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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Toward integrating high-fidelity CFD approaches in the thermal-hydraulic analysis of turbulent dry cask systems

Nuclear power plants have been supplying resilient and reliable electricity for decades, contributing to energy independence of the U.S.. However, nuclear waste management remains one of the most significant challenges in the industry. The safety of dry cask storage systems relies heavily on their thermal-hydraulic performance. Computational Fluid Dynamics (CFD) simulations are often used to demonstrate this performance and ensure that the system design meets safety standards. This study presents reduced numerical models for various types of dry cask systems. These numerical models can produce efficient and fast results based on the employed modeling strategies. Additionally, the study uses a novel approach to high-fidelity simulations to evaluate modeling assumptions in dry cask modeling. Large Eddy Simulations (LES) are used for this purpose, particularly in regions where fluid velocity is relatively high and the turbulence characteristics become important. Furthermore, the results of these high-fidelity simulations will enhance the interpretation of outcomes produced from a lower-fidelity CFD model.

CFD↗
DOE OSTI2022

Application Results on Early Exascale Hardware

This Exascale Computing Project (ECP) milestone report summarizes the status of 27 of the 31 ECP Applications Development (AD) subprojects at the end of FY21. In November and December of 2021, a comprehensive assessment of AD projects was conducted by the ECP leadership along with external subject matter experts (SMEs). (NNSA application projects are reviewed separately using the ASC milestone process.) The AD review committee—consisting of the AD lead, AD deputy, Level 3 (L3), and at least one external project SME—was tasked with evaluating each project’s progress relative to ECP project goals specified in the FY21 timeline. Key areas of focus were code maturity and performance on pre-exascale systems, an in-depth analysis of final key performance parameter (KPP) verification contracts, and future R&D priorities in the final year of ECP and beyond. As such, this report contains not only an accurate snapshot of each subproject’s current status but also represents a broad account of successes and challenges in porting large scientific applications to DOE’s next-generation high-performance computing architectures – the Frontier and Aurora systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Phase-field modeling of diffusional phase behaviors of solid surfaces: A case study of phase-separating Li X FePO 4 electrode particles

In this paper, we present a comprehensive phase-field model for simulating diffusion-mediated kinetic phase behaviors near the surface of a solid particle. The model incorporates elastic inhomogeneity and anisotropy, diffusion mobility anisotropy, interfacial energy anisotropy, and Cahn–Hilliard diffusion kinetics. The free energy density function is formulated based on the regular solution model taking into account the possible solute-surface interaction near the surface. The coherency strain energy is computed using the Fourier-spectral iterative-perturbation method due to the strong elastic inhomogeneity with a zero surface traction boundary condition. Employing a phase-separating Li X FePO 4 electrode particle for Li-ion batteries as a model system, we perform parametric three-dimensional computer simulations. The model permits the observation of surface phase behaviors that are different from the bulk counterpart. For instance, it reproduces the theoretically well-established surface modes of spinodal decomposition of an unstable solid solution: the surface mode of coherent spinodal decomposition and the surface-directed spinodal decomposition mode. We systematically investigate the influences of major factors on the kinetic surface phase behaviors during the diffusional process. Finally, our simulation study provides insights for tailoring the internal phase microstructure of a particle by controlling the surface phase morphology.

25 ENERGY STORAGE↗
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 OSTI2021

Design Space Exploration of Emerging Memory Technologies for Machine Learning Applications

Memory design space exploration methods study memory systems’ performances and limitations before implementation. The computer memory design space has grown exponentially because of the enormous growth of memory types, memory controllers, and application software. Computer simulators are commonly used for memory design space exploration. However, complex memory simulations take an enormous amount of time. Hence, in this paper, we proposed a machine learning-based design space exploration method for dynamic random-access memory and non-volatile memory systems. We applied our method to the CosmoGAN and LeNet applications to predict the following six memory response parameters: (i) bandwidth, (ii) power, (iii) average latency, (iv) average total latency, (v) memory reads, and (vi) memory writes. Our experimental results show that machine learning models can predict memory response parameter values faster than simulations. We used support vector machine, random forest, and gradient boosting machine learning models. We observed that the support vector machine provides better performance for bandwidth, average latency, and average total latency. The random forest model works better for memory reads and writes. The gradient boosting model provides superior prediction performance for power. We provide a detailed discussion on learning curve characteristics, error analysis, and memory type recommendation.

