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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Turbulence theories and statistical closure approaches

When discussing research in physics and in science more generally, it is common to ascribe equal importance to the three components of the scientific trinity: theoretical, experimental, and computational studies. This review will explore the future of modern turbulence theory by tracing its history, which began in earnest with Kolmogorov’s 1941 analysis of turbulence cascade and inertial range [A.N. Kolmogorov, Dokl. Akad. Nauk SSSR, 30, 299, (1941); 32, 19, (1941)]. The 80th Anniversary of Kolmogorov’s landmark study is a welcome opportunity to survey the achievements and evaluate the future of the theoretical approach of turbulence research. Over the years, turbulence theories have been critically important in laying the foundation of our understanding of the nature of turbulent flows. In particular, the Direct Interaction Approximation (DIA) [R.H. Kraichnan, J. Fluid Mech., 5, 497 (1959)] and its subsequent development, known as the statistical closure approach, can be identified as perhaps the most profound single advancement. The remarkable success of the statistical closure has furnished a platform to study such essential concepts as the energy transfer process and interacting scales, and the roles of the straining and sweeping motions. More recently, the quasi-Lagrangian formulation of V. L’vov & I. Procaccia and Kraichnan’s solvable passive scalar model provided powerful ways to explore another fundamental aspect of turbulent flows, the phenomena of intermittency, and the associated anomalous scaling exponents. In the meantime, the theory of fluid equilibria has been developed to describe the large-scale structures that can emerge from turbulent cascades of two-dimensional and geophysical flows at a later time. And yet, despite all these successes, analytical treatments suffer from mathematical complexities. As a result, the utility of theoretical approaches has been limited to relatively idealized flows. On the other hand, in recent decades, computational abilities and experimental facilities have reached an unprecedented scale. Looking beyond the horizon, the imminent deployment of exascale supercomputers will generate complete datasets of the entire flow field of key benchmark flows, allowing researchers to extract additional measurements concerning fully developed, complex turbulent flow fields far beyond those available from the statistical closure theories. Some other developments that could potentially influence the future course of turbulence theories include the advancement of machine learning, artificial intelligence, and data science; likely disruptions arising from the advent of quantum computation; and the increasingly prominent role of turbulence research in providing more accurate climate scientific data. Finally, turbulence theorists can leverage these developments by asking the right questions and developing advanced, sophisticated frameworks that will be able to predict and correlate vast amounts of data from the other two components of the trinity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Toward Molecular Catalysts by Computer

Rational design of molecular catalysts requires a systematic approach to designing ligands with specific functionality and precisely tailored electronic and steric properties. It then becomes possible to devise computer protocols to predict accurately the required properties and ultimately to design catalysts by computer. In this account we first review how thermodynamic properties such as oxidation-reduction potentials (E0), acidities (pKa), and hydride donor abilities (ΔGH-) form the basis for a systematic design of molecular catalysts for reactions that are critical for a secure energy future (hydrogen evolution and oxidation, oxygen and nitrogen reduction, and carbon dioxide reduction). We highlight how density functional theory allows us to determine and predict these properties within “chemical” accuracy (~ 0.06 eV for redox potentials, ~ 1 pKa unit for pKa values, and ~ 1.5 kcal/mol for hydricities). These quantities determine free energy maps and profiles associated with catalytic cycles, i.e. the relative energies of intermediates, and help us distinguish between desirable and high-energy pathways and mechanisms. Good catalysts have flat profiles that avoid high activation barriers due to low and high energy intermediates. We illustrate how the criterion of a flat energy profile lends itself to the prediction of design points by computer for optimum catalysts. This research was carried out in the Center for Molecular Electro-catalysis, an Energy Frontier Research Center funded by the U.S. Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences. Pacific Northwest National Laboratory (PNNL) is operated for the DOE by Battelle.

acidity↗
DOE OSTI2024

Frontiers in Scientific Workflows: Pervasive Integration With High-Performance Computing

Herein we address the increasing complexity of scientific workflows in the context of high-performance computing (HPC) and their associated need for robust, adaptable, and flexible computational support systems. We explore five key trends as well as future challenges and opportunities for scientific workflows and HPC technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Critical Issues in High End Computing (Final Report)

