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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 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Magnetic-Field-Driven Electron Dynamics in Graphene

Graphene exhibits unique optoelectronic properties originating from the band structure at the Dirac points. It is an ideal model structure to study the electronic and optical properties under the influence of the applied magnetic field. In graphene, electric field, laser pulse, and voltage can create electron dynamics which is influenced by momentum dispersion. However, computational modeling of momentum-influenced electron dynamics under the applied magnetic field remains challenging. In this work, we perform computational modeling of the photoexcited electron dynamics achieved in graphene under an applied magnetic field. Our results show that magnetic field leads to local deviation from momentum conservation for charge carriers. With the increasing magnetic field, the delocalization of electron probability distribution increases and forms a cyclotron-like trajectory. Our work facilitates understanding of momentum resolved magnetic field effect on non-equilibrium properties of graphene, which is critical for optoelectronic and photovoltaic applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Grid-based minimization at scale: Feldman-Cousins corrections for light sterile neutrino search

High Energy Physics (HEP) experiments generally employ sophisticated statistical methods to present results in searches of new physics. In the problem of searching for sterile neutrinos, likelihood ratio tests are applied to short-baseline neutrino oscillation experiments to construct confidence intervals for the parameters of interest. The test statistics of the form Δχ2 is often used to form the confidence intervals, however, this approach can lead to statistical inaccuracies due to the small signal rate in the region-of-interest. In this paper, we present a computational model for the computationally expensive Feldman-Cousins corrections to construct a statistically accurate confidence interval for neutrino oscillation analysis. The program performs a grid-based minimization over oscillation parameters and is written in C++. Our algorithms make use of vectorization through Eigen3, yielding a single-core speed-up of 350 compared to the original implementation, and achieve MPI data parallelism by employing DIY. We demonstrate the strong scaling of the application at High-Performance Computing (HPC) sites. We utilize HDF5 along with HighFive to write the results of the calculation to file.

Wospakrik, Marianette↗
DOE OSTI2021

Co-design of Advanced Architectures for Graph Analytics using Machine Learning

A graph is an excellent way of representing relationships among entities. We can use graph analytics to synthesize and analyze such relational data, and extract relevant features that are useful for various tasks such as machine learning. Considering the crucial role of graph analytics in various domains, it is important and timely to investigate the right hardware configurations that can achieve optimal performance for graph workloads on future high-performance computing systems. Design space exploration studies facilitate the selection of appropriate configurations (e.g. memory) to achieve a desired system performance. Recently, the approach of accelerating graph analytics using persistent non-volatile memory has gained a lot of attention. Traditional system simulators such as Gem5 and NVMain can be used to explore the design space of these advanced memory architectures for graph workloads. However, these simulators are slow in execution thus limiting the efficiency of design space exploration studies. To overcome this challenge, we proposed a machine learning based approach to co-design advanced memory architectures for graph workloads. We tested our approach with DRAM, non-volatile memory, and hybrid memory (DRAM+NVM) using a breadth first search benchmark algorithm. Our results showed the applicability of the proposed machine learning based approach to the co-design of the advanced memory architectures. In this paper, we provide recommendations on selecting advanced memory architectures to achieve desired performance for graph workloads. We also discuss the performances of different machine learning models that were considered in this study.