Hasan, S M Shamimul↗
DOE OSTI2021

Physics-Based Optical Neuromorphic Classification

Typical approaches to classify scenes from light convert the light field to electrons to perform the computation in the digital electronic domain. This conversion and downstream computational analysis require significant power and time. Diffractive neural networks have recently emerged as unique systems to classify optical fields at lower energy and high speeds. Previous work has shown that a single layer of diffractive metamaterial can achieve high performance on classification tasks. In analogy with electronic neural networks, it is anticipated that multilayer diffractive systems would provide better performance, but the fundamental reasons for the potential improvement have not been established. In this work, we present extensive computational simulations of two - layer diffractive neural networks and show that they can achieve high performance with fewer diffractive features than single layer systems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Non-Empirical and Self-Interaction Corrections for DFTB: Towards Accurate Quantum Simulations for Large Mesoscale Systems (Final Report)

This project was comprised of two complementary (but parallel) thrusts: (1) implementing massively-parallelized computing hardware (with new computational hardware that may replace GPUs) in the density functional tight binding (DFTB) approach and (2) developing new capabilities in DFTB to calculate the electronic structure and dynamics of large chemical systems. While classical molecular dynamics can handle hundreds of thousands of atoms, it cannot provide a first-principles based description of chemical systems at the quantum level. At the other extreme, conventional Kohn-Sham DFT methods can probe the true quantum mechanical nature of chemical systems; however, these methods cannot tackle the large sizes and length scales relevant to dynamics simulations of realistic systems. The DFTB formalism utilized in this project provides a viable approach for probing these large systems at a quantum mechanical level of detail. However, to utilize the DFTB approach for accurate calculations of electronic properties, it is crucial to incorporate quantum-based corrections in DFTB since exchange-correlation effects can still remain very strong in these large systems. At the same time, enhancing the computational efficiency of DFTB is also essential since optimal computational performance is required for addressing the large size scales associated with realistic chemical systems. As such, the new non-empirical corrections and computing hardware enhancements implemented in this project will enable accurate and computationally efficient approaches to directly probe electronic properties in these large, complex systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A deep learning-based battery sizing optimization tool for hybridizing generation plants

Hybrid generation and energy storage systems offer the ability to increase flexibility of the combined asset. This flexibility can be used to increase provision of services already provided by the generation asset, such as timing sale of electricity to the energy market during high price periods, and also enable provision of additional services, such as ancillary services or contribute to resource adequacy. From a generation asset owner perspective, the decision to hybridize includes selecting an energy storage system that, among other factors, maximizes financial performance of the energy storage investment. Yet, existing tools to optimize energy storage sizing are either too rudimentary (i.e., based on “rules of thumb”) or too complex to implement (i.e., require specialized engineering and software knowledge and a high-performance computer to run). This work presents a novel deep learning-based battery sizing optimization tool that is designed to help generation asset owners easily assess preliminary sizing considerations for potential battery investments to hybridize their generation facility. The tool uses deep learning to predict revenue over a broad search space of potential battery sizes, estimates capital and operating costs (including accounting for battery degradation), and computes financial performance of each potential battery system investment, recommending a system with maximum financial performance. The tool is tested and validated for hydropower assets. Finally, this tool will help a greater cross-section of industry consider investments in battery systems, increasing their revenue and helping them compete in rapidly evolving electrify markets.

