Results for “Computer applications” — Page 63

DOE OSTI2017

SIERRA Multimechanics Module: Aria Thermal Theory Manual (Version 4.46)

This document describes the theoretical foundation of thermal analysis in Sierra Mechanics. The SIERRA Multimechanics Module: Aria, henceforth referred to as Aria for brevity, was developed at Sandia National Laboratories under the ASC program, and approximates linear and nonlinear continuum models of heat transfer. Aria uses the SIERRA Framework, which provides data management services commonly required by computational mechanics software, and facilitates the development of coupled, multi-mechanics applications for massively parallel computers. The mathematical models in Aria are based heavily on those of COYOTE, a well-established thermal analysis program that was also developed at Sandia and its ASC code predecessor, Calore. Aria, Calore and COYOTE share a significant body of numerical methods, which are described in detail by Reddy and Gartling. Throughout this document, the terms software and implementation are synonymous with the Aria thermal-fluid analysis computer program.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

SIERRA Multimechanics Module: Aria Thermal Theory Manual (Version 4.44)

This document describes the theoretical foundation of thermal analysis in Sierra Mechanics. The SIERRA Multimechanics Module: Aria, henceforth referred to as Aria for brevity, was developed at Sandia National Laboratories under the ASC program, and approximates linear and nonlinear continuum models of heat transfer. Aria uses the SIERRA Framework, which provides data management services commonly required by computational mechanics software, and facilitates the development of coupled, multi-mechanics applications for massively parallel computers. The mathematical models in Aria are based heavily on those of COYOTE, a well-established thermal analysis program that was also developed at Sandia and its ASC code predecessor, Calore. Aria, Calore and COYOTE share a significant body of numerical methods, which are described in detail by Reddy and Gartling. Throughout this document, the terms software and implementation are synonymous with the Aria thermal-fluid analysis computer program.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

SIERRA Multimechanics Module: Aria Thermal Theory Manual (Version 4.42)

This document describes the theoretical foundation of thermal analysis in Sierra Mechanics. The SIERRA Multimechanics Module: Aria, henceforth referred to as Aria for brevity, was developed at Sandia National Laboratories under the ASC program, and approximates linear and nonlinear continuum models of heat transfer. Aria uses the SIERRA Framework, which provides data management services commonly required by computational mechanics software, and facilitates the development of coupled, multi-mechanics applications for massively parallel computers. The mathematical models in Aria are based heavily on those of COYOTE, a well-established thermal analysis program that was also developed at Sandia and its ASC code predecessor, Calore. Aria, Calore and COYOTE share a significant body of numerical methods, which are described in detail by Reddy and Gartling. Throughout this document, the terms software and implementation are synonymous with the Aria thermal-fluid analysis computer program.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era

The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration. Commensurately, algorithms and statistical models have begun to adapt --- e.g., via the onset of artificial intelligence --- which itself presents new challenges and opportunities for growth. This white paper aims to offer guidance and ideas for how we can evolve our technical and collaborative frameworks to promote efficient algorithmic development and take advantage of opportunities for scientific discovery in the petabyte era. We discuss challenges for discovery in large and complex data sets; challenges and requirements for the next stage of development of statistical methodologies and algorithmic tool sets; how we might change our paradigms of collaboration and education; and the ethical implications of scientists' contributions to widely applicable algorithms and computational modeling. We start with six distinct recommendations that are supported by the commentary following them. This white paper is related to a larger corpus of effort that has taken place within and around the Petabytes to Science Workshops (https://petabytestoscience.github.io/).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

