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Analysis of Alternatives (AoA) of Open Colllaboration and Research Capabilities Collaboratipon in Research and Engineering in Advanced Technology and Education and High-Performance Computing Innovation Center (HPCIC) on the LVOC.
The Livermore Valley Open Campus (LVOC), a joint initiative of the National Nuclear Security Administration (NNSA), Lawrence Livermore National Laboratory (LLNL), and Sandia National Laboratories (SNL), enhances the national security missions of NNSA by promoting greater collaboration between world-class scientists at the national security laboratories, and their partners in industry and academia. Strengthening the science, technology, and engineering (ST&E) base of our nation is one of the NNSA’s top goals. By conducting coordinated and collaborative programs, LVOC enhances both the NNSA and the broader national science and technology base, and helps to ensure the health of core capabilities at LLNL and SNL. These capabilities must remain strong to enable the laboratories to execute their primary mission for NNSA.
Advances in Domain Mapping of Massively Parallel Scientific Computations
One of the most important concerns in parallel computing is the proper distribution of workload across processors. For most scientific applications on massively parallel machines, the best approach to this distribution is to employ data parallelism; that is, to break the datastructures supporting a computation into pieces and then to assign those pieces to different processors. Collectively, these partitioning and assignment tasks comprise the domain mapping problem.
Air Flow Modeling in the Wind Tunnel of the FHWA Aerodynamics Laboratory at Turner-Fairbank Highway Research Center
Computational fluid dynamics (CFD) modeling is widely used in industry for design and in the research community to support, compliment, and extend the scope of experimental studies. Analysis of transportation infrastructure using high performance cluster computing with CFD and structural mechanics software is done at the Transportation Research and Analysis Computing Center (TRACC) at Argonne National Laboratory. These resources, available at TRACC, were used to perform advanced three-dimensional computational simulations of the wind tunnel laboratory at the Turner-Fairbank Highway Research Center (TFHRC). The goals were to verify the CFD model of the laboratory wind tunnel and then to use versions of the model to provide the capability to (1) perform larger parametric series of tests than can be easily done in the laboratory with available budget and time, (2) to extend testing to wind speeds that cannot be achieved in the laboratory, and (3) to run types of tests that are very difficult or impossible to run in the laboratory. Modern CFD software has many physics models and domain meshing options. Models, including the choice of turbulence and other physics models and settings, the computational mesh, and the solver settings, need to be validated against measurements to verify that the results are sufficiently accurate for use in engineering applications. The wind tunnel model was built and tested, by comparing to experimental measurements, to provide a valuable tool to perform these types of studies in the future as a complement and extension to TFHRC’s experimental capabilities. Wind tunnel testing at TFHRC is conducted in a subsonic open-jet wind tunnel with a 1.83 m (6 foot) by 1.83 m (6 foot) cross section. A three component dual force-balance system is used to measure forces acting on tested models, and a three degree of freedom suspension system is used for dynamic response tests. Pictures of the room are shown in Figure 1-1 to Figure 1-4. A detailed CAD geometry and CFD model of the wind tunnel laboratory at TFHRC was built and tested. Results were compared against experimental wind velocity measurements at a large number of locations around the room. This testing included an assessment of the air flow uniformity provided by the tunnel to the test zone and assessment of room geometry effects, such as influence of the proximity the room walls, the non-symmetrical position of the tunnel in the room, and the influence of the room setup on the air flow in the room. This information is useful both for simplifying the computational model and in deciding whether or not moving, or removing, some of the furniture or other movable objects in the room will change the flow in the test zone.
Advanced Methods for Immersed Domain Multi-physics Computations
This is the final report on the project, and describes the main accomplishments in the development of the Shifted Boundary Method (SBM), a new computational framework for embedded/immersed computational mechanics applications.
