EQMU & ECMF Advanced Simulations and Computing for Engineering Quantification
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Whelsky, Amber N.↗
SEARCH · Engineering Papers
Results for “Advanced Computing”
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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Evaluation of the shear rate effect on the interfacial forces acting on a single bubble
Owing to the outstanding characteristics of mass and heat transfer efficiency, two-phase bubbly flows have wide applications in the nuclear engineering fields. However, the physics behind the bubbly flows phenomenon can be very complex due to the various parameters and phenomena involved. Thanks to the increasing computing power and advanced algorithms, computational fluid dynamics (CFD) approach has emerged as an effective tool for exploring the bubbly flows. In typical laminar channel or pipe flow, the flow field develops a parabolic velocity profile due to the wall friction. Assuming the shear rate across the bubble diameter is constant, the bubble will encounter various shear rates depending on its transverse position. Different shear rates may lead to the acceleration of transverse migration speed and influence the bubble rising velocity. In this summary, we evaluate the effect of shear rate on the interfacial forces (lift and drag) acting on a single bubble with the shear rate ranging from 3.8 to 8.3 s{sup -1} and the Eotvos number ranging from 2.58 to 4.45.
42 ENGINEERING↗
Exploratory study on potential safeguards applications for shared ledger technology
The International Atomic Energy Agency (IAEA) is responsible for providing credible assurance that countries are meeting their obligations not to divert or misuse nuclear materials and facilities for non-peaceful purposes. To this end, the IAEA integrates information about States’ nuclear material inventories and transactions with other types of data to draw its safeguards conclusions. As the amount and variety of data and information has increased, the IAEA’s data acquisition, management, and analysis processes have greatly benefited from advancements in computer science, data management, and cybersecurity during the last 20 years. Despite these advancements, inconsistent use of advanced computer technologies as well as political concerns among certain IAEA Member States centered on trust, transparency, and IAEA authorities limit the overall effectiveness and efficiency of IAEA safeguards. As a result, there is an ongoing need to strengthen the effectiveness and efficiency of IAEA safeguards while improving Member State cooperation and trust in the safeguards system. These chronic safeguards needs could be met with some emerging technologies, specifically those associated with the digital currency bitcoin.
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
Modeling Protein–Protein and Protein–Ligand Interactions by the ClusPro Team in CASP16
ABSTRACT In the CASP16 experiment, our team employed hybrid computational strategies to predict both protein–protein and protein–ligand complex structures. For protein–protein docking, we combined physics‐based sampling—using ClusPro FFT docking and molecular dynamics—with AlphaFold (AF)‐based sampling, followed by AF‐based refinement. Our method produced numerous high‐accuracy complex models, including cases where AF alone failed, underscoring the critical role of physics‐based sampling alongside deep learning‐based refinement. For protein–ligand docking, we integrated the ClusPro LigTBM template‐based approach with a machine learning‐based confidence model for rescoring. The method preserves conserved interaction fragments derived from homologous complexes, followed by local resampling using physics‐based sampling and a diffusion model. Our template‐based strategy achieved a mean lDDT‐PLI of 0.69 across 233 targets, which was highly competitive. These results demonstrate that combining physics‐based modeling with AI‐driven refinement can significantly enhance the accuracy of both protein–protein and protein–ligand structure predictions.
Ashizawa, Ryota [Department of Applied Mathematics↗
Chemical and Conformational Control of the Spectroscopic Properties of Multi-Layer and Multi-Defect Carbon Dots
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Mehmood, Arshad [Division of Information Technolog↗
Computational Requirements in Clean Energy and Manufacturing: Summary report of the virtual workshop held on June 28-29, 2021
On June 28–29, 2021, the US Department of Energy’s (DOE’s) Advanced Scientific Computing Research (ASCR) program in the Office of Science convened a workshop with the Energy Efficiency and Renew able Energy (EERE) program offices to assess the future need for advanced computing resources in the areas of clean energy and advanced manufacturing. In part, this discussion served as an update to earlier workshops and townhalls. ASCR is guided by DOE mission needs as it develops research programs, computers, and networks at the leading edge of technologies. As the exascale computing era dawns, technology changes are creating new opportunities for those who must use high-performance computing (HPC) and data systems effectively. The ASCR computing facilities are augmenting their strategy to adapt to changing science needs and emerging technologies and to leverage the utility of exascale computing across the federal government.
97 MATHEMATICS AND COMPUTING↗
A state-of-the-art review of experimental and computational studies of granular materials: Properties, advances, challenges, and future directions
Modeling of heterogeneous materials and media is a problem of fundamental importance to a wide class of phenomena and systems, ranging from condensed matter physics, soft materials, and composite media to porous media, biological systems, geosystems, ceramic engineering, pharmaceutical science and even in space discoveries. Among the most important materials are granular systems, which have received intense interest from the engineering, physics, and mathematics communities. In this review paper, the recent developments and new advances in experimental, and computational methods on a variety of scales and physics that extend understanding to a wide range of materials and phenomena are reviewed. Experimental advances include computed neutron and nanometer-scale tomography, magnetic resonance imaging, refractive index matching, digital image correlation, acoustic emission analysis, and the most recent 4D techniques. Furthermore, a tremendous shift has occurred from the continuum scale to micro-scale and developing multiscale approaches. As such, various computational methods, including, constitutive modeling, discrete modeling, and multiscale approaches, have been developed. In conclusion, aside from all these evolutions, more complicated modeling called coupled, or multiphysics, systems representing a simultaneous presence of heat, fluid, chemical variation, and mechanical effect are also explored.
