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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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At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

An approach to combine neutron and ion irradiation data to accelerate material qualification for nuclear reactors

Next-generation nuclear power plants are generally characterized by higher operating temperatures, increased neutron fluences and energies, and distinct corrosive coolant environments versus the existing light water reactor fleet. Whether using existing materials in new environments, newly developed materials tailored for these environments, or new manufacturing methods, the traditional decades-long approach for materials qualification does not facilitate rapid deployment. Ion irradiation has demonstrated success in reproducing material microstructure and select property evolution resulting from neutron irradiation with three to four orders of magnitude reduction in time and cost, making it an ideal candidate for accelerated irradiation testing. Because microstructure has a large impact on bulk material properties, limited neutron irradiation data at lower damage levels can in principle be combined with accelerated ion testing results and modeling and simulation to form an accurate prediction of microstructure evolution and select properties under different neutron irradiation conditions and at higher damage levels. The objective of this work is to present a conceptual framework of specific steps to fulfill several technical challenges associated with qualifying materials for performance in radiation environments on an accelerated time frame. A brief review of the regulatory landscape for materials in nuclear environments is presented, followed by additional overviews to understand the current state of the art for correlation of materials properties across radiation environments using experimental and computational methodologies. Finally, the roles of academia, national laboratories, and industry in the advancement of this accelerated materials qualification framework are discussed as a path forward, with possible case studies presented.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Computationally aided, entropy-driven synthesis of highly efficient and durable multi-elemental alloy catalysts

Multi-elemental alloy nanoparticles (MEA-NPs) hold great promise for catalyst discovery in a virtually unlimited compositional space. However, rational and controllable synthesize of these intrinsically complex structures remains a challenge. Here, we report the computationally aided, entropy-driven design and synthesis of highly efficient and durable catalyst MEA-NPs. The computational strategy includes prescreening of millions of compositions, prediction of alloy formation by density functional theory calculations, and examination of structural stability by a hybrid Monte Carlo and molecular dynamics method. Selected compositions can be efficiently and rapidly synthesized at high temperature (e.g., 1500 K, 0.5 s) with excellent thermal stability. We applied these MEA-NPs for catalytic NH 3 decomposition and observed outstanding performance due to the synergistic effect of multi-elemental mixing, their small size, and the alloy phase. We anticipate that the computationally aided rational design and rapid synthesis of MEA-NPs are broadly applicable for various catalytic reactions and will accelerate material discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2024

PaRSEC: Scalability, flexibility, and hybrid architecture support for task-based applications in ECP

This paper highlights the most significant enhancements made to PaRSEC, a scalable task-based runtime system designed for hybrid machines, during the Exascale Computing Project (ECP). The enhancements focus on expanding the capabilities of PaRSEC to address the evolving landscape of parallel computing. Notable achievements include the integration of support for three major types of accelerators (NVIDIA, AMD, and Intel GPUs), the refinement and increased flexibility of the communication subsystem, and the introduction of new programming interfaces tailored for irregular applications. Additionally, the project resulted in the development of powerful debugging and performance analysis tools aimed at assisting users in understanding and optimizing their applications. We present a comprehensive demonstration of these advancements through a series of benchmarks and applications within ECP and beyond, thereby showcasing the enhanced capabilities of PaRSEC across the diverse architectures within the ECP, providing valuable insights into the runtime system’s adaptability and performance across varied computing environments.

Bouteiller, Aurelien↗
DOE OSTI2020

Quantum Computing for Neutrino-Nucleus Scattering

Neutrino-nucleus cross section uncertainties are expected to be a dominant systematic in future accelerator neutrino experiments. The cross sections are determined by the linear response of the nucleus to the weak interactions of the neutrino, and are dominated by energy and distance scales of the order of the separation between nucleons in the nucleus. These response functions are potentially an important early physics application of quantum computers. Here we present an analysis of the resources required and their expected scaling for scattering cross section calculations. The current estimates of Trotter steps needed to achieve an energy resolution of 10 MeV and the number of CNOT gates for analyzing Ar40 highlights the need for significant improvements in algorithms. We also examine simple small-scale neutrino-nucleus models on modern quantum hardware. In this paper, we use variational methods to obtain the ground state of a three nucleon system (the triton) and then implement the relevant time evolution. To tame the errors in present-day NISQ devices, we explore the use of different error-mitigation techniques to increase the fidelity of the calculations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Accelerating Lossy and Lossless Compression on Emerging BlueField DPU Architectures

