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DOE OSTI2022

ASC FY2023 Implementation Plan Revision 0

The DOE National Nuclear Security Administration (NNSA) Stockpile Stewardship Program (SSP) is an integrated technical program for maintaining the safety, security, and reliability of the U.S. nuclear stockpile. The SSP incorporates nuclear test data, computational modeling and simulation, and experimental facilities to advance understanding of nuclear weapons. The suite of data analyzed comes from activities including previous nuclear tests, stockpile surveillance, experimental research, and development and engineering programs. This integrated national program requires the continued use of experimental facilities and the computational capabilities to support the SSP missions. These component parts, in addition to an appropriately scaled production capability, enable NNSA to support stockpile requirements. The ultimate goal of the SSP, and thus of the Advanced Simulation and Computing (ASC) Program, is to ensure that the U.S. maintains a safe, secure, and effective strategic deterrent.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

First principles modeling of microscopic scintillation mechanisms using a Many-Body Green’s function approach (Final Report)

In 2010, we started the first lifecycle of a project on first principles modeling of microscopic unit processes that occur during scintillation in order to bring about a capability of formulating physics-based reaction-diffusion models that can predict scintillator light yield and explain their non-proportional response to the energy of incident ionizing radiation, with the hope that this will accelerate materials discovery that can mitigate this problem. During the ensuing years we focused on investigating from first principles the elementary quantum mechanical processes leading to polaron formation and exciton self-trapping in scintillators and identify the microscopic pathways leading to their transport. The effort to derive the master equation for carrier and exciton transport in realistic scintillators materials from first principles constitutes a challenge that requires an extensive background in various areas of computational physics. Previous theoretical investigations involved significant approximations and were limited in their scope. Most notably, the standard workhorse of the computational material scientist, density-functional theory, fails in describing the localized nature of self-trapped carriers as well as the electronic structure of rare-earth dopants. Advances in both algorithms and computational resources however, have rendered a comprehensive and accurate approach feasible. This has been amply demonstrated by the results produced by us during the first lifecycle of this project, which impressed upon the reviewers of the Independent Project Review: “The Review Team agreed, and commented that ’amazing and excellent progress had been made to date.’ The researchers tried and did improve the technology to advance the state of the art and show that it was adequate for the project.” In the following, we detail the progress achieved during each year of the project. We conclude by a final section where all our main achievements are summarized.

36 MATERIALS SCIENCE↗

DOE OSTI2024

ECP libraries and tools: An overview

The Exascale Computing Project (ECP) Software Technology and Co-Design teams addressed the growing complexities in high-performance computing (HPC) by developing scalable software libraries and tools that leverage exascale system capabilities. As we enter the exascale era, the need for reusable, optimized software solutions that can handle the unique challenges posed by these systems becomes increasingly important. The primary challenges the ECP teams faced were to create software libraries and tools that are performant on exascale architectures and portable and usable across diverse hardware platforms. Efforts addressed issues related to concurrent execution, memory management, and the integration of heterogeneous computing resources, such as GPUs from multiple vendors. The ECP’s strategy involved a structured development process encompassing the creation, optimization, and deployment of software in collaboration with industry, academia, and national laboratories. The project was organized into several technical areas: co-design of domain-specific suites with target applications, programming models and runtimes, development tools, mathematical libraries, data and visualization tools, and software ecosystem and delivery mechanisms. ECP has successfully developed a large portfolio of software libraries and tools that demonstrate significant improvements in performance and scalability on exascale systems. These products have been integrated into the Department of Energy’s computing facilities, supporting various scientific applications and ensuring robust performance across different hardware setups. ECP advancements in software development for exascale computing highlight the importance of a collaborative and adaptive approach to handling next-generation HPC systems complexities. The lessons learned emphasize the need for continuous engagement with end-users and vendors, and the importance of maintaining a balance between innovation and practical implementation. Future efforts will focus on ensuring scalability, keeping pace with rapid hardware advancements, and further enhancing the interoperability and usability of the software ecosystem. In conclusion, subsequent articles in this special issue provide in-depth discussions and case studies into specific library and tool efforts.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Beyond Coherent Oxide Heterostructures: Atomic-Scale Structure of Misfit Dislocations