High-End computing (HEC) has been a driver for advances in science and engineering for the past four decades. Increasingly HEC has become a significant element in the national security, economic vitality, and competitiveness of the United States. Advances in HEC provide results that cut across traditional disciplinary and organizational boundaries. This program provides opportunities to share information about HEC systems and computational techniques across multiple disciplines and organizations through conferences and exhibitions of HEC advances held in Washington DC so that mission agency staff, scientists, and industry can come together with White House, Congressional and Legislative staff in an environment conducive to the sharing of technical information, accomplishments, goals, and plans. A common thread across this series of conferences is the understanding of computational science and applied mathematics techniques across a diverse set of application areas of interest to the Nation. The specific objectives of this program are: Program Objective 1. To provide opportunities to share information about advances in high-end computing systems and computational techniques between mission critical agencies, agency laboratories, academics, and industry. Program Objective 2. To gather pertinent data, address specific topics of wide interest to mission critical agencies. Program Objective 3. To promote a continuing discussion of critical issues in high-end computing. Program Objective 4. To provide a venue where a multidisciplinary scientific audience can discuss the difficulties applying computational science techniques to specific problems and can specify future research that, if successful, will eliminate these problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

360⁰ -View of Quantum Theory and Ab Initio Simulation at Extreme Conditions: 2014 Sanibel Symposium

The Sanibel Symposium 2014 was held February 16-21, 2014, at the King and Prince, St. Simons Island, GA. It was successful in bringing condensed-matter physicists and quantum chemists together productively to drive the emergence of those specialties. The Symposium had a significant role in preparing a whole generation of quantum theorists. The 54th Sanibel meeting looked to the future in two ways. We had 360⁰-View sessions to honor the exceptional contributions of Rodney Bartlett (70), Bill Butler (70), Yngve Öhrn (80), Fritz Schaefer (70), and Malcolm Stocks (70). The work of these five has greatly impacted several generations of quantum chemists and condensed matter physicists. The “360⁰” is the sum of their ages. More significantly, it symbolizes a panoramic view of critical developments and accomplishments in theoretical and computational chemistry and physics oriented toward the future. Thus, two of the eight 360⁰-View sessions focused specifically on younger scientists. The 360⁰-View program was the major component of the 2014 Sanibel meeting. Another four sessions included a sub-symposium on ab initio Simulations at Extreme Conditions, with focus on getting past the barriers of present-day Born-Oppenheimer molecular dynamics by advances in finite-temperature density functional theory, orbital-free DFT, and new all-numerical approaches.

360-view↗
DOE OSTI2026

Energy and physical resource impacts of quantum computing merit greater attention

Quantum computing research and development is growing worldwide; yet the energy and physical resource demands of future quantum-accelerated data centres are unknown. Planning for quantum computing requires strong collaboration between research communities across engineering, physics, environmental sciences, economics, policy, and energy systems and scenario modelling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Derivation of the Future Time Equation for Analog, Non-Multiplying Monte Carlo Simulation

The expected computational time required to simulate a particle from a point in phase space through a Monte Carlo history, termed the expected future time, is found by solving the Future Time Equation (FTE). The expected future time may be useful when generating variance reduction parameters for a Monte Carlo simulation with a method such as Consistent Adjoint Driven Importance Sampling (CADIS). This report presents a detailed derivation of the Future Time Probability Density Function (FTPDF) and FTE for neutral particle Monte Carlo transport to aid future researchers. For simplicity, this derivation only considers analog transport in non-multiplying media.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2013

DFT modeling of adsorption onto uranium metal using large-scale parallel computing

There is a dearth of atomistic simulations involving the surface chemistry of 7-uranium which is of interest as the key fuel component of a breeder-burner stage in future fuel cycles. Recent availability of high-performance computing hardware and software has rendered extended quantum chemical surface simulations involving actinides feasible. With that motivation, data for bulk and surface 7-phase uranium metal are calculated in the plane-wave pseudopotential density functional theory method. Chemisorption of atomic hydrogen and oxygen on several un-relaxed low-index faces of 7-uranium is considered. The optimal adsorption sites (calculated cohesive energies) on the (100), (110), and (111) faces are found to be the one-coordinated top site (8.8 eV), four-coordinated center site (9.9 eV), and one-coordinated top 1 site (7.9 eV) respectively, for oxygen; and the four-coordinated center site (2.7 eV), four-coordinated center site (3.1 eV), and three-coordinated top2 site (3.2 eV) for hydrogen. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

TRUST Sensors in Environments: Thermocouples (SE-TC) Report Release FY24

The objective of the Delivery Environments (DE) Testbeds to Reduce Uncertainty in Simulations and Tests (TRUST) project is to quantify and help increase confidence in specific areas of computational and experimental capabilities that are applicable to the development, on-target assessment, and qualification of current and future delivery environments. More complete quantification of confidence in experimental and computational capabilities and the sufficient increase of confidence in those capabilities is critical to improving weapons engineering design, qualification, and assessment efforts that are critical to the current and future stockpile. This work uses and provides feedback on analysis tools and experimental results databases for efficient and responsive engineering which are currently under development.