Data compression has become a crucial technique in addressing performance bottlenecks caused by increasing data volumes in High-Performance Computing (HPC), Big Data, and Deep Learning (DL). Despite its potential to boost system performance, recent studies have identified significant challenges with existing compression methods, mainly due to their high computational demands amidst continuously growing data sizes. Concurrently, the advent of Data Processing Units (DPUs), equipped with programmable System-on-Chip (SoC) and specialized compression accelerators, offers a promising opportunity to alter the landscape of data compression. This paper explores the complexities and potential of leveraging NVIDIA BlueField DPUs to accelerate lossy and lossless compression. Towards this, we introduce PEDAL, an innovative library that leverages the hardware capabilities of DPUs to unify and optimize data compression designs. Moreover, we seamlessly co-design PEDAL with the popular MPICH MPI library, demonstrating up to 101x speedup in compression time and 88x decrease in communication latency. Drawing on these achievements, we share our experience with various research communities about accelerating data compression on DPUs in communication-oriented HPC scenarios.

Li, Yuke↗
DOE OSTI2020

On the Efficient Evaluation of the Exchange Correlation Potential on Graphics Processing Unit Clusters

The predominance of Kohn–Sham density functional theory (KS-DFT) for the theoretical treatment of large experimentally relevant systems in molecular chemistry and materials science relies primarily on the existence of efficient software implementations which are capable of leveraging the latest advances in modern high-performance computing (HPC). With recent trends in HPC leading toward increasing reliance on heterogeneous accelerator-based architectures such as graphics processing units (GPU), existing code bases must embrace these architectural advances to maintain the high levels of performance that have come to be expected for these methods. In this work, we purpose a three-level parallelism scheme for the distributed numerical integration of the exchange-correlation (XC) potential in the Gaussian basis set discretization of the Kohn–Sham equations on large computing clusters consisting of multiple GPUs per compute node. In addition, we purpose and demonstrate the efficacy of the use of batched kernels, including batched level-3 BLAS operations, in achieving high levels of performance on the GPU. We demonstrate the performance and scalability of the implementation of the purposed method in the NWChemEx software package by comparing to the existing scalable CPU XC integration in NWChem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey

Uncertainty quantification (UQ) includes the characterization, integration, and propagation of uncertainties that result from stochastic variations and a lack of knowledge or data in the natural world. Monte Carlo (MC) method is a sampling-based approach that has widely used for quantification and propagation of uncertainties. However, the standard MC method is often time-consuming if the simulation-based model is computationally intensive. This article gives an overview of modern MC methods to address the existing challenges of the standard MC in the context of UQ. Specifically, multilevel Monte Carlo (MLMC) extending the concept of control variates achieves a significant reduction of the computational cost by performing most evaluations with low accuracy and corresponding low cost, and relatively few evaluations at high accuracy and corresponding high cost. Multifidelity Monte Carlo (MFMC) accelerates the convergence of standard Monte Carlo by generalizing the control variates with different models having varying fidelities and varying computational costs. Multimodel Monte Carlo method (MMMC), having a different setting of MLMC and MFMC, aims to address the issue of UQ and propagation when data for characterizing probability distributions are limited. Multimodel inference combined with importance sampling is proposed for quantifying and efficiently propagating the uncertainties resulting from small data sets. All of these three modern MC methods achieve a significant improvement of computational efficiency for probabilistic UQ, particularly uncertainty propagation. Finally, an algorithm summary and the corresponding code implementation are provided for each of the modern MC methods. The extension and application of these methods are discussed in detail.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Bringing OpenCL to Commodity RISC-V CPUs