How AI Predicts the Untrained and Unseen

Focus Area: Model predictability improvements (Primary); Data optimization (secondary); Data complexity insights (secondary). The Scientific Challenge: If we believe that a future under extreme conditions will look very differently from today, we can likely agree that ML/AI models trained on past and present datasets will not be adequate to make reliable predictions into the future. This is true for water cycling, as well as biogeochemistry and other Earth system components and behaviors. Additionally, ML/AI models are inherently non-physical. Despite the flourishing success of ML/AI in many applications, such as computer vision, natural language process, and gaming, even the most sophisticated AI models don’t understand the very basic physical laws. Therefore, a natural question is: Can we trust ML/AI based predictions of Earth system behaviors that are fundamentally driven by physical laws? So, are physics models with meticulous process representation a better choice? Not exactly. Physical models, when firmly rooted in first principles, work great at predicting behaviors of systems with a well-defined set of boundary conditions and variables. However, as a complex system, the number of parameters and the degree of complexity and dynamics in processes, coupling, and scale dependent emergent behaviors make the Earth system behaviors very challenging to predict with physical models composed of deterministic laws. In addition, due to the lack of fundamental understandings, physical models often implement empirical correlations derived from observations with biases from locality of data generation. Because correlation is not necessarily causation or comply with first principles, scaling of model predictions beyond locality is often invalid. Beyond the limitation of models, physics or AI, our knowledge of the Earth system is limited by the lack of observational technologies and resources. Insufficient data density, dimensionality and diversity only offer a sliced (or projected) view of the Earth system, e.g. Plato’s Cave analogy, limiting our capability to better understand and represent fundamental processes in models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

AXIOM Unfold 0.7.0, Users Manual

The AXIOM-Unfold application is a computational code for performing spectral unfolds along with uncertainty quantification of the photon spectrum. While this code was principally designed for spectral unfolds on the Saturn source, it is also relevant to other radiation sources such as Pithon. This code is a component of the AXIOM project which was undertaken in order to measure the time-resolved spectrum of the Saturn source; to support this, the AXIOM-Unfold code is able to process time-dependent dose measurements in order to obtain a time-resolved spectrum. This manual contains a full description of the algorithms used by the method. The code features are fully documented along with several worked examples.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

OC6 Phase Ia Definition Document: Validation of Nonlinear Hydrodynamic Loading on the DeepCwind Semisubmersible

The objective of OC6 Phase I is to investigate the persistent under-prediction (about 20% on average) of the structural loads in the OC5-DeepCwind floating semisubmersible wind system, as observed within the previous OC5 Phase II project. The OC5 results indicate that much of this under-prediction originates from the low-frequency response of the system at the surge/pitch natural frequencies, resulting from nonlinear hydrodynamic loading and/or overprediction of hydrodynamic damping. This work package therefore focuses on better understanding this low-frequency response behavior, the applicability of computational-fluid-dynamics (CFD) models and engineering-level hydrodynamic models for predicting the nonlinear hydrodynamic loading, and the level of uncertainty in the measured response characteristics. To address these objectives, two experimental campaigns were developed and performed in 2017 and 2018 by a sub-group of the OC5 project: Floating test campaign: A simplified OC5-DeepCwind semisubmersible model (no turbine, rigid tower) was moored to three taut-spring lines and subjected to motion decay and wave tests (regular and irregular waves), during which motion amplitudes of the model were measured (see Figure 1a). The purpose of this test campaign was to examine the repeatability of the low-frequency response of the system to wave excitation and determine the level of uncertainty in the response behavior.Constrained test campaign: The same model, but without the tower present, was fixed to a carriage and subjected to towing, forced oscillation and wave tests (regular and irregular waves), during which forces exerted upon the model were measured (see Figure 1b). The purpose of this test campaign was to examine the hydrodynamic loading contributions by separating the wave excitation and radiation forces. Both model test campaigns were performed in the concept basin of the Maritime Research Institute Netherlands (MARIN) in Wageningen, the Netherlands and the first test was within the framework of the MARINET2 project. Both experimental campaigns were examined within Phase Ia of the OC6 project. The purpose of this report is to supply the needed information to participants of the OC6 project to build and simulate the experimental campaigns.