13 HYDRO ENERGY↗
DOE OSTI2015

Radiograph and passive data analysis using mixed variable optimization

Disclosed herein are representative embodiments of methods, apparatus, and systems for performing radiography analysis. For example, certain embodiments perform radiographic analysis using mixed variable computation techniques. One exemplary system comprises a radiation source, a two-dimensional detector for detecting radiation transmitted through a object between the radiation source and detector, and a computer. In this embodiment, the computer is configured to input the radiographic image data from the two-dimensional detector and to determine one or more materials that form the object by using an iterative analysis technique that selects the one or more materials from hierarchically arranged solution spaces of discrete material possibilities and selects the layer interfaces from the optimization of the continuous interface data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2013

Layout-aware I/O Scheduling for terabits data movement

Many science facilities, such as the Department of Energy's Leadership Computing Facilities and experimental facilities including the Spallation Neutron Source, Stanford Linear Accelerator Center, and Advanced Photon Source, produce massive amounts of experimental and simulation data. These data are often shared among the facilities and with collaborating institutions. Moving large datasets over the wide-area network (WAN) is a major problem inhibiting collaboration. Next-generation, terabit-networks will help alleviate the problem, however, the parallel storage systems on the endsystem hosts at these institutions can become a bottleneck for terabit data movement. The parallel storage system (PFS) is shared by simulation systems, experimental systems, analysis and visualization clusters, in addition to wide-area data movers. These competing uses often induce temporary, but significant, I/O load imbalances on the storage system, which impact the performance of all the users. The problem is a serious concern because some resources are more expensive (e.g. super computers) or have time-critical deadlines (e.g. experimental data from a light source), but parallel file systems handle all requests fairly even if some storage servers are under heavy load. This paper investigates the problem of competing workloads accessing the parallel file system and how the performance of wide-area data movement can be improved in these environments. First, we study the I/O load imbalance problems using actual I/O performance data collected from the Spider storage system at the Oak Ridge Leadership Computing Facility. Second, we present I/O optimization solutions with layout-awareness on end-system hosts for bulk data movement. With our evaluation, we show that our I/O optimization techniques can avoid the I/O congested disk groups, improving storage I/O times on parallel storage systems for terabit data movement.

Kim, Youngjae↗
DOE OSTI2025

Automation for Grid Interconnected Laboratory Emulation

As computational capabilities improve, digital twins are becoming vital for evaluating equipment realistically in laboratories. This paper outlines a digital twin architecture for the power grid, employing electromagnetic transient (EMT) simulation alongside real-time simulation of power hardware and hierarchical control systems. EMT simulation occurs on a high-performance computing server for scalability. Additionally, the paper describes a workflow and real-time data streaming software facilitating connectivity among EMT simulation, hierarchical control systems, and power hardware. This software enables automated equipment connectivity in the laboratory for realistic evaluations, aiding in identifying necessary upgrades for both equipment control systems and the power grid.

Marthi, Phani Ratna Vanamali [ORNL] (ORCID:0000000↗
DOE OSTI2016

Electric Grid Expansion Planning with High Levels of Variable Generation

Renewables are taking a large proportion of generation capacity in U.S. power grids. As their randomness has increasing influence on power system operation, it is necessary to consider their impact on system expansion planning. To this end, this project studies the generation and transmission expansion co-optimization problem of the US Eastern Interconnection (EI) power grid with a high wind power penetration rate. In this project, the generation and transmission expansion problem for the EI system is modeled as a mixed-integer programming (MIP) problem. This study analyzed a time series creation method to capture the diversity of load and wind power across balancing regions in the EI system. The obtained time series can be easily introduced into the MIP co-optimization problem and then solved robustly through available MIP solvers. Simulation results show that the proposed time series generation method and the expansion co-optimization model and can improve the expansion result significantly after considering the diversity of wind and load across EI regions. The improved expansion plan that combines generation and transmission will aid system planners and policy makers to maximize the social welfare. This study shows that modelling load and wind variations and diversities across balancing regions will produce significantly different expansion result compared with former studies. For example, if wind is modeled in more details (by increasing the number of wind output levels) so that more wind blocks are considered in expansion planning, transmission expansion will be larger and the expansion timing will be earlier. Regarding generation expansion, more wind scenarios will slightly reduce wind generation expansion in the EI system and increase the expansion of other generation such as gas. Also, adopting detailed wind scenarios will reveal that it may be uneconomic to expand transmission networks for transmitting a large amount of wind power through a long distance in the EI system. Incorporating more details of renewables in expansion planning will inevitably increase the computational burden. Therefore, high performance computing (HPC) techniques are urgently needed for power system operation and planning optimization. As a scoping study task, this project tested some preliminary parallel computation techniques such as breaking down the simulation task into several sub-tasks based on chronology splitting or sample splitting, and then assigning these sub-tasks to different cores. Testing results show significant time reduction when a simulation task is split into several sub-tasks for parallel execution.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