The importance of open-source hardware has been increasing in recent years with the introduction of the RISC-V Open ISA. This has also accelerated the push for support of the open-source software stack from compiler tools to full-blown operating systems. Parallel computing with today’s Application Programming Interfaces such as OpenCL has proven to be effective at leveraging the parallelism in commodity multi-core processors and programmable parallel accelerators. However, to the best of our knowledge, there is currently no publicly available implementation of OpenCL targeting commodity RISC-V processors that is accessible to the open-source community. Besides opening RISC-V to the existing rich variety of scientific parallel applications, OpenCL also provides access to a unique genre of benchmarks useful in computer architecture research. In this work, we extended an Open-source implementation of OpenCL to target RISC-V CPUs. Our work not only cover commodity multi-core RISC-V processors, but also plethora of low- profile embedded RISC-V CPUs that often do not support atomic instructions or multi-threading.

Tine, Blaise↗
DOE OSTI2022

Linear stability analysis via simulated annealing and accelerated relaxation

Simulated annealing (SA) is a kind of relaxation method for finding equilibria of Hamiltonian systems. A set of evolution equations is solved with SA, which is derived from the original Hamiltonian system so that the energy of the system changes monotonically while preserving Casimir invariants inherent to noncanonical Hamiltonian systems. The energy extremum reached by SA is an equilibrium. Since SA searches for an energy extremum, it can also be used for stability analysis when initiated from a state where a perturbation is added to an equilibrium. The procedure of the stability analysis is explained, and some examples are shown. Because the time evolution is computationally time consuming, efficient relaxation is necessary for SA to be practically useful. An acceleration method is developed by introducing time dependence in the symmetric kernel used in the double bracket, which is part of the SA formulation described here. An explicit formulation for low-beta reduced magnetohydrodynamics (MHD) in cylindrical geometry is presented. In conclusion, since SA for low-beta reduced MHD has two advection fields that relax, it is important to balance the orders of magnitude of these advection fields.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Development of a colinear Second-Harmonic Orthogonal Polarization (SHOP) interferometer for electron areal density measurements in Magnetically Insulated Transmission Lines (MITLs)

Experimental measurements of low density plasmas forming in Magnetically Insulated Transmission Line (MITL) regions are desired to improve our understanding of current loss and power flow. Therefore, a new optical interferometer diagnostic was commissioned via this LDRD project. To measure the expected 10 13 - 10 17 cm -3 electron densities inside the 0.5 - 6 mm Anode-Cathode (A-K) gaps, a colinear SHOP interferometer diagnostic was constructed. The diagnostic was initially fielded on the University of New Mexico (UNM) Helicon-Cathode (HelCat) plasma device which provided a highly repeatable and well understood plasma source for which the colinear SHOP interferometer’s functionality could be verified and measured. Utilizing the highly repeatable plasma source and shot averaging, the interferometer was able to achieve an areal density sensitivity of 1×10 14 cm -2 . This work at UNM lead to a Review of Scientific Instruments (RSI) publication [20], DOI:10.1063/5.0101687. After the diagnostic’s capability was proven at UNM, the colinear SHOP interferometer was commissioned for use on the Sandia National Laboratories (SNL) Mykonos accelerator. Here, it provided the first temporal areal density measurements of plasma formation in a parallel plate MITL. The diagnostic was able to achieve a single shot (no multi-shot averaging like at UNM) areal density sensitivity of 1×10 15 cm -2 along a ~ 2mm probing path length, which provided adequate capability to conduct fundamental physics research of MITL plasma formation. CHICAGO and ALEGRA simulations support the diagnostics experimental findings. More experimental and computational work will continue, likely leading to another publication(s). The smaller scale Mykonos accelerator work has also provided justification that the colinear SHOP interferometer is a capable diagnostic for measuring plasma areal densities in the inner MITL and convolute regions of larger TW-class accelerators like SNL’s Z machine.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Advanced Research Directions on AI for Science, Energy, and Security: Report on Summer 2022 Workshops