Software Tools Ecosystem Project (STEP) Midyear Report CY2025
This document provides a technical project report for the first six months of 2025 for the Software Tools Ecosystem Project (STEP). The mission of STEP is to enable critical software tools to proactively adapt to emerging platform technologies (such as new accelerators, storage devices, network technologies, and smart devices) and emerging application use cases (such as advanced machine learning and workflow frameworks) so that they continue to meet the needs of scientific computing and provide a strong foundation for future Advanced Scientific Computing Research activities. Our challenges include the wide breadth of our stakeholders and rapidly evolving platform technology dependencies.
Software Tools Ecosystem Project (STEP): CY2025 Annual Report
This document provides a technical project report for the Software Tools Ecosystem Project (STEP) during calendar year 2025. The mission of STEP is to enable critical software tools to proactively adapt to emerging platform technologies (such as new accelerators, storage devices, network technologies, and smart devices) and emerging application use cases (such as advanced machine learning and workflow frameworks) so that they continue to meet the needs of scientific computing and provide a strong foundation for future Advanced Scientific Computing Research activities.
The Advanced Radiographic Capability, A Major Upgrade Of The Computer Controls For The National Ignition Facility*
Abstract not provided
Resistive Memory: How Advances in Nanotechnology will enable Next Generation Exascale Computers.
Abstract not provided.
Advancing 3D Printing of Metals and Electronics using Computational Fluid Dynamics.
Abstract not provided.
A Computational Tool for Applying Optimization Under Uncertainty on Advanced Process Simulators
Presented by NETL at 2018 AIChE Annual Meeting, 10/18
Computational Multiphysics Engineered Surfaces and Next-Generation Advanced Reactor Materials.
Abstract not provided.
Experimental High Energy Physics at the University of Illinois
This research effort funded by the Office of High Energy Physics in the U.S. Department of Energy is aimed at exploring our universe at its most basic level. The goal of our effort is to learn more about how and why nature behaves the way it does. Within this effort, we are studying the smallest particles and the largest distances we can possibly observe. Our work in particular focuses on the search for new interactions that will help us understand how the universe came into being. Through this work, we collaborate with other scientists and engineers to develop new technologies that can be utilized throughout society. Benefits from our research include advances in medical technology, electronics, transportation, sustainability, information and computing as well as the advanced training of undergraduate and graduate students in science and engineering.
Softwarized Federations of Science Instruments with Edge-Continuum Containers
Significant expansion of capabilities of DOE science complex of supercomputers, instruments and networks is expected as powerful experimental facilities, exascale computers and terabit networks are added. Combined with the advances in edge and cloud computing technologies, DOE science users now have the promise of unprecedented execution of complex, continuum workflows, namely, small and latency-sensitive computations at the edge and site, massive computations at remote HPC systems, and everything in between on the cloud. But, bringing this capability to the science user requires overcoming the overwhelming complexity of forming the federations of systems, and efficiently and effectively orchestrating the workflows while ensuring high utilization of the expensive facilities. Current manual configuration of the federated systems simply will not scale, since the coordination across sites may take weeks to months, often leading to under-utilized and hard-to-diagnose compositions. A powerful, composable software stack will be developed to (i) wrap the systems so that federations can be composed fast in software, and (ii) containerize computations to be orchestrated across the edge, site, cloud and HPC resources.
Efficient computational search for lanthanide-binding additive dopants for advanced U-Zr based fuels
A rational design strategy combining theory and experiment is highly desirable for the development of U-Zr based fuels that are resistant to lanthanide-induced fuel-cladding chemical interaction (FCCI). Here, we performed rapid computational screening of FCCI-mitigating (via strong lanthanide-binding) elements across the periodic table by utilizing three simple criteria and density functional theory (DFT) calculated total energies of relevant elements and compounds retrieved from high-throughput DFT databases. In addition to successfully identifying previously demonstrated additives, our search leads to the discovery of Bi as a highly effective element among all 68 elements screened, and its effectiveness was experimentally confirmed in this study.
