nature of electron and x-ray scattering, enabling space-time mapping of lattice vibrations and energy transport, facilitating the understanding of molecular dynamics of chemical reactions, the photonic control of emergence in quantum materials, and the dynamics of mesoscopic materials. C. Lab-In-Gap Dynamic Microscope: This instrument would enable quantitative measurements of materials structure, composition, and bonding evolution in technologically relevant environments, including liquids, gases and plasmas, thereby assuring the understanding of structure function relationship at the atomic scale with up to nanosecond temporal resolution. This instrument would employ a versatile, modular sample stage and holder geometry to allow the multi-modal (e.g., optical, thermal, mechanical, electrical, and electrochemical) probing of materials’ functionality in situ and in operando. The electron optics encompasses a pole piece that can accommodate the new stage, differential pumping, detectors, aberration correctors, and other electron optical elements for measurement of materials dynamics. To realize the proposed instruments in a timely fashion, BES should aggressively support research and development of complementary and enabling instruments, including new electron sources, advanced electron optics, new tunable specimen pumps and sample stages, and new detectors. The proposed instruments would have transformative impact on physics, chemistry, materials science, engineering

43 PARTICLE ACCELERATORS↗
DOE OSTI2012

Measurements of the top-quark decay width and mass at CDF using the template method.

Measurements of the top quark decay width and mass are presented using the tt events produced in p p collisions at Fermilab's Tevatron collider and collected by the CDF II detector. A data sample corresponding to 4.3 fb -1 of integrated luminosity is used for the top quark width measurement. Two estimators, the reconstructed top quark mass and the mass of hadronically decaying W boson that comes from the top-quark decay are reconstructed for each event and compared with templates of different input top quark widths and deviations from nominal CDF jet energy scale (ΔJES) to perform a simultaneous fit for both parameters. ΔJES is used for the in situ calibration of the jet energy scale at CDF. By applying a Feldman-Cousins limit-setting approach, we establish an upper limit at 95% confidence level (CL) of Γtop < 7.6 GeV and a two-sided 68% CL interval of (0.3 GeV, 4.4) GeV assuming a top quark mass of 172.5 GeV/c 2 , which are consistent with the standard model prediction. The measurement of the top quark mass uses a data sample of tt events in 5.7 fb -1 of integrated luminosity collected by the same detector. Candidate events in the top quark mass measurement are required to have large missing transverse energy, no identified charged leptons, and four, five, or six jets with at least one jet tagged as coming from a b quark. This analysis considers events from the semileptonic tt decay channel, including events that contain tau leptons. The measurement is based on a multidimensional template method, in a similar way to the top quark width measurement, and the top quark mass is measured to be Mtop = 172.32 ± 2.37 ± 0.98 GeV/c 2 .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Device Scale Modeling of Solvent Absorption using MFIX-TFM

Recent climate change is largely attributed to greenhouse gases (e.g., carbon dioxide, methane) and fossil fuels account for a large majority of global CO 2 emissions. That said, fossil fuels will continue to play a significant role in the generation of power for the foreseeable future. The extent to which CO 2 is emitted needs to be reduced, however, carbon capture and sequestration are also necessary actions to tackle climate change. Different approaches exist for CO 2 capture including both post-combustion and pre-combustion technologies, oxy-fuel combustion and/or chemical looping combustion. The focus of this effort is on post-combustion solvent-absorption technology. To apply CO 2 technologies at commercial scale, the availability and maturity and the potential for scalability of that technology need to be considered. Solvent absorption is a proven technology but not at the scale needed by typical power plant. The scale up and down and design of laboratory and commercial packed bed reactors depends heavily on the specific knowledge of two-phase pressure drop, liquid holdup, the wetting efficiency and mass transfer efficiency as a function of operating conditions. Simple scaling rules often fail to provide proper design. Conventional reactor design modeling approaches will generally characterize complex non-ideal flow and mixing patterns using simplified and/or mechanistic flow assumptions. While there are varying levels of complexity used within these approaches, none of these models resolve the local velocity fields. Consequently, they are unable to account for important design factors such as flow maldistribution and channeling from a fundamental perspective. Ideally design would be aided by development of predictive models based on truer representation of the physical and chemical processes that occur at different scales. Computational fluid dynamic (CFD) models are based on multidimensional flow equations with first principle foundations. CFD models can include a more accurate physical description of flow processes and be modified to include more complex behavior. Wetting performance and spatial liquid distribution inside the absorber are recognized as weak areas of knowledge requiring further investigation. CFD tools offer a possible method to investigating such topics and gaining a better understanding of their influence on reactor performance. This report focuses first on describing a hydrodynamic model for countercurrent gas-liquid flow through a packed column and then on the chemistry, heat and mass transfer specific to CO 2 absorption using monoethanolamine (MEA). The indicated model is implemented in MFIX, a CFD open source software package. The user defined functions needed to build this model are described in detail along with the keywords for the corresponding input file. A test case is outlined along with a few results. The example serves to briefly illustrate the developed CFD tool and its potential capability to investigate solvent absorption.

