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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Textbook Multigrid Efficiency for Leading Edge Stagnation

A multigrid solver is defined as having textbook multigrid efficiency (TME) if the solutions to the governing system of equations are attained in a computational work which is a small (less than 10) multiple of the operation count in evaluating the discrete residuals. TME in solving the incompressible inviscid fluid equations is demonstrated for leading-edge stagnation flows. The contributions of this paper include (1) a special formulation of the boundary conditions near stagnation allowing convergence of the Newton iterations on coarse grids, (2) the boundary relaxation technique to facilitate relaxation and residual restriction near the boundaries, (3) a modified relaxation scheme to prevent initial error amplification, and (4) new general analysis techniques for multigrid solvers. Convergence of algebraic errors below the level of discretization errors is attained by a full multigrid (FMG) solver with one full approximation scheme (FAS) cycle per grid. Asymptotic convergence rates of the FAS cycles for the full system of flow equations are very fast, approaching those for scalar elliptic equations.

Diskin, Boris↗
DOE OSTI2021

Neuromorphic Computing for Autonomous Racing

Neuromorphic computing has many opportunities in future autonomous systems, especially those that will operate at the edge. However, there are relatively few demonstrations of neuromorphic implementations on real-world applications, partly because of the lack of availability of neuromorphic hardware and software, but also because of the lack of availability of an accessible demonstration platform. In this work, we propose utilizing the F1Tenth platform as an evaluation task for neuromorphic computing. F1Tenth is a competition wherein one tenth scale cars compete in an autonomous racing task; there are significant open source resources in both software and hardware for realizing this task. We present a workflow with neuromorphic hardware, software, and training that can be used to develop a spiking neural network for neuromorphic hardware deployment to perform autonomous racing. We present initial results on utilizing this approach for this small-scale, real-world autonomous vehicle task.

Patton, Robert↗
DOE OSTI2025

Effect of convective transport in edge and scrape-off-layer plasmas of ADITYA-U tokamak

The 2-D edge plasma fluid transport code, UEDGE has been used to simulate the edge and scrape-off layer regions of circular limiter plasmas of ADITYA-U. The computational mesh defining the limiter geometry of ADITYA-U has been introduced in the UEDGE code through an in-house developed routine. It has successfully been integrated with the UEDGE code and the measured radial profile of electron density (n e ) in the edge region is modelled using the simulations. The plasma discharge for the present study has plasma current I P ~ 130 kA, duration ~ 150 ms, chord-averaged electron density ~ 1.3 x 10 19 m -3 , and central electron temperature ~ 270 eV has been observed. The maximum toroidal magnetic field is ~ 1.2 T. It has been found that an inward convective velocity, v conv ~ 1.5 m/s is required in addition to the constant perpendicular diffusion coefficient, D ⟂ ~ 0.2 m 2 /s to adequately match the measured n e profile in typical discharges of ADITYA-U. The value of D ⟂ ~ 0.2 m 2 /s is found to lie between the estimated neoclassical diffusivity and Bohm diffusivity.

Physics - Plasma physics↗
DOE OSTI2023

Orchestration of materials science workflows for heterogeneous resources at large scale

In the era of big data, materials science workflows need to handle large-scale data distribution, storage, and computation. Any of these areas can become a performance bottleneck. We present a framework for analyzing internal material structures (e.g., cracks) to mitigate these bottlenecks. We demonstrate the effectiveness of our framework for a workflow performing synchrotron X-ray computed tomography reconstruction and segmentation of a silica-based structure. Our framework provides a cloud-based, cutting-edge solution to challenges such as growing intermediate and output data and heavy resource demands during image reconstruction and segmentation. Specifically, our framework efficiently manages data storage, scaling up compute resources on the cloud. The multi-layer software structure of our framework includes three layers. A top layer uses Jupyter notebooks and serves as the user interface. A middle layer uses Ansible for resource deployment and managing the execution environment. A low layer is dedicated to resource management and provides resource management and job scheduling on heterogeneous nodes (i.e., GPU and CPU). At the core of this layer, Kubernetes supports resource management, and Dask enables large-scale job scheduling for heterogeneous resources. The broader impact of our work is four-fold: through our framework, we hide the complexity of the cloud’s software stack to the user who otherwise is required to have expertise in cloud technologies; we manage job scheduling efficiently and in a scalable manner; we enable resource elasticity and workflow orchestration at a large scale; and we facilitate moving the study of nonporous structures, which has wide applications in engineering and scientific fields, to the cloud. While we demonstrate the capability of our framework for a specific materials science application, it can be adapted for other applications and domains because of its modular, multi-layer architecture.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