Kurte, Kuldeep↗
DOE OSTI2015

Mitigation of Syngas Cooler Plugging and Fouling

This Final Report summarizes research performed to develop a technology to mitigate the plugging and fouling that occurs in the syngas cooler used in many Integrated Gasification Combined Cycle (IGCC) plants. The syngas cooler is a firetube heat exchanger located downstream of the gasifier. It offers high thermal efficiency, but its’ reliability has generally been lower than other process equipment in the gasification island. The buildup of ash deposits that form on the fireside surfaces in the syngas cooler (i.e., fouling) lead to reduced equipment life and increased maintenance costs. Our approach to address this problem is that fouling of the syngas cooler cannot be eliminated, but it can be better managed. The research program was funded by DOE using two budget periods: Budget Period 1 (BP1) and Budget Period 2 (BP2). The project used a combination of laboratory scale experiments, analysis of syngas cooler deposits, modeling and guidance from industry to develop a better understanding of fouling mechanisms and to develop and evaluate strategies to mitigate syngas cooler fouling and thereby improve syngas cooler performance. The work effort in BP 1 and BP 2 focused on developing a better understanding of the mechanisms that lead to syngas cooler plugging and fouling and investigating promising concepts to mitigate syngas cooler plugging and fouling. The work effort focused on the following: • analysis of syngas cooler deposits and fuels provided by an IGCC plant collaborating with this project; • performing Jet cleaning tests in the University of Utah Laminar Entrained Flow Reactor to determine the bond strength between an ash deposit to a metal plate, as well as implementing planned equipment modifications to the University of Utah Laminar Entrained Flow Reactor and the one ton per day, pressurized Pilot Scale Gasifier; • performing Computational Fluid Dynamic modeling of industrially relevant syngas cooler configurations to develop a better understanding of deposit formation mechanisms; • performing Techno-Economic-Analysis for a representative IGCC plant to investigate the impact on plant economics, in particular the impacts on the Cost of Electricity (COE), due to plant shutdowns caused by syngas cooler plugging and fouling and potential benefits to plant economics of developing strategies to mitigate syngas cooler fouling; and • performing modeling and pilot scale tests to investigate the potential benefits of using a sorbent (fuel additive) to capture the vaporized metals that result in syngas cooler fouling. All project milestones for BP 1 and BP 2 were achieved. DOE was provided a briefing on our accomplishments in BP1 and BP2 and our proposed plans for Budget Period 3 (BP 3). Based on our research the mitigation technology selected to investigate in BP 3 was the use of a sorbent that can be injected into the gasifier with the fuel slurry to capture vaporized metals that lead to the deposit formation in the syngas cooler. The work effort proposed for BP 3 would have focused on addressing concerns raised by gasification industry personnel for the impacts on gasifier performance of sorbent injection, so that at the end of BP 3 the use of sorbent injection would be at “pre-commercial” stage and ready for use in a Field Demonstration that could be funded by industry or DOE. A Budget Continuation Application (BCA) was submitted to obtain funding for BP3 DOE but DOE chose to not fund the proposed BP3 effort.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Improving Gas Reactor Design with Complex Non-Standard Reaction Mechanisms in a Reactive Flow Model

Fluorinated gases are critical to a number of high-technology industries including semiconductor manufacturing and related processes such as flat panel display, LED, and photovoltaic manufacturing. These fluorinated gases have very high global warming potentials (GWPs) and, unfortunately are chemically very stable, in some cases lasting for thousands of years in the atmosphere. For environmental reasons, it is imperative that the release of these gases be minimized, but existing methods to destroy them requires a significant amount of energy. The goal of this project was to develop a more energy efficient method of destroying these high GWP gases using computational fluid dynamics, but the computational requirements are significant. In this project, we bring together resources available at the national labs, including high-performance computing hardware and modern computational methods, in order to explore an industrially important problem with key environmental and economic impact. In this project, we examined the state-of-the-art models for the physical processes involved, and made subtle changes to arrive at alternative formulations considerably better suited for computation and study. We examined several representative scenarios and developed a set of analyses techniques and tools that could be used to explore the systems in much greater detail. This exploratory project has demonstrated to ALZETA that HPC can indeed provide a unique view into their complex engineering problem - one that is simply inaccessible by any other means. The tools allow us to explore the details of the chemical process, and allow one to construct "what-if" scenarios to address shortcomings of the physical device in its current operating modes. However, as with many sources of completely new information, many new questions are illuminated with this capability. It will take considerable time, effort and experience before we learn to probe the solutions, pose operating scenarios and confirm modifications to the device that lead to real engineering advances.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Evaluation of fluxon synapse device based on superconducting loops for energy efficient neuromorphic computing

With Moore’s law nearing its end due to the physical scaling limitations of CMOS technology, alternative computing approaches have gained considerable attention as ways to improve computing performance. Here, we evaluate performance prospects of a new approach based on disordered superconducting loops with Josephson-junctions for energy efficient neuromorphic computing. Synaptic weights can be stored as internal trapped fluxon states of three superconducting loops connected with multiple Josephson-junctions (JJ) and modulated by input signals applied in the form of discrete fluxons (quantized flux) in a controlled manner. The stable trapped fluxon state directs the incoming flux through different pathways with the flow statistics representing different synaptic weights. We explore implementation of matrix–vector-multiplication (MVM) operations using arrays of these fluxon synapse devices. We investigate the energy efficiency of online-learning of MNIST dataset. Our results suggest that the fluxon synapse array can provide ~100× reduction in energy consumption compared to other state-of-the-art synaptic devices. This work presents a proof-of-concept that will pave the way for development of high-speed and highly energy efficient neuromorphic computing systems based on superconducting materials.