17 WIND ENERGY↗

DOE OSTI2023

Disorder, interactions, and their interplay in novel narrow-gap Dirac materials and Weyl semimetals

Progress of the modern day condensed matter physics is to a large extent driven by the synthesis of new materials, advances in their experimental characterization and theoretical description. Recent discoveries of novel gapless Weyl semimetals, such as NaBi,CdAs, and BiTe-based films, in which magnetic dopants essentially suppress the gap, have added to the family of graphene and topological insulators actively investigated over the past decade. With the field of novel semimetals rapidly maturing, its focus necessarily shifts from demonstrations of the feasibility of such materials to their quantitative characterization. While the transport and optical properties of graphene and topological insulators are well captured within the picture of free non-interacting electrons, gapless 3D Weyl semimetals and narrow-gap 2D semiconductors with Dirac spectrum are known to be extremely susceptible to disorder and electron-electron interactions. This susceptibility obscures the manifestations of nontrivial band structure -- like quantum anomalous Hall effect -- of the new topological materials. Among particular projects to be addressed are: 1) optical conductivity of 3D gapless Dirac fermions in the presence of smooth disorder, 2) interplay of disorder and Coulomb interactions in the spectral properties of such fermions, 3) formation and structure of the impurity band with Coulomb supercritical clusters, 4) Coulomb interaction-driven renormalization of the electron spectrum and of the transport response in the presence of strong magnetic field, 5) instanton approach to the disorder-induced fluctuation states in zero-gap 3D materials, and 6) the role of disorder in quantum anomalous Hall effect. The proposal relies upon the investigators' previous broad expertise in interacting and disordered electron systems. The methods to be employed include perturbative diagrammatic technique, non-perturbative instanton and self-consistent approximations, hydrodynamics of electron liquid. Both analytical as well as numerical approaches are to be employed. The anticipated broader outcome of the proposal includes gaining an in-depth understanding of the interplay of the disorder and interactions under the conditions when this interplay has the most dramatic impact on observables. Traditionally, interaction effects are among the most challenging and interesting problems of condensed matter physics. Similarly, disordered systems typically present very difficult but extremely rich problems in the description of various materials. Importantly, understanding the spectral and transport properties of such materials not only presents the fundamental objective, but is also of particular interest for many applications, such as computation, memory, optics, plasmonics. In particular realization of the quantum anomalous Hall effect may lead to the development of low-power-consumption electronics. Indeed, a major constraint for practical use of the quantum Hall effect is limited by the requirement of the quantizing magnetic field. At the same time, the quantum anomalous Hall effect samples exhibit non-dissipative edge quantum transport in a zero magnetic field.

36 MATERIALS SCIENCE↗

DOE OSTI2020

FY20 Proxy App Suite Release: Report for ECP Proxy App Project Milestone ADCD-504-10

Version 4.0 of the ECP Proxy App Suite is practically unchanged from the previous release. The current set of proxies has proven useful for many aspects of benchmarking and co-design and we see little reason to alter the suite. Although there have been few changes to the ECP suite, the team has been hard at work in other areas. In the area of Machine Learning (ML) we have now created a separate proxy suite dedicated to this scientific applications of ML. The suite includes: miniGAN (Generative Adversarial Networks), miniRL (Reinforcement Learning), CRADL (inline inference), Cosmoflow-Benchmark (Convolutional Neural Network), and MLPerf-DeepCam (Climate Segmentation Benchmark). Section 3 contains more information about these proxies as well as the principles that are guiding the development of the suite. We have surveyed available proxies for several application domains including Computational Fluid Dynamics, Quantum Chemistry, Quantum Computing Simulation, Molecular Dynamics, Monte Carlo Transport, and Density Functional Theory to identify gaps in proxy coverage. Several new proxy apps are either already available or will be released soon to fill these gaps. Section 4 provides full details. Finally, section 5 reports on our continued collaboration with the ECP Continuous Integration (CI) effort to use proxy apps to help identify problems and roadblocks to cross-lab CI. We have also assisted the El Capitan Center of Excellence (COE) to stand up a CI system that will be used to test releases of HPE and AMD software stacks. We hope that the COE effort can serve as a model for ECP by showing how the Proxy App Team can work with AD and ST teams identify critical features, kernels, patterns, etc. and incorporate them into a CI system that will help ensure that Frontier and Aurora will provide those needed capabilities.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Material point generation from an STL file