Assessment of computational prediction of tail buffeting

Assessments of the viability of computational methods and the computer resource requirements for the prediction of tail buffeting are made. Issues involved in the use of Euler and Navier-Stokes equations in modeling vortex-dominated and buffet flows are discussed and the requirement for sufficient grid density to allow accurate, converged calculations is stressed. Areas in need of basic fluid dynamics research are highlighted: vorticity convection, vortex breakdown, dynamic turbulence modeling for free shear layers, unsteady flow separation for moderately swept, rounded leading-edge wings, vortex flows about wings at high subsonic speeds. An estimate of the computer run time for a buffeting response calculation for a full span F-15 aircraft indicates that an improvement in computer and/or algorithm efficiency of three orders of magnitude is needed to enable routine use of such methods. Attention is also drawn to significant uncertainties in the estimates, in particular with regard to nonlinearities contained within the modeling and the question of the repeatability or randomness of buffeting response.

Edwards, John W.↗
DOE OSTI2021

MARS-Q modeling of kink-peeling instabilities in DIII-D QH-mode plasma

In quiescent H-mode (QH-mode) regime, edge harmonic oscillations (EHOs) are believed to provide necessary radial transport to prevent occurrence of large edge localized modes. A systematic modeling study is performed here on the low- n EHOs in a DIII-D QH-mode plasma (Chen et al 2016 Nucl. Fusion 56 076011), by utilizing the MARS-Q code (Liu et al 2013 Phys. Plasmas 20 042503). Both the n = 1 and n = 2 instabilities are found to be strongly localized near the plasma edge, exhibiting the edge-peeling characteristics. The DIII-D resistive wall is found to have minor effects on these instabilities. The plasma resistivity is found to strongly modify the mode growth rate. Assuming the Spitzer model for the plasma resistivity, the computed mode growth rate scales as S sup>-1/3 with S being the Lundquist number. Toroidal flow of the plasma slightly stabilizes these edge localized kink-peeling modes. Drift kinetic effects all have a destabilization effect on these modes. Non-perturbative magneto-hydrodynamic-kinetic hybrid computations find that the drift kinetic effects associated with thermal particle species push the peak location of the eigenmode radially inward but still in the pedestal region. The modeled plasma temperature and density fluctuations in the plasma edge region, as well as the poloidal magnetic field perturbations along both the low and high field sides of the plasma surface, are in good agreement with experimental measurements. Finally, the quasi-linear initial value simulations find a strong non-linear interplay between the kink-peeling instability and the toroidal flow near the plasma edge. The combined effect of the damping of the flow amplitude and change of the edge flow shear is found to be the stabilizing factor for the kink-peeling mode, leading to the mode saturation and thus EHOs.