09 BIOMASS FUELS↗
DOE OSTI2017

Inverse and Predictive Modeling

The LANL Seismo-Acoustic team has a strong capability in developing data-driven models that accurately predict a variety of observations. These models range from the simple – one-dimensional models that are constrained by a single dataset and can be used for quick and efficient predictions – to the complex – multidimensional models that are constrained by several types of data and result in more accurate predictions. Team members typically build models of geophysical characteristics of Earth and source distributions at scales of 1 to 1000s of km, the techniques used are applicable for other types of physical characteristics at an even greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.

58 GEOSCIENCES↗
DOE OSTI2017

ATDM LANL FleCSI: Topology and Execution Framework

FleCSI is a compile-time configurable C++ framework designed to support multi-physics application development. As such, FleCSI attempts to provide a very general set of infrastructure design patterns that can be specialized and extended to suit the needs of a broad variety of solver and data requirements. This means that FleCSI is potentially useful to many different ECP projects. Current support includes multidimensional mesh topology, mesh geometry, and mesh adjacency information, n-dimensional hashed-tree data structures, graph partitioning interfaces, and dependency closures (to identify data dependencies between distributed-memory address spaces). FleCSI introduces a functional programming model with control, execution, and data abstractions that are consistent with state-of-the-art task-based runtimes such as Legion and Charm++. The model also provides support for fine-grained, data-parallel execution with backend support for runtimes such as OpenMP and C++17. The FleCSI abstraction layer provides the developer with insulation from the underlying runtimes, while allowing support for multiple runtime systems, including conventional models like asynchronous MPI. The intent is to give developers a concrete set of user-friendly programming tools that can be used now, while allowing flexibility in choosing runtime implementations and optimizations that can be applied to architectures and runtimes that arise in the future. This project is essential to the ECP Ristra Next-Generation Code project, part of ASC ATDM, because it provides a hierarchically parallel programming model that is consistent with the design of modern system architectures, but which allows for the straightforward expression of algorithmic parallelism in a portably performant manner.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Improved Safety Margin Characterization of Risk from Loss of Offsite Power

Original intent: The original intent of this task was “support of the Risk-Informed Safety Margin Characteristic (RISMC) methodology in order” “to address … efficiency of computation so that more accurate and cost-effective techniques can be used to address safety margin characterizations” (S. M. Hess et al., “Risk-Informed Safety Margin Characterization,” Procs. ICONE17, Brussels, July 2009, CD format). It was intended that “in Task 1 itself this improvement will be directed toward upon the very important issue of Loss of Offsite Power (LOOP) events,” more specifically toward the challenge of efficient computation of the multidimensional nonrecovery integral that has been discussed by many previous contributors to the theory of nuclear safety. It was further envisioned that “three different computational approaches will be explored,” corresponding to the three subtasks listed below; deliverables were tied to the individual subtasks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Materials Dynamics

The Materials Dynamics area of leadership focuses on understanding structure-properties-performance relationships for the extreme conditions of dynamic loading. This research encompasses controlled synthesis of materials to meet dynamic performance requirements and requires computational coupling across length and time scales for three-dimensional microstructure modeling. For this leadership area, we define dynamic loading as strain rates greater than 103/s. A key grand challenge of this area is to predict and measure the evolution of microstructural phases, defect structures, and electronic structure under dynamic conditions while also dynamically measuring local temperature to understand transition states. Solving this challenge will require agile, multidimensional data analysis and interpretation capability.