42 ENGINEERING↗
NASA NTRS1988

Implications of the GSFC Q3 model for trapped particle motion

The Uranus magnetic field model of Connerney et al. (1987), designated GSFC Q3, is used to compute field geometric invariant parameters that determine the adiabatic motion of energetic charged particles trapped in the Uranian magnetosphere, performing computations only for points located along the Voyager 2 flyby trajectory. The L-shell values computed along the Voyager-2 trajectory were compared with L shell values corresponding to the orbital positions for the Uranian satellites Ariel, Umbriel, Miranda, and Titania for a time period centered on the time of the Voyager 2 closest approach to the planet. Bimodal distributions of L minima asociated with the orbital motion of the moons are obtained, thus complicating the model predictions and correlations with charged particle data. The location of charged particle absorption signatures associated with the sweeping effects of the Uranian satellites is reasonably predicted, but significant discrepancies remain which cannot be explained by Q3 model uncertainties.

Acuna, M. H.↗
NASA NTRS2009

Understanding the Scalability of Bayesian Network Inference using Clique Tree Growth Curves

Bayesian networks (BNs) are used to represent and efficiently compute with multi-variate probability distributions in a wide range of disciplines. One of the main approaches to perform computation in BNs is clique tree clustering and propagation. In this approach, BN computation consists of propagation in a clique tree compiled from a Bayesian network. There is a lack of understanding of how clique tree computation time, and BN computation time in more general, depends on variations in BN size and structure. On the one hand, complexity results tell us that many interesting BN queries are NP-hard or worse to answer, and it is not hard to find application BNs where the clique tree approach in practice cannot be used. On the other hand, it is well-known that tree-structured BNs can be used to answer probabilistic queries in polynomial time. In this article, we develop an approach to characterizing clique tree growth as a function of parameters that can be computed in polynomial time from BNs, specifically: (i) the ratio of the number of a BN's non-root nodes to the number of root nodes, or (ii) the expected number of moral edges in their moral graphs. Our approach is based on combining analytical and experimental results. Analytically, we partition the set of cliques in a clique tree into different sets, and introduce a growth curve for each set. For the special case of bipartite BNs, we consequently have two growth curves, a mixed clique growth curve and a root clique growth curve. In experiments, we systematically increase the degree of the root nodes in bipartite Bayesian networks, and find that root clique growth is well-approximated by Gompertz growth curves. It is believed that this research improves the understanding of the scaling behavior of clique tree clustering, provides a foundation for benchmarking and developing improved BN inference and machine learning algorithms, and presents an aid for analytical trade-off studies of clique tree clustering using growth curves.

Mengshoel, Ole Jakob↗
NASA NTRSDate not supplied

Deep Learning System for Efficient Processing of Geostationary Satellite Imagery

Improved capabilities of Earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. Further, a large majority of archived datasets generated by scientific processing is never used. This motivates the development of an efficient machine learning system for end-to-end processing of multi-level satellite datasets, from level 1 top of atmosphere observations to user friendly environmental variables of interest. Using current generation geostationary satellites GOES-16/17 (NOAA/NASA), and Himawari-8/9 (JAXA), we present an interchangeable set of machine models to perform spectral adjustment among sensors, physical model emulation, LEO-GEO emulation, and optical flow in a high performance computing environment. We use these tools on the NASA Earth eXchange (NEX) to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate surface reflectance, surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Thomas Vandal↗
DOE OSTI2021