Physics↗
DOE OSTI2024

Neuro-Spark: A Submicrosecond Spiking Neural Networks Architecture for In-Sensor Filtering

Neuro-Spark, which is a new neuromorphic architecture with a field-programmable gate array (FPGA) implementation for ultrafast spiking neural network (SNN) inference at the edge, facilitates smart-pixel in-sensor filtering for high-energy physics experiments at the Large Hadron Collider (LHC). Utilizing the evolutionary optimization for neuromorphic systems (EONS) training method, we generate compact SNN models with 91% signal efficiency, akin to convolutional neural networks but with half the parameters. However, deploying near the detector poses a challenge because the SNN must handle a sustained input data rate exceeding 1013 GB/s. To overcome this, we propose a novel hardware architecture that uses high-level synthesis to construct a tuned architecture for the EONS-trained SNN. In addition to the analysis and validation with an AMD Xilinx Artix-A7 FPGA, our solution consumes only ç24% of FPGA LUT and flipflops. We also introduce an innovative quantization method that reduces FPGA resource utilization by ç15% without compromising accuracy. Our FPGA implementation achieves computing latency of ç10 ns for smart-pixel application inference on an edge FPGA.

Miniskar, Narasinga Rao↗
NASA NTRS1988

Computer systems and software engineering

The High Technologies Laboratory (HTL) was established in the fall of 1982 at the University of Houston Clear Lake. Research conducted at the High Tech Lab is focused upon computer systems and software engineering. There is a strong emphasis on the interrelationship of these areas of technology and the United States' space program. In Jan. of 1987, NASA Headquarters announced the formation of its first research center dedicated to software engineering. Operated by the High Tech Lab, the Software Engineering Research Center (SERC) was formed at the University of Houston Clear Lake. The High Tech Lab/Software Engineering Research Center promotes cooperative research among government, industry, and academia to advance the edge-of-knowledge and the state-of-the-practice in key topics of computer systems and software engineering which are critical to NASA. The center also recommends appropriate actions, guidelines, standards, and policies to NASA in matters pertinent to the center's research. Results of the research conducted at the High Tech Lab/Software Engineering Research Center have given direction to many decisions made by NASA concerning the Space Station Program.

Mckay, Charles W.↗
DOE OSTI2023

Graph Sparsification by Approximate matrix Multiplication

Graphs arising in statistical problems, signal processing, large networks, combinatorial optimization, and data analysis are often dense, which causes both computational and storage bottlenecks. One way of sparsifying a weighted graph, while sharing the same vertices as the original graph but reducing the number of edges, is through spectral sparsification. We study this problem through the perspective of RandNLA. Specifically, we utilize randomized matrix multiplication to give a clean and simple analysis of how sampling according to edge weights gives a spectral approximation to graph Laplacians, without requiring spectral information. Through the CR–MM algorithm, we attain a simple and computationally efficient sparsifier whose resulting Laplacian estimate is unbiased and of minimum variance. Here, we define a new notion of additive spectral sparsifiers, which has not been considered in the literature.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1995

Euler technology assessment for preliminary aircraft design employing OVERFLOW code with multiblock structured-grid method

The viability of applying a state-of-the-art Euler code to calculate the aerodynamic forces and moments through maximum lift coefficient for a generic sharp-edge configuration is assessed. The OVERFLOW code, a method employing overset (Chimera) grids, was used to conduct mesh refinement studies, a wind-tunnel wall sensitivity study, and a 22-run computational matrix of flow conditions, including sideslip runs and geometry variations. The subject configuration was a generic wing-body-tail geometry with chined forebody, swept wing leading-edge, and deflected part-span leading-edge flap. The analysis showed that the Euler method is adequate for capturing some of the non-linear aerodynamic effects resulting from leading-edge and forebody vortices produced at high angle-of-attack through C(sub Lmax). Computed forces and moments, as well as surface pressures, match well enough useful preliminary design information to be extracted. Vortex burst effects and vortex interactions with the configuration are also investigated.