36 MATERIALS SCIENCE↗
DOE OSTI2018

R&D Roadmap for Enhanced Resilient Plant Systems, Metrics, Scenarios, Risk Analysis, and Modeling and Simulation

The nuclear industry and the U.S. Department of Energy are jointly developing accident tolerant fuel (ATF) which has the potential to greatly improve the safety and economic performance of the current fleet of NPPs. The combination of ATF with the optimal use of FLEX equipment (deployed as an industry response to the Fukushima Daiichi Nuclear Power Plant (NPP) accident in 2012), enhancements on plant components and systems, incorporation of augmented cooling systems or new passive cooling systems, and improvements in fuel cycle efficiency, have the potential to render the NPPs more efficient and resilient to events (both internally and externally initiated) that have the potential to challenge the integrity of the fuel or plant containment. In this report, combinations of these plant improvements are called Enhanced Resilient Plant Systems. In order to accelerate the development and deployment of the components that comprise Enhanced Resilient Plant Systems, a well-integrated toolkit that is fuel/clad-, fuel cycle-, systems-, and scenario-centric is required to provide a ready platform for the analysis of the advanced ATF fuel and cladding materials that are being developed, as well as plant systems modifications to achieve enhanced resiliency. Since the deployment of these enhancements will comprise a risk/benefit tradeoff of their impacts on plant safety and economics, the envisioned toolkit will need to be capable to characterizing the multidimensional aspects associated with these evaluations and provide decision-makers with information that can optimize the technological, economic, and safety benefits that are to be obtained. The Risk-Informed Systems Analysis (RISA) Pathway of the DOE Light Water Reactor Sustainability (LWRS) Program initiated several research tasks to develop analytical capabilities to support the industry in the transition to the ATF and Enhanced Resilient Plant Systems. The general idea behind the initiative is the development of an integrated evaluation approach which combines the plant probabilistic risk assessment (PRA) models with Multi-Physics Best Estimate Plus Uncertainty (MP-BEPU) analyses in a seamless fashion. The integrated evaluation framework aims at enabling plant system configuration variations to be studied with speed and precision, including detailed risk and benefit assessments of introducing ATF and Enhanced Resilient Plant Systems into current LWR plants to achieve both safety and operational performance enhancements. The focus of this report is to present an integrated research and development (R&D) roadmap to identify and perform high value evaluations of proposed ATF and Enhanced Resilient Plant Systems concepts to identify both the technical (e.g. benefits to risk, safety, and operational margins) and the economic (i.e. business and cost) elements associated with industry adoption of the technology. The integrated evaluation approach will be developed to support the development, testing, qualification, licensing, and deployment of ATF and Enhanced Resilient Plant Systems technology that is capable of achieving substantial safety and economic improvements as well as timely widespread adoption by the US nuclear industry.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Summary of Expansions and Updates in GREET® 2018

The GREET® (Greenhouse gases, Regulated Emissions, and Energy use in Transportation) model has been developed by Argonne National Laboratory (Argonne) with the support of the U.S. Department of Energy(DOE). GREET is a life-cycle analysis (LCA) tool, structured to systematically examine energy and environmental effects of a wide variety of transportation fuels and vehicle technologies in major transportation sectors (i.e., road, air, marine, and rail). There are two GREET modeling platforms; GREET Excel is a multidimensional spreadsheet model that provides a comprehensive LCA tool, and GREET.Net provides an interactive graphical toolbox to perform LCA. The GREET 2018 release includes expansions and updates for both platforms, and this report provides a summary of the release.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