A Big Data Analytics Framework for HPC Log Data: Three Case Studies Using the Titan Supercomputer Log

Reliability, availability and serviceability (RAS) logs of high performance computing (HPC) resources, when closely investigated in spatial and temporal dimensions, can provide invaluable information regarding system status, performance, and resource utilization. These data are often generated from multiple logging systems and sensors that cover many components of the system. The analysis of these data for finding persistent temporal and spatial insights faces two main difficulties: the volume of RAS logs makes manual inspection difficult and the unstructured nature and unique properties of log data produced by each subsystem adds another dimension of difficulty in identifying implicit correlation among recorded events. To address these issues, we recently developed a multi-user Big Data analytics framework for HPC log data at Oak Ridge National Laboratory (ORNL). This paper introduces three in-progress data analytics projects that leverage this framework to assess system status, mine event patterns, and study correlations between user applications and system events. We describe the motivation of each project and detail their workflows using three years of log data collected from ORNL's Titan supercomputer.

Park, Byung↗
DOE OSTI2015

Whole program Adaptive Error Detection and Mitigation

Our project investigated methods to detect soft errors in computing systems. Such detection schemes must have low overheads and also generate no false positives. Our solutions focused on control-space error detection based on changing addressing schemes so that faults tend to cascade – rather than being masked. Our solutions also addressed data-space error detection by synthesizing error detectors based on rigorous floating-point error analysis.

42 ENGINEERING↗
DOE OSTI2015

Validation of a novel robot-assisted 3DUS system for real-time planning and guidance of breast interstitial HDR brachytherapy

Purpose: In current clinical practice, there is no integrated 3D ultrasound (3DUS) guidance system clinically available for breast brachytherapy. In this study, the authors present a novel robot-assisted 3DUS system for real-time planning and guidance of breast interstitial high dose rate (HDR) brachytherapy treatment. Methods: For this work, a new computer controlled robotic 3DUS system was built to perform a hybrid motion scan, which is a combination of a 6 cm linear translation with a 30° rotation at both ends. The new 3DUS scanner was designed to fit on a modified Kuske assembly, keeping the current template grid configuration but modifying the frame to allow the mounting of the 3DUS system at several positions. A finer grid was also tested. A user interface was developed to perform image reconstruction, semiautomatic segmentation of the surgical bed as well as catheter reconstruction and tracking. A 3D string phantom was used to validate the geometric accuracy of the reconstruction. The volumetric accuracy of the system was validated with phantoms using magnetic resonance imaging (MRI) and computed tomography (CT) images. In order to accurately determine whether 3DUS can effectively replace CT for treatment planning, the authors have compared the 3DUS catheter reconstruction to the one obtained from CT images. In addition, in agarose-based phantoms, an end-to-end procedure was performed by executing six independent complete procedures with both 14 and 16 catheters, and for both standard and finer Kuske grids. Finally, in phantoms, five end-to-end procedures were performed with the final CT planning for the validation of 3DUS preplanning. Results: The 3DUS acquisition time is approximately 10 s. A paired Student t-test showed that there was no statistical significant difference between known and measured values of string separations in each direction. Both MRI and CT volume measurements were not statistically different from 3DUS volume (Student t-test: p > 0.05) and they were significantly correlated to 3DUS measurement (Pearson test: MRI p < 0.05 and CT p < 0.001). The mean angular separation distance between catheter trajectories segmented from 3DUS and CT images was 0.42° ± 0.24°, while the maximum and mean trajectory separations were 0.51 ± 0.19 and 0.37 ± 0.17 mm, respectively. Overall, the new finer grid has performed significantly better in terms of dosimetric indices. The planning target volume dosimetric indices were not found statistically different between 3DUS and CT planning (Student t-test, p > 0.05). Both the skin and the pectoral muscle dosimetric indices were within ABS guidelines. Conclusions: A novel robot-assisted 3DUS system was designed and validated. To their knowledge, this is the first system capable of performing real-time guidance and planning of breast multicatheter HDR brachytherapy treatments. Future investigation will test the feasibility of using the system in the clinic and for permanent breast brachytherapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Performance trade-offs in reconfigurable networks for HPC