Nanoscale design of complex oxide heterostructures and thin films is imperative as they have significant promise in novel technological applications. A coherent interface is formed in oxide heterostructures with small mismatches, and the lattice mismatch is completely compensated by elastic strain. In semi-coherent oxide heterostructures, when an epitaxial layer is grown on the substrate above the critical thickness of the film, misfit dislocations are formed to mitigate the strain between the two materials with dissimilar lattice constants. Key properties of semi-coherent oxide heterostructures are influenced or even controlled by the presence of misfit dislocations. Thus, it is critical to understand the atomic-scale structure of semi-coherent oxide heterostructures, specifically the structure of misfit dislocations that are ubiquitous at such heterointerfaces. Numerous state-of-the-art experiments have reported emergent phenomena at semi-coherent oxide heterostructures, wherein misfit dislocations play a crucial role. Yet, their atomic-scale and nanoscale structure is not always discernable from experiments. Because of large system sizes, computational studies dedicated to examining misfit dislocations in semi-coherent oxide heterostructures are still in their infancy. This review aims to summarize the recent advancements and challenges involved in computational studies elucidating the atomic-scale structure of misfit dislocations in semi-coherent oxide heterostructures and motivate future computational efforts.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2024

Revolutionizing Neuromorphic Computing for Science (Brochure on the 2024 ASCR Workshop on Neuromorphic Computing for Science)

The ASCR basic research needs for Neuromorphic Computing for Science workshop was held in September 2024. The workshop brochure and report aim to inform and draft a set of grand challenges for advancing the field of neuromorphic computing and developing proof of principle neuromorphic circuits applicable for High Performance Computer (HPC) acceleration for scientific discovery, and brainstorm ideas needed for a successful, robust, and world leading basic research program. The resulting priority research directions are: (1) Neuromorphic computing circuit primitives; (2) Connectivity, communication, and hardware integration; (3) Neuroscience-derived dynamics and algorithms; and (4) Ecosystem for scalable neuromorphic co-design. Breakthroughs in understanding, designing, and prototyping the circuitry and simulation capabilities for a truly neuromorphic computer are essential to enable progress in the field.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Revolutionizing Neuromorphic Computing for Science (Report for the 2024 ASCR Workshop on Neuromorphic Computing for Science)

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Proposing an Abstracted Interface and Protocol for Computer Systems

While it made sense for historical reasons to develop different interfaces and protocols for memory channels, CPU to CPU interactions, and I/O devices, ongoing developments in the computer industry are leading to more converged requirements and physical implementations for these interconnects. As it becomes increasingly common for advanced components to contain a variety of computational devices as well as memory, the distinction between processors, memory, accelerators, and I/O devices becomes increasingly blurred. As a result, the interface requirements among such components are converging. There is also a wide range of new disruptive technologies that will impact the computer market in the coming years, including 3D integration and emerging NVRAM memory. Optimal exploitation of these technologies cannot be done with the existing memory, storage, and I/O interface standards. The computer industry has historically made major advances when industry players have been able to add innovation behind a standard interface. The standard interface provides a large market for their products and enables relatively quick and widespread adoption. To enable a new wave of innovation in the form of advanced memory products and accelerators, we need a new standard interface explicitly designed to provide both the performance and flexibility to support new system integration solutions

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Proposing an Abstracted Interface and Protocol for Computer Systems

While it made sense for historical reasons to develop different interfaces and protocols for memory channels, CPU to CPU interactions, and I/O devices, ongoing developments in the computer industry are leading to more converged requirements and physical implementations for these interconnects. As it becomes increasingly common for advanced components to contain a variety of computational devices as well as memory, the distinction between processors, memory, accelerators, and I/O devices become s increasingly blurred. As a result, the interface requirements among such components are converging. There is also a wide range of new disruptive technologies that will impact the computer market in the coming years , including 3D integration and emerging NVRAM memory. Optimal exploitation of these technologies cannot be done with the existing memory, storage, and I/O interface standards. The computer industry has historically made major advances when industry players have been able to add innovation behind a standard interface. The standard interface provides a large market for their products and enables relatively quick and widespread adoption. To enable a new wave of innovation in the form of advanced memory products and accelerators, we need a new standard interface explicitly designed to provide both the performance and flexibility to support new system integration solutions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

SHARP: a distributed GPU-based ptychographic solver

Ever brighter light sources, fast parallel detectors and advances in phase retrieval methods have made ptychography a practical and popular imaging technique. Compared to previous techniques, ptychography provides superior robustness and resolution at the expense of more advanced and time-consuming data analysis. Therefore, by taking advantage of massively parallel architectures, high-throughput processing can expedite this analysis and provide microscopists with immediate feedback. These advances allow real-time imaging at wavelength-limited resolution, coupled with a large field of view. This article describes a set of algorithmic and computational methodologies used at the Advanced Light Source and US Department of Energy light sources. These are packaged as a CUDA-based software environment named SHARP, aimed at providing state-of-the-art high-throughput ptychography reconstructions for the coming era of diffraction-limited light sources.