In this short note we describe a method for generating material points from a file in the stere olithography (STL) format commonly used in computer aided design (CAD), 3-D printing, and computer aided manufacturing applications. In this file format the surface of an object is described by triangles in three-dimensional space. The coordinates of the triangle vertexes are provided in the file. In this note we assume that the object of interest is inside of the enclosing set of triangles. Similar methods can also be used to generate material points outside of the enclosing triangles.

42 ENGINEERING↗

DOE OSTI2024

Controlling reversible phase transitions in rare-earth nickelates for novel memory devices

Resistive switching in correlated complex oxides is lucrative for emerging applications in neuromorphic computing, and densely scaled non-volatile memory. Electrical conductance of such complex oxides can be controllable switched across multiple orders of magnitude by either (a) electroforming a conduction channel (e.g., in tungsten oxide), or (b) inducing Mott-Hubbard transition (e.g., in rare-earth nickelates)– both via controlled migration of defects (such as oxygen vacancies) under applied bias. Nevertheless, the promise of such defect-driven electronic transitions are far from realized due to a lack of fundamental understanding of the atomic-scale processes that underlie migration and spatiotemporal evolution of oxygen vacancies over nano-to-mesoscopic length/timescales under applied electric field. In this project, we employ a synergistic integration of density functional theory (DFT) calculations, ab initio/classical molecular dynamics (AIMD/CMD) simulations, machine learning (ML), precision synthesis, and multi-modal X-ray imaging experiments to address this knowledge gap. Such an integrated approach offers to elucidate the correlations between subtle structural distortion and oxidation states; treat localized charge carriers; describe defect/ion transport in the presence of electric field; and, in turn, greatly advance the current understanding of microstructural evolution in complex oxides under applied bias. The fundamental knowledge gained from this work will enable precise control over hierarchical defect structures and unravel new routes to manipulate resistance states in complex oxides. This, in turn, will accelerate design of novel devices with desired set of neural functionalities, and high-speed densely-scaled resistive random access memory technologies.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Nitridation of atomically smooth (111) diamond surfaces using a low temperature Penning plasma discharge

Diamond is a material with a wide band gap that can host a variety of isolated paramagnetic defects, called “color centers”1. These color centers have attractive optical and magnetic properties suitable for quantum applications including quantum computing, nanophotonics and quantum sensing. The most common one is the negatively charged nitrogen vacancy (NV) color center. For quantum sensing, it is desirable to reduce the distance between the NV center and the analyte by using shallow color centers in order to increase sensitivity. In this context, the diamond surface termination is especially critical. The appropriate surface termination is required to stabilize the color centers and mitigate surface magnetic noise. The nitrogen termination of diamond has been postulated as highly desirable for the NV color center. The goal of this project is to take advantage of an electron beam-generated ExB low temperature plasma developed by the Princeton Collaborative Low Temperature Plasma Research Facility (PCRF) at the Princeton Plasma Physics Laboratory (PPPL) to nitridate the surface of (100) diamond single crystals with minimal surface damage. In this reactor, the use of a magnetized plasma enables gentle processing of materials sensitive to ion damage. This is in strong contrast to radiofrequency plasma processing reactors which are known to etch and sputter the surface. XPS measurements indicate the incorporation of nitrogen and oxygen atoms at the surface in similar amounts. XAS measurements confirm the nitridation and indicate that the nitrogen termination is different from the nitrogen termination obtained using radiofrequency plasma treatment4. The combination of these results indicates the formation of amid species at the (100) diamond surface that are promising for the stabilization of NV centers for quantum sensing.