42 ENGINEERING↗
DOE OSTI2014

Reducing Communication in Algebraic Multigrid Using Additive Variants

Algebraic multigrid (AMG) has proven to be an effective scalable solver on many high performance computers. However, its increasing communication complexity on coarser levels has shown to seriously impact its performance on computers with high communication cost. Moreover, additive AMG variants provide not only increased parallelism as well as decreased numbers of messages per cycle but also generally exhibit slower convergence. Here we present various new additive variants with convergence rates that are significantly improved compared to the classical additive algebraic multigrid method and investigate their potential for decreased communication, and improved communication-computation overlap, features that are essential for good performance on future exascale architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Hybrid Quantum-Classical Neural Networks

Deep learning is one of the most successful and far-reaching strategies used in machine learning today. However, the scale and utility of neural networks is still greatly limited by the current hardware used to train them. These concerns have become increasingly pressing as conventional computers are soon expected to approach the physical limitations that will slow their performance improvements in the near future. For these reasons, scientists have begun to explore alternative computing platforms, like quantum computers, for training neural networks. In recent years, variational quantum circuits have emerged as one of the most successful approaches to quantum deep learning on noisy intermediate scale quantum devices. We propose a hybrid quantum-classical neural network architecture where each neuron is a variational quantum circuit. We empirically analyze the performance of this hybrid neural network on a series of binary classification data sets using a simulated IBM universal quantum computer and a state-of-the-art IBM universal quantum computer. On the simulated hardware, we observe that the hybrid neural network achieves around 10% higher classification accuracy and 20% better minimization of the cost function than an individual variational quantum circuit. On the quantum hardware, we observe that each model only performs well when the qubit and gate count is sufficiently small.

Arthur, Davis↗
DOE OSTI2021

Future Physics Perspectives on the Equation of State from Heavy Ion Collisions to Neutron Stars

With the computational power and algorithmic improvements available today, the ongoing STAR/RHIC and HADES/GSI experiments, the future FAIR and NICA facilities becoming operational, and the new precise measurements from NICER and LIGO/VIRGO, the high-energy nuclear physics and astrophysics communities are in the unique position to set very stringent constraints on the equation of state of strongly interacting matter. Here, we review the state-of-the-art of different approaches used in the description of hot and ultradense baryonic matter in and out of equilibrium, and discuss the regions in the phase diagram where heavy-ion collisions and neutron star mergers can overlap. Future perspectives are discussed to help define a comprehensive, multi-disciplinary strategy to map out the phase diagram of strongly interacting matter from heavy ion collisions to neutron stars.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

COOLR: A New System for Dynamic Thermal-Aware Computing

Extreme-scale systems need to walk a fine line between the amount of cooling they receive and the thermal-induced performance and reliability degradation they can sustain. System managers are extremely motivated to battle cooling-energy cost: the largest line item in their total operating cost. On the other hand, pushing the nodes to peak performance in tightened cooling regimes places the burden on the dynamic thermal management (DTM) to protect the hardware. DTM schemes throttle performance to relieve heat accumulation within nodes when cooling cannot mitigate the problem. These interventions prevent fatal failures but introduce inevitable performance degradations and variations. Such variations can have drastic consequences on the performance of extreme-scale systems. Furthermore, distribution of heat across different system nodes depends not only on the amount of workload. Even if a strictly equal share of computational load is assigned to all nodes, there are physical attributes and topological features of each system that inherently cause uneven accumulation of heat. These can cause one subcomponent to trigger DTM prematurely and penalize the overall system. Our goal in this project is to create a holistic thermal-aware view of the system, capturing both inherent physical attributes and dynamic system state. We developed COOLR, a dynamic system with the ability to evaluate the interplay between management of computation, data, power dissipation, and thermal state. Thereby, COOLR will achieve higher overall performance at the same energy and cooling cost. First, we performed a systematic power and thermal modeling and of high performance computing architectures. Analytical and empirical techniques were blended to generate a light-weight thermal model of target systems. Second, we developed a thermal-aware OS and runtime system. Policies for managing resources and schedules for processes has been designed to co-optimize the thermal state and performance of the system. The main outcome of our proposed thermal-aware dynamic computing system are scheduling and allocation of processes and memory accesses leading to an appropriately “skewed” distribution of activity across the system. The ultimate result of this new arrangement resulted in reduced occurrences of hotspots and reduced episodes of DTM intervention, leading to improvements in performance. The overall thermal characterization and modeling methodology (thermal instrumentation techniques, model building, and systematic model reduction) resulting from this project can be applied to systems beyond our immediate scope and will be applicable to future scaling. The thermal-aware dynamic computing paradigm will impact the efficiency of extreme-scale systems. The gained “thermal slack” can be given back to further tighten the cooling budget, benefiting the management cost of next generation extreme-scale systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Relativistic Effects From Coupled-Cluster Theory