Over the past decade, fundamental changes in artificial intelligence (AI)—from foundational to applied—have delivered dramatic insights across a wide breadth of U.S. Department of Energy (DOE) mission space. AI is helping to augment and improve scientific and engineering workflows (e.g., for control, design, and dramatic performance gains through surrogate models) in national security, the Office of Science, and DOE’s applied energy programs. The progress and potential for AI in DOE science was captured in the 2020 “AI for Science” report from the DOE laboratory community in collaboration with academia and industry. Specific scientific areas ready to further leverage the power of AI ranged from the scale and performance of computational models to data analysis to creating new classes of observations using computer vision. Since that report, the scale and scope of scientific AI have accelerated, revealing new, emergent properties that yield insights that go beyond enabling opportunities to being potentially transformative in the way that scientific problems are posed and solved. Thus, under the guidance of both the Office of Science (SC) and the National Nuclear Security Administration (NNSA), the DOE national laboratories organized a series of workshops in 2022 to gather input on new and rapidly emerging opportunities and challenges of scientific AI. This 2023 report is a synthesis of those workshops. The scientific community believes AI can have a foundational impact on a broad range of DOE missions, including science, energy, and national security. Further, DOE has unique capabilities that enable the community to drive progress in scientific use of AI, building on long-standing DOE strengths and investments in computation, data, and communications infrastructure, spanning the Energy Sciences Network (ESnet), the Exascale Computing Project (ECP), and integrative programs such as the NNSA Office of Defense Programs Advanced Simulation and Computing (ASC) and the SC Scientific Discovery through Advanced Computing (SciDAC) programs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Portability: A Necessary Approach for Future Scientific Software

Today's world of scientific software for High Energy Physics (HEP) is powered by x86 code, while the future will be much more reliant on accelerators like GPUs and FPGAs. The portable parallelization strategies (PPS) project of the High Energy Physics Center for Computational Excellence (HEP/CCE) is investigating solutions for portability techniques that will allow the coding of an algorithm once, and the ability to execute it on a variety of hardware products from many vendors, especially including accelerators. We think without these solutions, the scientific success of our experiments and endeavors is in danger, as software development could be expert driven and costly to be able to run on available hardware infrastructure. We think the best solution for the community would be an extension to the C++ standard with a very low entry bar for users, supporting all hardware forms and vendors. We are very far from that ideal though. We argue that in the future, as a community, we need to request and work on portability solutions and strive to reach this ideal.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Accelerating Hydraulic Fracture Imaging by Deep Transfer Learning

Deep transfer learning has a great success story in computer vision (CV), natural language processing, and many other fields. In this communication, we are going to push forward the deep transfer learning to the hydraulic fracture imaging problem by proposing a two-step approach: 1) train a convolutional neural network (CNN) to reconstruct target geometries by a relatively large amount of approximated field patterns generated from a simplified model and 2) fine-tune the top layers of transferred CNN by a small amount of true field patterns generated through a full model. The advantages include the rapid generation of large amount of data through the simplified model and the high reconstruction accuracy through a careful design of the deep transfer learning. Here, the CNN trained through the deep transfer learning can accurately reconstruct the lateral extent and direction of fractures with unseen conductivity and white Gaussian noise, showing a notable acceleration/accuracy enhancement over the previous CNN trained by mixing a small number of true data into the approximated data as data augmentation.

02 PETROLEUM↗
DOE OSTI2024

Object Proxy Patterns for Accelerating Distributed Applications

Workflow and serverless frameworks have empowered new approaches to distributed application design by abstracting compute resources. However, their typically limited or one-size-fits-all support for advanced data flow patterns leaves optimization to the application programmer—optimization that becomes more difficult as data become larger. The transparent object proxy, which provides wide-area references that can resolve to data regardless of location, has been demonstrated as an effective low-level building block in such situations. Here we propose three high-level proxy-based programming patterns—distributed futures, streaming, and ownership—that make the power of the proxy pattern usable for more complex and dynamic distributed program structures. We motivate these patterns via careful review of application requirements and describe implementations of each pattern. As a result, we evaluate our implementations through a suite of benchmarks and by applying them in three meaningful scientific applications, in which we demonstrate substantial improvements in runtime, throughput, and memory usage.