36 MATERIALS SCIENCE↗

DOE OSTI2026

A New Route Toward Atomically Flat and Defect-Free Ge/SiGe Planar Heterostructures

Germanium-based planar heterostructures are emerging as versatile platforms for realizing quantum devices. In particular, planar Ge/SiGe quantum wells (QWs) host hole states with exceptionally high mobility and strong, electrically tunable spin–orbit interactions, enabling full electrical control of quantum information. A key requirement for these systems is the growth of microscopic, defect-free, atomically flat Ge QW heterostructures on relaxed or reverse-graded SiGe buffer layers, as well as on commercial Ge substrates. While several physical deposition techniques have demonstrated high-quality planar Ge/SiGe QWs, a major challenge remains: minimizing defect density typically requires high growth temperatures, which are incompatible with standard CMOS process flows. Here, we present a convenient low-temperature process for realizing high-quality planar SiGe/Ge heterostructures using a combination of thermal and electron-beam evaporation. We systematically map the effects of ex-situ and in-situ substrate preparation protocols, growth temperature, and post-deposition annealing conditions, and correlate these parameters with surface roughness and defect density. We find that in-situ oxide desorption conditions and post-annealing parameters have the most pronounced impact on improving surface quality. Under optimized conditions, we achieve atomically smooth surfaces with root-mean-square roughness σrms ≤ 10 Å and negligible defect density. Interestingly, thermally evaporated Ge layers exhibit oriented triangular crystallites that elongate upon post-annealing in the presence of high Ge vapor pressure. These results demonstrate that this simple, low-temperature deposition approach is a viable and effective route for achieving high-quality Ge QW heterostructures, with strong potential for scalable quantum computing and sensing applications.

Tripathi, Malvika [Fermilab] (ORCID:00000001989251↗

DOE OSTI2026

A New Route Toward Atomically Flat and Defect-Free Ge/SiGe Planar Heterostructures

Germanium-based planar heterostructures are emerging as versatile platforms for realizing quantum devices. In particular, planar Ge/SiGe quantum wells (QWs) host hole states with exceptionally high mobility and strong, electrically tunable spin–orbit interactions, enabling full electrical control of quantum information. A key requirement for these systems is the growth of microscopic, defect-free, atomically flat Ge QW heterostructures on relaxed or reverse-graded SiGe buffer layers, as well as on commercial Ge substrates. While several physical deposition techniques have demonstrated high-quality planar Ge/SiGe QWs, a major challenge remains: minimizing defect density typically requires high growth temperatures, which are incompatible with standard CMOS process flows. Here, we present a convenient low-temperature process for realizing high-quality planar SiGe/Ge heterostructures using a combination of thermal and electron-beam evaporation. We systematically map the effects of ex-situ and in-situ substrate preparation protocols, growth temperature, and post-deposition annealing conditions, and correlate these parameters with surface roughness and defect density. We find that in-situ oxide desorption conditions and post-annealing parameters have the most pronounced impact on improving surface quality. Under optimized conditions, we achieve atomically smooth surfaces with root-mean-square roughness σrms ≤ 10 Å and negligible defect density. Interestingly, thermally evaporated Ge layers exhibit oriented triangular crystallites that elongate upon post-annealing in the presence of high Ge vapor pressure. These results demonstrate that this simple, low-temperature deposition approach is a viable and effective route for achieving high-quality Ge QW heterostructures, with strong potential for scalable quantum computing and sensing applications.