Designing efficient interconnects to support high-bandwidth and low-latency communication is critical toward realizing high performance computing (HPC) and data center (DC) systems in the exascale era. At extreme computing scales, providing the requisite bandwidth through overprovisioning becomes impractical. These challenges have motivated studies exploring reconfigurable network architectures that can adapt to traffic patterns at runtime using optical circuit switching. Despite the plethora of proposed architectures, surprisingly little is known about the relative performances and trade-offs among different reconfigurable network designs. We aim to bridge this gap by tackling two key issues in reconfigurable network design. First, we study how cost, power consumption, network performance, and scalability vary based on optical circuit switch (OCS) placement in the physical topology. Specifically, we consider two classes of reconfigurable architectures: one that places OCSs between top-of-rack (ToR) switches—ToR-reconfigurable networks (TRNs)—and one that places OCSs between pods of racks—pod-reconfigurable networks (PRNs). Second, we tackle the effects of reconfiguration frequency on network performance. Our results, based on network simulations driven by real HPC and DC workloads, show that while TRNs are optimized for low fan-out communication patterns, they are less suited for carrying high fan-out workloads. PRNs exhibit better overall trade-off, capable of performing comparably to a fully non-blocking fat tree for low fan-out workloads, and significantly outperform TRNs for high fan-out communication patterns.

Teh, Min Yee↗
DOE OSTI2019

Preliminary Designs for Criticality Safety Benchmarks—Iron/Steel/Chromium Series

The nuclear criticality safety community uses critical benchmarks extensively when analyzing nuclear systems. Having benchmarks sensitive to important materials of interest helps to ensure safety and constrain results. Critical benchmarks are also invaluable to the nuclear data community, which uses these benchmarks as a check and measure of quality of nuclear data libraries. Additionally, integral benchmarks are often used to validate the performance of computational tools for simulating nuclear systems. Many integral benchmarks have been documented in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) handbook, the International Reactor Physics Experiment Evaluation Project (IRPhEP) handbook, and the Shielding Integral Benchmark Archive Database (SINBAD). The National Criticality Experiments Research Center (NCERC), operated by Los Alamos National Laboratory, is the only general purpose critical experiments facility in the United States of America. NCERC regularly designs and executes critical experiments and other measurements useful to a wide variety of fields including nuclear criticality safety, commercial nuclear energy, nonproliferation, and nuclear data. NCERC has recently developed tools to visualize gaps and deficiencies in existing criticality benchmarks – this work is still ongoing. Through this work and other reviews, some high priority deficiencies have been identified. This paper aims to highlight three areas where additional critical benchmarks would be useful to the nuclear criticality safety community and various other groups. Additionally, NCERC is seeking feedback in regard to these proposed measurements to make sure they are as useful to as many communities as possible.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Transforming Energy Through Computational Excellence: NREL's Computational Science Center

Computational methods underpin advancing the science and engineering of energy efficiency, sustainable transportation, renewable power technologies, and developing a knowledge base to optimize energy systems. NREL's Computational Science Center (CSC) proudly focuses on providing the service of computing, advancing the science of computing, and enabling NREL's clean energy mission.

applied mathematics↗