36 MATERIALS SCIENCE↗
Combining Modeling and Gaming for Predictive Analytics
Many of our most significant challenges involve people. While human behavior has long been studied, there are recent advances in computational modeling of human behavior. With advances in computational capabilities come increases in the volume and complexity of data that humans must understand in order to make sense of and capitalize on these modeling advances. Ultimately, models represent an encapsulation of human knowledge. One inherent challenge in modeling is efficient and accurate transfer of knowledge from humans to models, and subsequent retrieval. The simulated real-world environment of games presents one avenue for these knowledge transfers. In this paper we describe our approach of combining modeling and gaming disciplines to develop predictive capabilities, using formal models to inform game development, and using games to provide data for modeling.
99 GENERAL AND MISCELLANEOUS↗
Coding the Computing Continuum: Fluid Function Execution in Heterogeneous Computing Environments
Advances in network technologies have greatly decreased barriers to accessing physically distributed computers. This newfound accessibility coincides with increasing hardware specialization, creating exciting new opportunities to dispatch workloads to the best resource for a specific purpose, rather than those that are closest or most easily accessible. We present Delta, a service designed to intelligently schedule function-based workloads across a distributed set of heterogeneous computing resources. Delta implements an extensible architecture in which different predictors and scheduling algorithms can be integrated to provide dynamically evolving estimates of function execution times on different resources-estimates that can be used to determine the most appropriate location for execution. We describe predictors for function runtime, data transfer time, and cold-start resource provisioning and configuration delay; dynamic learning methods that update predictor models over time; and scheduling strategies that take into account both function and endpoint information. We show that these methods can halve workload makespan when compared with a strategy that selects the fastest resource, and decrease makespan by a factor of five when compared to a round robin strategy, when deployed on a heterogeneous testbed with resources ranging from a Raspberry Pi to a GPU node in an academic cloud.
Computing continuum↗
Louisiana Immersive Technologies Enterprise (LITE): Enabling Coastal Stewardship and Reliable Energy
This project will focus on research and application development in the area of large scale data computing and visualization that are critical to the DoE BER program areas of climate change research, environmental sciences, life sciences, and medical sciences. Most commercial applications available today are unable to handle the sizes of data in these domains, usually in the order of terabytes and petabytes. This project will address this problem, by taking advantage of the advanced scientific computing infrastructure of the LITE and LONI to conduct research and develop software applications that run natively on these 64-bit supercomputing platforms. The project is consistent with the goals of Scientific Discovery through Advanced Computing (SciDAC), a DoE initiative.
54 ENVIRONMENTAL SCIENCES↗
ASCR Report on a Quantum Computing Testbed for Science
This report details the findings of the Quantum Testbed Stakeholder Workshop sponsored by the Department of Energy’s Advanced Scientific Computing Research Program to identify opportunities and challenges in establishing a quantum testbed to advance quantum computing hardware and software systems that will enable science investigations. The workshop was help on February 14 – 16, 2017 in Washington DC and served as a forum for individual stakeholders from academia, industry, national laboratories, and government to provide their perspectives and ideas on the overarching goals and objectives of a quantum testbed, technical considerations, and how a quantum testbed program would be synergistic with other nascent quantum computing efforts in the US and worldwide. This report summarizes discussions on best practices for management of various types of collaborative research activities, including topics such as workforce training and building strong relationships with the research community. It also reviews specific technologies that have been identified by the ASCR Program as being important for the success of a quantum testbed whose overall goal is advancing quantum computing for scientific applications in the next five years.
97 MATHEMATICS AND COMPUTING↗
Advanced Methods for the Computation of Particle Beam Transport and the Computation of Electromagnetic Fields and Multiparticle Phenomena
Since 1980, under the grant DEFG02-96ER40949, the Department of Energy has supported the educational and research work of the University of Maryland Dynamical Systems and Accelerator Theory (DSAT) Group. The primary focus of this educational/research group has been on the computation and analysis of charged-particle beam transport using Lie algebraic methods, and on advanced methods for the computation of electromagnetic fields and multiparticle phenomena. This Final Report summarizes the accomplishments of the DSAT Group from its inception in 1980 through its end in 2011.