Tripathi, Malvika [Fermilab] (ORCID:00000001989251↗

DOE OSTI2023

CMIP6-based Multi-model Streamflow Projections over the Conterminous US

This dataset presents an ensemble of streamflow projections based on the hydroclimate projections dataset supporting the SECURE Water Act Section 9505 Assessment for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). The six-member General Climate Model (GCM) ensemble from the Coupled Models Intercomparison Project phase 6 (CMIP6) downscaled using statistical (i.e., DBCCA) and dynamical (i.e., RegCM) and bias-corrected using two meteorological reference observations (Daymet & Livneh) are driven through two calibrated hydrologic models (VIC & PRMS) to simulate projected future hydrologic responses. This leads to the production of an ensemble of hydroclimate projections, including total runoff (surface runoff and baseflow) for 1980–2019 baseline and 2020–2059 near-term future periods under multiple emission scenarios at 1/24° (~4 km) spatial resolution across the CONUS. The total runoff projections are routed through the Routing Application for Parallel computatIon of Discharge (RAPID) routing model to produce an ensemble of streamflow projections for both periods across 2.7 million NHDPlusV2 stream reaches in the CONUS.

13 HYDRO ENERGY↗

DOE OSTI2022

Dayflow: CONUS Daily Streamflow Reanalysis, Version 1 (V1)

Dayflow V1 is a historical streamflow reanalysis dataset reconstructed for a 36-year period (1980-2015). The dataset provides both daily and monthly scale streamflow information at about 2.7 million NHDPlusV2 stream reaches in the conterminous US (CONUS). Dayflow is the result of a nationally scalable modeling framework that integrates the simulated runoff from the Variable Infiltration Capacity (VIC) model with the Routing Application for Parallel computatIon of Discharge (RAPID) routing model. Two types of streamflow products, simulated streamflow with or without assimilation of historic US Geological Survey (USGS) streamflow observations, are provided in Dayflow V1. A comprehensive evaluation at 7,526 USGS National Water Information System (NWIS) gauges is performed for both types of streamflow products. The resulting key evaluation metrics are also included in the Dayflow V1 Dataset.

13 HYDRO ENERGY↗

DOE OSTI2024

Infinite quantum signal processing

Quantum signal processing (QSP) represents a real scalar polynomial of degree d using a product of unitary matrices of size 2 × 2 , parameterized by ( d + 1 ) real numbers called the phase factors. This innovative representation of polynomials has a wide range of applications in quantum computation. When the polynomial of interest is obtained by truncating an infinite polynomial series, a natural question is whether the phase factors have a well defined limit as the degree d → ∞ . While the phase factors are generally not unique, we find that there exists a consistent choice of parameterization so that the limit is well defined in the ℓ 1 space. This generalization of QSP, called the infinite quantum signal processing, can be used to represent a large class of non-polynomial functions. Our analysis reveals a surprising connection between the regularity of the target function and the decay properties of the phase factors. Our analysis also inspires a very simple and efficient algorithm to approximately compute the phase factors in the ℓ 1 space. The algorithm uses only double precision arithmetic operations, and provably converges when the ℓ 1 norm of the Chebyshev coefficients of the target function is upper bounded by a constant that is independent of d . This is also the first numerically stable algorithm for finding phase factors with provable performance guarantees in the limit d → ∞ .

Dong, Yulong [Department of Mathematics, Universit↗

DOE OSTI2025

Estimating Eigenenergies from Quantum Dynamics: A Unified Noise-Resilient Measurement-Driven Approach

Ground state energy estimation in physical, chemical, and materials sciences is one of the most promising applications of quantum computing. In this work, we introduce a new hybrid approach that finds the eigenenergies by collecting real-time measurements and post-processing them using the machinery of dynamic mode decomposition (DMD). From the perspective of quantum dynamics, we establish that our approach can be formally understood as a stable variational method on the function space of observables available from a quantum many-body system. We also provide strong theoretical and numerical evidence that our method converges rapidly even in the presence of a large degree of perturbative noise, and show that the method bears an isomorphism to robust matrix factorization methods developed independently across various scientific communities. Our numerical benchmarks on spin and molecular systems demonstrate an accelerated convergence and a favorable resource reduction over state-of-the-art algorithms. The DMD-centric strategy can systematically mitigate noise and stands out as a leading hybrid quantum-classical eigensolver.

Shen, Yizhi↗