Computational Math Problems for a Clean Energy Future

Cutting edge computational mathematics are ubiquitous in renewable energy research. Problems in resilient and reliable electric grid operations, infrastructure planning, wind farm yaw control, and more demand sophisticated and scalable computational tools that enable the transition of renewable energy technologies from proof of concept to deployment into our energy system. The mission of the Computational Science Center at NREL is to lead the lab's efforts to solve energy challenges using high-performance computing (HPC), computational science, applied mathematics, scientific data management, visualization, and informatics. In this poster, we provide a short overview of three areas of computational mathematics research at NREL: wind power scenario generation for stochastic grid operations and infrastructure planning, improved rational function approximations for electromagnetic transients codes, and wind farm yaw control using a combination of the Alternating Direction Method of Multipliers (ADMM) and reinforcement learning (RL). Increasing penetrations of renewable energy into power grids motivate the investigation of new approaches to characterizing uncertainty for five-minute economic dispatch problems. Similarly, as the penetration of distributed energy resources on power grids increases, it becomes important to revisit our methods of modelling transient phenomena, i.e. electromagnetic transients programs. Finally, the combination of ADMM and RL for wind farm yaw control presented here can potentially increase the efficiency of the deployed distributed controllers by orders of magnitude.

ADMM↗
DOE OSTI2019

Evaluating Proxy Apps with FPGAs

Field-programmable gate arrays (FPGAs) are becoming more promising for high performance computing (HPC). Unlike fixed hardware specialization, reconfigurability allows FPGAs to be customized for a wide variety of HPC workloads. Our ultimate goal is to understand the viability of FPGAs as accelerators for some of the key HPC codes at LLNL and identify application characteristics that can benefit from the reconfigurability of FPGAs. More specifically, this LDRD project conducted a detailed performance study of FPGAs using sparse matrix computations and identified key optimization techniques to effectively use FPGA hardware resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Evaluation of pre-training large language models on leadership-class supercomputers

Large language models (LLMs) have arisen rapidly to the center stage of artificial intelligence as the foundation models applicable to many downstream learning tasks. However, how to effectively build, train, and serve such models for many high-stake and first-principle-based scientific use cases are both of great interests and of great challenges. Moreover, pre-training LLMs with billions or even trillions of parameters can be prohibitively expensive not just for academic institutions, but also for well-funded industrial and government labs. Furthermore, the energy cost and the environmental impact of developing LLMs must be kept in mind. Here, in this work, we conduct a first-of-its-kind performance analysis to understand the time and energy cost of pre-training LLMs on the Department of Energy (DOE)’s leadership-class supercomputers. Employing state-of-the-art distributed training techniques, we evaluate the computational performance of various parallelization approaches at scale for a range of model sizes, and establish a projection model for the cost of full training. Our findings provide baseline results, best practices, and heuristics for pre-training such large models that should be valuable to HPC community at large. We also offer insights and optimization strategies for using the first exascale computing system, Frontier, to train models of the size of GPT-3 and beyond.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2012

An Application-Based Performance Evaluation of NASAs Nebula Cloud Computing Platform

The high performance computing (HPC) community has shown tremendous interest in exploring cloud computing as it promises high potential. In this paper, we examine the feasibility, performance, and scalability of production quality scientific and engineering applications of interest to NASA on NASA's cloud computing platform, called Nebula, hosted at Ames Research Center. This work represents the comprehensive evaluation of Nebula using NUTTCP, HPCC, NPB, I/O, and MPI function benchmarks as well as four applications representative of the NASA HPC workload. Specifically, we compare Nebula performance on some of these benchmarks and applications to that of NASA s Pleiades supercomputer, a traditional HPC system. We also investigate the impact of virtIO and jumbo frames on interconnect performance. Overall results indicate that on Nebula (i) virtIO and jumbo frames improve network bandwidth by a factor of 5x, (ii) there is a significant virtualization layer overhead of about 10% to 25%, (iii) write performance is lower by a factor of 25x, (iv) latency for short MPI messages is very high, and (v) overall performance is 15% to 48% lower than that on Pleiades for NASA HPC applications. We also comment on the usability of the cloud platform.