Aqueous Titania Interfaces
Water–metal oxide interfaces are central to many phenomena and applications, ranging from material corrosion and dissolution to photoelectrochemistry and bioengineering. In particular, the discovery of photocatalytic water splitting on TiO 2 has motivated intensive studies of water-TiO 2 interfaces for decades. So far, a broad understanding of the interaction of water vapor with several TiO 2 surfaces has been obtained. However, much less is known about liquid water–TiO 2 interfaces, which are more relevant to many practical applications. Probing these complex systems at the molecular level is experimentally challenging and is sometimes possible only through computational studies. This review summarizes recent advances in the atomistic understanding, mostly through computational simulations, of the structure and dynamics of interfacial water on TiO 2 surfaces. The main focus is on the nature, molecular or dissociated, of water in direct contact with low-index defect-free crystalline surfaces. The hydroxyls resulting from water dissociation are essential in the photooxidation of water and critically affect the surface chemistry of TiO 2.
Perspectives on Research in Computational Plasma Physics With Applications to Experiments
Computational plasma physics was born some 50 years ago with the development of high-speed computers. Computer simulation is well suited to the challenging nature of fundamental and applied plasma science, which exhibits enormous ranges of time and space scales, and whose underlying mathematical framework is comprised of nonlinear partial differential equations and nonlinear kinetic equations. Computational plasma physics has advanced from relatively simple calculations with limited dimensionality and scope to sophisticated and comprehensive simulation models. Moreover, computational plasma physics has matured as a significant scientific discipline with a rigorous mathematical foundation and voluminous literature. While computational plasma physics has greatly benefitted from the growth in computing capability by many orders of magnitude, the contributions from innovation in methods and algorithms have been no less important. This talk presents a personal perspective on the development and application of computational plasma physics to plasmas in nature and in the laboratory. The examples described will be based on experience over the course of a career in computational plasma physics and illustrate both the fundamental plasma phenomena and the behavior of laboratory plasmas. Some of the specific examples include simulations of microinstabilities in the magnetic mirror, tokamak and spheromak plasmas, laser-plasma interactions, and the Knudsen-layer phenomena, as it affects fusion performance. The examples also illustrate the development of appropriate models and algorithms that are well suited to simulating the phenomena of interest efficiently. Finally, a particular interest of the author has been the development of multiple time-scale algorithms.
Advanced Research Directions on AI for Science, Energy, and Security: Report on Summer 2022 Workshops
Over the past decade, fundamental changes in artificial intelligence (AI)—from foundational to applied—have delivered dramatic insights across a wide breadth of U.S. Department of Energy (DOE) mission space. AI is helping to augment and improve scientific and engineering workflows (e.g., for control, design, and dramatic performance gains through surrogate models) in national security, the Office of Science, and DOE’s applied energy programs. The progress and potential for AI in DOE science was captured in the 2020 “AI for Science” report from the DOE laboratory community in collaboration with academia and industry. Specific scientific areas ready to further leverage the power of AI ranged from the scale and performance of computational models to data analysis to creating new classes of observations using computer vision. Since that report, the scale and scope of scientific AI have accelerated, revealing new, emergent properties that yield insights that go beyond enabling opportunities to being potentially transformative in the way that scientific problems are posed and solved. Thus, under the guidance of both the Office of Science (SC) and the National Nuclear Security Administration (NNSA), the DOE national laboratories organized a series of workshops in 2022 to gather input on new and rapidly emerging opportunities and challenges of scientific AI. This 2023 report is a synthesis of those workshops. The scientific community believes AI can have a foundational impact on a broad range of DOE missions, including science, energy, and national security. Further, DOE has unique capabilities that enable the community to drive progress in scientific use of AI, building on long-standing DOE strengths and investments in computation, data, and communications infrastructure, spanning the Energy Sciences Network (ESnet), the Exascale Computing Project (ECP), and integrative programs such as the NNSA Office of Defense Programs Advanced Simulation and Computing (ASC) and the SC Scientific Discovery through Advanced Computing (SciDAC) programs.