43 PARTICLE ACCELERATORS↗
Simulation-based characterization of the variability of earthquake risk to buildings in the near-field
Recent advancements in high performance computing platforms and computational workflow for regional-scale simulations are enabling unprecedented modeling of fault-to-structure earthquake processes. Regional simulations resolving ground motions at frequencies relevant to engineered systems are becoming computationally viable and provide a new capability to improve understanding of the geographical distribution and intensity of risk to buildings and critical infrastructure. As computational capabilities advance, it is essential to move beyond illustrative single rupture realizations for scenario earthquake events towards the development of a full suite of rupture realizations that appropriately characterize the range of risk to building systems. The work described in this article investigates the application of a suite of fault rupture realizations with the objective of assessing near-fault, site-specific seismic demand variability for building structures. A representative high-performance regional-scale computational model is utilized to execute ground motion and building response simulations based on 18 kinematic rupture realizations of an M7 strike-slip scenario earthquake. The fault rupture models for the scenario earthquake are created by systematically perturbing the hypocenter location and stochastically generating rupture parameters (slip, rise time, rake angle) to represent a breadth of ground motion intensities resulting from the spatial and temporal variabilities of an earthquake rupture process. The resulting seismic demand variability for three-story (short period) and forty-story (long period) steel moment-resisting frame buildings is characterized in terms of the median and distribution of peak inter-story drift ratio for a range of near-fault sites. The full suite of 18 fault rupture realizations and approximately 280,000 nonlinear dynamic building simulations indicate that the three-story building undergoes higher median seismic demand and significantly greater variability of demand at a given site than the forty-story building, which has important implications for the level of certainty in predicting building performance during an earthquake. The simulations performed provide deeper insight into the relationship between fault rupture parameterization and building response, which is essential information for developing a representative suite of rupture realizations for specific earthquake scenarios.
58 GEOSCIENCES↗
Nuclear Science User Facilities High Performance Computing: FY2019 Operations and Accomplishments Report
This report highlights a subset of accomplishments including projects that were completed in FY 2019 by researchers using Idaho National Laboratory's High-Performance Computing (HPC) resources.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
LLNL Partners with IBM on Brain-Like Computing Chip
Lawrence Livermore National Laboratory (LLNL) will receive a first-of-a-kind brain-inspired supercomputing platform for deep learning developed by IBM Research. Based on a breakthrough neurosynaptic computer chip called IBM TrueNorth, the scalable platform will process the equivalent of 16 million neurons and 4 billion synapses and consume the energy equivalent of a hearing aid battery – a mere 2.5 watts of power. The brain-like, neural network design of the IBM Neuromorphic System is able to infer complex cognitive tasks such as pattern recognition and integrated sensory processing far more efficiently than conventional chips.
97 MATHEMATICS AND COMPUTING↗
Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics
Abstract Advancements in computing power have recently made it possible to utilize machine learning and deep learning to push scientific computing forward in a range of disciplines, such as fluid mechanics, solid mechanics, materials science, etc. The incorporation of neural networks is particularly crucial in this hybridization process. Due to their intrinsic architecture, conventional neural networks cannot be successfully trained and scoped when data are sparse, which is the case in many scientific and engineering domains. Nonetheless, neural networks provide a solid foundation to respect physics-driven or knowledge-based constraints during training. Generally speaking, there are three distinct neural network frameworks to enforce the underlying physics: (i) physics-guided neural networks (PgNNs), (ii) physics-informed neural networks (PiNNs), and (iii) physics-encoded neural networks (PeNNs). These methods provide distinct advantages for accelerating the numerical modeling of complex multiscale multiphysics phenomena. In addition, the recent developments in neural operators (NOs) add another dimension to these new simulation paradigms, especially when the real-time prediction of complex multiphysics systems is required. All these models also come with their own unique drawbacks and limitations that call for further fundamental research. This study aims to present a review of the four neural network frameworks (i.e., PgNNs, PiNNs, PeNNs, and NOs) used in scientific computing research. The state-of-the-art architectures and their applications are reviewed, limitations are discussed, and future research opportunities are presented in terms of improving algorithms, considering causalities, expanding applications, and coupling scientific and deep learning solvers.
Computer Science↗
Concurrent determination of nanocrystal shape and amorphous phases in complex materials by diffraction scattering computed tomography
Advanced functional materials often contain multiple phases which are (nano)crystalline and/or amorphous. The spatial distribution of these phases and their properties, including nanocrystallite size and shape, often drives material function yet is difficult to obtain with current experimental techniques. This article describes the use of diffraction scattering computed tomography, which maps wide-angle scattering information onto sample space, to address this challenge. The wide-angle scattering signal contains information on both (nano)crystalline and amorphous phases. Rietveld refinement of reconstructed diffraction patterns is employed to determine anisotropic nanocrystal shapes. The background signal from refinements is used to identify contributing amorphous phases through multivariate curve resolution. Thus it is demonstrated that reciprocal space analysis in combination with diffraction scattering computed tomography is a very powerful tool for the complete analysis of complex multiphase materials such as energy devices.
Birkbak, Mie Elholm↗
Author Correction: High-fidelity qutrit entangling gates for superconducting circuits
The original version of this Article contained an error in the second sentence of the Acknowledgements, which incorrectly read: ‘This work was supported by the Quantum Testbed Program of the Advanced Scientific Computing Research for Basic Energy Sciences program, Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231’. The correct version states ‘the Quantum Testbed Program of the Advanced Scientific Computing Research Division’ in place of ‘the Quantum Testbed Program of the Advanced Scientific Computing Research for Basic Energy Sciences program’. This has been corrected in both the PDF and HTML versions of the Article.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