47 OTHER INSTRUMENTATION↗

DOE OSTI2017

Petascale supercomputing to accelerate the design of high-temperature alloys

Recent progress in high-performance computing and data informatics has opened up numerous opportunities to aid the design of advanced materials. Herein, we demonstrate a computational workflow that includes rapid population of high-fidelity materials datasets via petascale computing and subsequent analyses with modern data science techniques. We use a first-principles approach based on density functional theory to derive the segregation energies of 34 microalloying elements at the coherent and semi-coherent interfaces between the aluminium matrix and the θ '-Al 2 Cu precipitate, which requires several hundred supercell calculations. We also perform extensive correlation analyses to identify materials descriptors that affect the segregation behaviour of solutes at the interfaces. Finally, we show an example of leveraging machine learning techniques to predict segregation energies without performing computationally expensive physics-based simulations. As a result, the approach demonstrated in the present work can be applied to any high-temperature alloy system for which key materials data can be obtained using high-performance computing.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Asynchronous in situ connected-components analysis for complex fluid flows

Fluid flow in porous media is at the heart of problems such as groundwater contamination and carbon sequestration, and presents an important challenge for scientific computing. For this class of problem, large three-dimensional simulations are performed to advance scientific inquiry. On massively parallel computing systems, the volume of data generated by such approaches can become a productivity bottleneck if the raw data generated from the simulation is analyzed in a post-processing step. We present a physics-based framework for in situ data reduction that is theoretically grounded in multiscale averaging theory. We show how task parallelism can be exploited to concurrently perform a variety of analysis tasks with data-dependent costs, including the generation of iso-surfaces, morphological analyses, and connected components analysis. A task management framework is constructed to launch asynchronous analysis threads, manage dependencies between different tasks, promote data locality and hide the impact of data transfers. The framework is applied to analyze GPU-based simulations of two-fluid flow in porous media, generating a set of averaged measures that represents the overall system behavior. We demonstrate how the approach can be applied to perform physically-consistent averaging over fluid subregions using connected components analysis. Simulations performed on Oak Ridge National Lab s Titan supercomputer are profiled to demonstrate the performance of the associated multi-threaded in situ analysis for typical production simulation of two-fluid flow.

Mcclure, James↗

DOE OSTI2016

Asynchronous In Situ Connected-Components Analysis for Complex Fluid flows, In: ISAV '16 Proceedings of the 2nd Workshop on In Situ Infrastructures for Enabling Extreme-scale Analysis and Visualization

The simulation of multiscale physics is an important challenge for scientific computing. For this class of problem, large three-dimensional simulations are performed to advance scientific inquiry. On massively parallel computing systems, the volume of data generated by such approaches can become a productivity bottleneck if the raw data generated from the simulation is analyzed in a post-processing step. To address this, we present a physics-based framework for in situ data reduction that is theoretically grounded in multiscale averaging theory. We show how task parallelism can be exploited to concurrently perform a variety of analysis tasks with data-dependent costs, including the generation of iso-surfaces, morphological analyses, and connected components analysis. All analyses are performed in parallel using distributed memory and use the same domain decomposition as the simulation. A task management framework is constructed to leverage available parallelism within a node for analysis. The capabilities of the framework are to launch asynchronous analysis threads, manage dependencies between different tasks, promote data locality and minimize the impact of data transfers. The framework is applied to analyze GPU-based simulations of two-fluid-phase flow in porous media, generating a set of averaged measures that represents the overall system behavior. We demonstrate how the approach can be applied to perform physically-consistent analysis over fluid sub-regions determined from connected components analysis. Simulations performed on Oak Ridge National Lab's Titan supercomputer are profiled to demonstrate the performance of the associated multi-threaded in situ analysis approach for typical production simulation of two-fluid-phase flow.