Distributed Computing↗
DOE OSTI2024

Zero-RK Acceleration of Low-Life-cycle Carbon Fuel (LLFC)Simulations

Designing modern combustion systems now relies on computer models that predict how changes in design will affect performance. These models have replaced older methods that relied on the designer’s intuition or costly and time-consuming physical testing. By using improved models, design cycles can be shortened and cleaner and more efficient combustion devices can be created. This project aims to improve computer simulations of low-life-cycle carbon fuels (LLCFs) with the goal of making these simulations faster and more accurate for predicting combustion in vehicles.

02 PETROLEUM↗
DOE OSTI2020

Small-Scale Recycling of Irradiated Uranium and Transuranic Elements (Np, Pu) Using 3D-Printed Centrifugal Contactors

Niowave’s technology employs super-conducting linear accelerators to induce fission on low-enriched uranium targets followed by subsequent radiochemical processing to purify 99 Mo as well as a number of other fission products. Argonne’s Radiochemistry group (CFCT), in partnership with Niowave and UNLV, will be managing the TCF project to develop a closed-cycle loop for Niowave’s uranium targets. The roles of the DOE national laboratory partner will be to 1) develop a basic chemical understanding of the separations and purifications required to meet industry standards, 2) initiate the additive manufacturing (AM) of the centrifugal contactors, 3) develop a process flowsheet using Argonne computer codes.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Transformation of Spinors in Accelerators and Beam Transfer Lines

The stable spin direction and the spin tune are two important concepts used when polarized beams are transferred along beam lines or circulating in an accelerator. One of the methods to calculate the stable spin direction and the spin tune of the reference particle of a polarized beam bunch is by integrating simultaneously the Lorentz Force $\vec{F}$ = q($\vec{E}$ +$\vec{v}$ x $\vec{B}$) and the BMT equation of the reference particle as it is transported by the magnets of a beam line or accelerator. A modified version of the Raytrace code was used to integrate the Lorentz equation and the BMT equation. Using the spin motion which is governed by the BMT equation one can compute both, the stable spin direction of the reference particle at any point along the reference orbit of an accelerator or a beam line, and the spin tune of the closed orbit in an accelerator. The simultaneous integration of the Lorentz force and of the BMT equation is the most accurate method to calculate the trajectory and the spin motion of a charged particle moving in an electromagnetic field, however this method requires the accurate description of the electromagnetic fields of the elements in which the charged particles are moving. An alternative method of calculating the stable spin direction and the spin tune of spin ½ charged particles along the reference trajectory of a beam line or an accelerator is the transformation of the spinor which is the wavefunction of spin ½ particles. This method requires the knowledge of the stable spin direction of each element of the beam line or the accelerator. This technical note describes the “spinor-method” to calculate the stable-spin- direction and the spin-tune of a spin ½ particle moving along the reference trajectory of a beam line or an accelerator.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Active Learning Surrogates for Integrating Electron Microscopy and Computational Insights from Simulations in Autonomous Experiments

Artificial Intelligence (AI) combined with simulations and experiments has great potential to accelerate scientific discovery across technology and pharmaceuticals. However, the gap between simulations and experiments is challenging due to disparities in time and scale, making it difficult to estimate properties like energy and electronic states from experiments, and to provide feedback based on theoretical insights.Our research addresses the challenge by developing unique deep kernel based surrogate models that learns from microscopic images, mapping structural features to energy differences from defect formation. We start with full-training using simulated images to determine optimal settings, establishing a baseline for active learning. Using these settings from the baseline, active learning is trained, and predicts structures along simulation trajectories based on uncertainty and energetic stability, thus reducing data requirements, simulation time and computational costs. The results demonstrate that the model achieves a low average error margin of approximately 0.03 meV, indicating good performance. To enhance feature extraction and reconstruction capabilities, we developed an autoencoder-decoder as additional surrogate to create latent space to capture essential features, enabling precise comparisons between simulations and experiments. The results from this model achieved a reconstruction loss of around 0.2 and accurately reconstructed molecular structures.Overall, this work advances the steering of experiments through computational simulations by employing a surrogate models that actively predicts the trajectories of structural evolution, achieving time-to-solution comparable to experimental measurements.

Saranathan, Gayathri [Hewlett-Packard]↗