Treiber, David A.↗
DOE OSTI2024

Investigation of the Impact of Flow on MHD Perturbations with the NIMROD Code (Final Report)

This work will enable improved understanding of tokamak edge flows and impurity-species dynamics and their impact on MHD stability as it relates to ELMs, RMPs and QH-mode by leveraging computational developments of the NIMROD code. Experimentally, it is established that the tokamak flows and impurities species can have a substantial effect on MHD stability. This is particularly true for the edge plasma where, for example, large flow shear is correlated with the occurrence of the Quiescent H-mode (QH- mode) state as opposed to operation with edge localized modes (ELMs). Understanding plasma flows is challenging because of the many physical effects that come into play: particle orbits loss, neutrals dynamics, and interaction between multiple ion species with decoupled motion. We will develop a model that incorporates multiple ion species into the MHD framework that includes self-consistent magnetic-field evolution. With this model we will understand how the presence of impurities impacts the tokamak edge-pedestal flows. Finally, we will quantify the impact of the new multiple species modeling on 3D QH-mode simulations. This work will have broader impacts on all MHD studies when multiple species are present (as is the case in modern tokamaks), as well as contributing to the edge and transport communities who also are interested in the studies of multiple-ion-species flows in the edge pedestal region.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Stable Pentagonal Layered Palladium Diselenide Enables Rapid Electrosynthesis of Hydrogen Peroxide

Electrosynthesis of hydrogen peroxide (H 2 O 2 ) via the two-electron oxygen reduction reaction (2e – ORR) is promising for various practical applications, such as wastewater treatment. However, few electrocatalysts are active and selective for 2e – ORR yet are also resistant to catalyst leaching under realistic operating conditions. Here, a joint experimental and computational study reveals active and stable 2e – ORR catalysis in neutral media over layered PdSe 2 with a unique pentagonal puckered ring structure type. Computations predict active and selective 2e – ORR on the basal plane and edge of PdSe 2 , but with distinct kinetic behaviors. Further, electrochemical measurements of hydrothermally synthesized PdSe 2 nanoplates show a higher 2e – ORR activity than other Pd–Se compounds (Pd 4 Se and Pd 17 Se 15 ). PdSe 2 on a gas diffusion electrode can rapidly accumulate H 2 O 2 in buffered neutral solution under a high current density. The electrochemical stability of PdSe 2 is further confirmed by long device operational stability, elemental analysis of the catalyst and electrolyte, and synchrotron X-ray absorption spectroscopy. This work establishes a new efficient and stable 2e – ORR catalyst at practical current densities and opens catalyst designs utilizing the unique layered pentagonal structure motif.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

2024 Atmospheric Radiation Measurement (ARM) Annual Report

This report provides an overview of the Atmospheric Radiation Measurement (ARM) user facility and a sample of achievements for fiscal year 2024 (FY2024). Strong collaborations between nine DOE national laboratories enable ARM to successfully operate in remote locations around the world. This unique partnership supports the DOE mission to provide for the energy security of the nation. Without the support of the following laboratories, ARM would not be the state-of-the-art facility that it is today. ARM is a multi-laboratory, U.S. Department of Energy (DOE) Office of Science user facility and a key contributor to national and international atmospheric and climate research efforts. ARM offers scientists cutting-edge, ground-based observatories, aerial observation capabilities, and high-performance computing. ARM’s capabilities have enabled more than 30 years of continuous measurements of cloud and aerosol properties and their effects on Earth’s energy balance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Los Alamos National Laboratory R&D Intern

This report details the responsibilities, outcomes, and project details of a summer R&D internship at Los Alamos National Laboratory (LANL). LANL is a multidisciplinary laboratory that focuses on current cutting-edge research in many fields such as national security, engineering, materials science, computational modeling, and advanced manufacturing. The goal of the internship project was to work with lab engineers and resources to develop an energy absorbing structure for high-velocity impact applications. The successful development of this technology and methodology would not only positively impact future project funding but also contribute to the laboratory's commitment to solve national security challenges through simultaneous excellence. Such devices would also support efforts surrounding the research and development of energy absorbing structures and would provide new vital information backed by experimentation. Different computational and modeling methods were used to design these structures, in addition to qualitative background information provided by past literature. The resultant designs were successfully tested, and the test results were successfully quantified. From these results, new computational methods were developed through python programming and modeling to predict ideal materialistic properties for these structures given a sufficiently defined application.