Computer Science↗

DOE OSTI2022

Super-Resolution Approaches in Three-Dimensions for Classification and Screening of Commercial-Off-The-Shelf Components

X-ray computed tomography is generally a primary step in characterization of defective electronic components, but is generally too slow to screen large lots of components. Super-resolution imaging approaches, in which higher-resolution data is inferred from lower-resolution images, have the potential to substantially reduce collection times for data volumes accessible via x-ray computed tomography. Here we seek to advance existing two-dimensional super-resolution approaches directly to three-dimensional computed tomography data. Multiple scan resolutions over a half order of magnitude of resolution were collected for four classes of commercial electronic components to serve as training data for a deep-learning, super-resolution network. A modular python framework for three-dimensional super-resolution of computed tomography data has been developed and trained over multiple classes of electronic components. Initial training and testing demonstrate the vast promise for these approaches, which have the potential for more than an order of magnitude reduction in collection time for electronic component screening.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Optimization of Functionally Graded Materials Using Additive Manufacturing: An Integrated Experimental and Computational Approach (Abbreviated Final Report)

Many advanced technologies, such as next-generation energy systems, aerospace vehicles, and defense platforms, require materials that can withstand extreme environments, including high temperatures, corrosion, and radiation, while remaining strong and lightweight. Traditionally, joining different materials to achieve these properties introduces weak, failure-prone interfaces and defects that limit performance. Our research aimed to overcome this challenge by using additive manufacturing, specifically directed energy deposition, to create functionally graded materials—components with smooth transitions between different metals. This approach eliminates sharp boundaries and allows for tailored material properties throughout a part.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Arjun Shankar: Advancing National Missions through Smart Data

Computer scientist Arjun Shankar is director of the Compute and Data Environment for Science (CADES), ORNL’s multidisciplinary big data computing center. CADES offers computing, networking and data analytics to facilitate workflows for both ORNL and external research projects.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Computer Vision-Enabled Smart Traffic Monitoring for Sustainable Transportation Management

Transportation accounts for a significant portion of total global energy consumption. Excessive energy consumption usually occurs in urban traffic environments with congestion and travel delays. With the advancement of remote sensing and computer vision technologies, real-time traffic conditions can be monitored. Therefore, sustainable transportation management strategies can be developed to optimize the overall energy and environment performance and reduce congestion and emissions. This work presents a smart traffic monitoring system based on remote camera sensors. Real-time and historical traffic conditions at the US Department of Energy’s Oak Ridge National Laboratory (ORNL) were monitored and analyzed to develop optimal transportation management strategies for sustainability. Computer vision algorithms were developed and applied to process the real-time camera data to obtain complete traffic information across the ORNL campus. Weeks of historical data were collected and processed to analyze the traffic and identify bottlenecks. The proposed traffic monitoring and management approach can be applied and extended to benefit other campuses or urban areas.

Shao, Yunli↗

DOE OSTI2018

Computational Design and Development of a New, Lightweight Cast Alloy for Advanced Cylinder Heads in High-Efficiency, Light-duty Engines

The objective of the collaborative multi-year Cast Lightweight Alloy program was to develop a new, lightweight, cast aluminum alloy for automotive engines with better high temperature properties than the incumbent alloys (B319, A356, A356+0.5Cu) to allow for more efficient engine operation. The program kicked off with the generation of alloy concepts and selecting alloy models with specific milestones for establishing property requirements, generating design concepts and selecting models for casting design. Following this, sub-scale prototypes of the promising design concepts were cast and characterized in order to validate the design concepts and develop data for subsequent model calibration and improvement. Using the calibrated models, a set of alloy designs were created. This first generation set of designs were validated through production and characterization of lab-scale castings. With the learnings from the lab-scale prototypes and the improvements in the computational models, final embodiment design of the new alloy was completed. The final alloy designs were cast at lab-scale and also at component-scale to characterize its final properties. An alloy recyclability analysis and cost modelling was also performed.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Compare Mechanistic Predictions for Doped UO 2 Mechanical Response and Other Properties with Empirical Models and Experimental Measurements

The U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation program develops predictive capabilities using computational methods for the analysis and design of advanced reactor and fuel cycle systems. This program has been supporting the development of BISON, a high-fidelity, high resolution fuel performance tool at the engineering scale. As part of its development, additional modeling capabilities and improvements have been developed for relevant fuel forms. In this work, a fuel creep deformation model for Cr-doped fuel has been implemented into BISON, along with improvements to the empirical UO 2 fuel creep model based on experimental data and improvements to the radial power factor calculation for doped fuels. This work allows for more accurate simulation analyses for both UO 2 and doped-UO 2 fuels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