Saini, Subhash↗
DOE OSTI2015

Parallel Application Performance on Two Generations of Intel Xeon HPC Platforms

Two next-generation node configurations hosting the Haswell microarchitecture were tested with a suite of microbenchmarks and application examples, and compared with a current Ivy Bridge production node on NREL" tm s Peregrine high-performance computing cluster. A primary conclusion from this study is that the additional cores are of little value to individual task performance--limitations to application parallelism, or resource contention among concurrently running but independent tasks, limits effective utilization of these added cores. Hyperthreading generally impacts throughput negatively, but can improve performance in the absence of detailed attention to runtime workflow configuration. The observations offer some guidance to procurement of future HPC systems at NREL. First, raw core count must be balanced with available resources, particularly memory bandwidth. Balance-of-system will determine value more than processor capability alone. Second, hyperthreading continues to be largely irrelevant to the workloads that are commonly seen, and were tested here, at NREL. Finally, perhaps the most impactful enhancement to productivity might occur through enabling multiple concurrent jobs per node. Given the right type and size of workload, more may be achieved by doing many slow things at once, than fast things in order.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1988

Static tensile and tensile creep testing of five ceramic fibers at elevated temperatures

Static tensile and tensile creep testing of five ceramic fibers at elevated temperature was performed. J.P. Stevens, Co., Astroquartz 9288 glass fiber, Nippon Carbon, Ltd., (Dow Corning) Nicalon NLM-102 silicon carbide fiber, and 3M Company Nextel 312, 380, and 480 alumina/silica/boria fibers were supplied in unsized tows. Single fibers were separated from the tows and tested in static tension and tensile creep. Elevated test temperatures ranged from 400 to 1300 C and varied for each fiber. Room temperature static tension was also performed. Computer software was written to reduce all single fiber test data into engineering constants using ASTM Standard Test Method D3379-75 as a reference. A high temperature furnace was designed and built to perform the single fiber elevated temperature testing up to 1300 C. A computerized single fiber creep apparatus was designed and constructed to perform four fiber creep tests simultaneously at temperatures up to 1300 C. Computer software was written to acquire and reduce all creep data.

Zimmerman, Richard S.↗
NASA NTRS1989

Static tensile and tensile creep testing of five ceramic fibers at elevated temperatures

Static tensile and tensile creep testing of five ceramic fibers at elevated temperature was performed. J.P. Stevens, Co., Astroquartz 9288 glass fiber; Nippon Carbon, Ltd., (Dow Corning) nicalon NLM-102 silicon carbide fiber; and 3M Company Nextel 312, 380, and 480 alumina/silica/boria fibers were supplied in unsized tows. Single fibers were separated from the tows and tested in static tension and tensile creep. Elevated test temperatures ranged from 400 C to 1300 C and varied for each fiber. Room temperature static tension was also performed. Computer software was written to reduce all single fiber test data into engineering constants using ASTM Standard Test Method D3379-75 as a reference. A high temperature furnace was designed and built to perform the single fiber elevated temperature testing up to 1300 C. A computerized single fiber creep apparatus was designed and constructed to perform four fiber creep tests simultaneously at temperatures up to 1300 C. Computer software was written to acquire and reduce all creep data.

Zimmerman, Richard S.↗
DOE OSTI2024

Visual Analytics of Performance of Quantum Computing Systems and Circuit Optimization

Driven by potential exponential speedups in business, security, and scientific scenarios, interest in quantum computing is surging. This interest feeds the development of quantum computing hardware, but several challenges arise in optimizing application performance for hardware metrics (e.g., qubit coherence and gate fidelity). In this work, we describe a visual analytics approach for analyzing the performance properties of quantum devices and quantum circuit optimization. Our approach allows users to explore spatial and temporal patterns in quantum device performance data and it computes similarities and variances in key performance metrics. Detailed analysis of the error properties characterizing individual qubits is also supported. We also describe a method for visualizing the optimization of quantum circuits. The resulting visualization tool allows researchers to design more efficient quantum algorithms and applications by increasing the interpretability of quantum computations.

Chae, Junghoon↗
NASA NTRS1991

A parallel-vector Lanczos eigen-solver for structural vibration problems

The Lanczos algorithm for the solution of generalized eigen-problem has been receiving a lot of attention in recent years due to its computational efficiency. The focus of this paper is to develop a Lanczos algorithm which can exploit both the parallel and vector capabilities provided by modern high-performance computers. A partial restoring orthogonality scheme is also developed and incorporated into the basic Lanczos algorithm. The numerical performance in terms of accuracy and efficiency of the proposed parallel-vector Lanczos algorithm is demonstrated by solving for the frequencies and mode shapes of structural problems on multiprocessor supercomputers.

Qin, Jiangning↗