36 MATERIALS SCIENCE↗
DOE OSTI2022

GAAF: Searching Activation Functions for Binary Neural Networks Through Genetic Algorithm

Binary neural networks (BNNs) show promising utilization in cost and power-restricted domains such as edge devices and mobile systems. This is due to its significantly less computation and storage demand, but at the cost of degraded performance. To close the accuracy gap, in this paper we propose to add a complementary activation function (AF) ahead of the sign based binarization, and rely on the genetic algorithm (GA) to automatically search for the ideal AFs. These AFs can help extract extra information from the input data in the forward pass, while allowing improved gradient approximation in the backward pass. Fifteen novel AFs are identified through our GA-based search, while most of them show improved performance (up to 2.54% on ImageNet) when testing on different datasets and network models. Interestingly, periodic functions are identified as a key component for most of the discovered AFs, which rarely exist in human designed AFs. Our method offers a novel approach for designing general and application-specific BNN architecture.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Fast GPU-Based Generation of Large Graph Networks From Degree Distributions

Synthetically generated, large graph networks serve as useful proxies to real-world networks for many graph-based applications. The ability to generate such networks helps overcome several limitations of real-world networks regarding their number, availability, and access. Here, we present the design, implementation, and performance study of a novel network generator that can produce very large graph networks conforming to any desired degree distribution. The generator is designed and implemented for efficient execution on modern graphics processing units (GPUs). Given an array of desired vertex degrees and number of vertices for each desired degree, our algorithm generates the edges of a random graph that satisfies the input degree distribution. Multiple runtime variants are implemented and tested: 1) a uniform static work assignment using a fixed thread launch scheme, 2) a load-balanced static work assignment also with fixed thread launch but with cost-aware task-to-thread mapping, and 3) a dynamic scheme with multiple GPU kernels asynchronously launched from the CPU. The generation is tested on a range of popular networks such as Twitter and Facebook, representing different scales and skews in degree distributions. Results show that, using our algorithm on a single modern GPU (NVIDIA Volta V100), it is possible to generate large-scale graph networks at rates exceeding 50 billion edges per second for a 69 billion-edge network. GPU profiling confirms high utilization and low branching divergence of our implementation from small to large network sizes. For networks with scattered distributions, we provide a coarsening method that further increases the GPU-based generation speed by up to a factor of 4 on tested input networks with over 45 billion edges.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1974

Computational procedure for evaluation of space shuttle TPS requirements

The rudiments of a computational procedure are presented for predicting heat transfer rates to the windward pitchplane of a shuttle vehicle under conditions of nonequilibrium flow. The procedure involves computations in three regions of the flow field, namely, the stagnation region, inviscid flow region, and boundary layer region. The thermochemical state in the inviscid flow was calculated with the aid of correlations developed from shadowgraphs of shuttle configurations. The flow in the boundary layer and stagnation regions was computed with a nonequilibrium viscous layer code. For boundary layers, the outer edge thermochemical state was determined by matching the mass flow in the boundary layer with the mass flow represented by each inviscid streamline. Typical solutions are presented to show the effects of surface pressure, catalycity, and homogeneous chemical reactions on flow field variables and surface heat transfer rates.

Tong, H.↗
NASA NTRS1981

Finite element analysis of transonic flows in highly staggered cascades

Finite element method is employed in the solution of full-potential equation for analyzing steady, transonic flows through a series of highly staggered cascades. Convergence characteristics and efficiency of the developed numerical procedure are discussed. Accuracy of the mathematical model is investigated in terms of the employed computational grid. In particular, the importance of accurate representation of the trailing edge together with the shock on the obtained flow results are studied. Numerical results include choked flows, for a cascade of NACA 0012 airfoils with pitch/chord length = 1, and stagger angle of 45 deg.

Akay, H. U.↗