We discuss the theory and computational challenges of the relativistic coupled-cluster methods. Example calculations of heavy-atom-containing molecules are then presented to demonstrate the importance of scalar-relativistic, spin-orbit coupling, and electron-correlation effects on molecular properties as well as the applicability and usefulness of relativistic coupled-cluster methods in calculations aiming at high-accuracy results. Here, a unique applicability of the spinor-based relativistic coupled-cluster methods is also highlighted using the calculations of open shell actinide-containing small molecules. As a result, a summary is given together with an outlook into future developments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Providing Geospatial Intelligence through a Scalable Imagery Pipeline

This chapter describes ORNL’s (Oak Ridge National Laboratory’s) contributions to imagery preprocessing for geospatial intelligence research and development (R&D) in four sections. First, we discuss challenges involved in building an effective imagery preprocessing workflow and the world-class high-performance computing (HPC) resources at ORNL available to process petabytes of imagery data. Second, we highlight how we developed imagery preprocessing tools over three decades while paving the way for our current cutting-edge machine learning and computer vision algorithms that are impacting humanitarian and disaster response efforts. Third, we discuss how PIPE modules work together to turn raw images into analysis-ready datasets. Fourth, we look toward the future and discuss planned advancements to PIPE and computing trends that will affect geospatial intelligence R&D.

Reith, Andrew↗
DOE OSTI2017

Porting AMG2013 to Heterogeneous CPU+GPU Nodes

LLNL's future advanced technology system SIERRA will feature heterogeneous compute nodes that consist of IBM PowerV9 CPUs and NVIDIA Volta GPUs. Conceptually, the motivation for such an architecture is quite straightforward: While GPUs are optimized for throughput on massively parallel workloads, CPUs strive to minimize latency for rather sequential operations. Yet, making optimal use of heterogeneous architectures raises new challenges for the development of scalable parallel software, e.g., with respect to work distribution. Porting LLNL's parallel numerical libraries to upcoming heterogeneous CPU+GPU architectures is therefore a critical factor for ensuring LLNL's future success in ful lling its national mission. One of these libraries, called HYPRE, provides parallel solvers and precondi- tioners for large, sparse linear systems of equations. In the context of this intern- ship project, I consider AMG2013 which is a proxy application for major parts of HYPRE that implements a benchmark for setting up and solving di erent systems of linear equations. In the following, I describe in detail how I ported multiple parts of AMG2013 to the GPU (Section 2) and present results for di erent experiments that demonstrate a successful parallel implementation on the heterogeneous ma- chines surface and ray (Section 3). In Section 4, I give guidelines on how my code should be used. Finally, I conclude and give an outlook for future work (Section 5).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Solving quantum riddles with neutron scattering

Quantum materials exhibit a rich landscape of highly-degenerate quantum states that are widely regarded to hold vast potential for future applications, ranging from power management and transmission, to platforms for quantum computation, to novel versatile sensors and electronics. A key to realizing the promise of future applications is to identify the fundamental energy scales that control the emergence of such quantum states and their properties.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Safety Assessment for the Kozloduy National Disposal Facility in Bulgaria - 13507

Due to the early decommissioning of four Water-Water Energy Reactors (WWER) 440-V230 reactors at the Nuclear Power Plant (NPP) near the city of Kozloduy in Bulgaria, large amounts of low and intermediate radioactive waste will arise much earlier than initially scheduled. In or-der to manage the radioactive waste from the early decommissioning, Bulgaria has intensified its efforts to provide a near surface disposal facility at Radiana with the required capacity. To this end, a project was launched and assigned in international competition to a German-Spanish consortium to provide the complete technical planning including the preparation of the Intermediate Safety Assessment Report. Preliminary results of operational and long-term safety show compliance with the Bulgarian regulatory requirements. The long-term calculations carried out for the Radiana site are also a good example of how analysis of safety assessment results can be used for iterative improvements of the assessment by pointing out uncertainties and areas of future investigations to reduce such uncertainties in regard to the potential radiological impact. The computer model used to estimate the long-term evolution of the future repository at Radiana predicted a maximum total annual dose for members of the critical group, which is carried to approximately 80 % by C-14 for a specific ingestion pathway. Based on this result and the outcome of the sensitivity analysis, existing uncertainties were evaluated and areas for reasonable future investigations to reduce these uncertainties were identified. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