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At least 199 records · Page 11

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NASA NTRSDate not supplied

Uncertainty in Observational Estimates of the Aerosol Direct Radiative Effect and Forcing

Aerosols continue to be responsible for the largest uncertainty in determining the anthropogenic radiative forcing of the climate. To both reconcile the large range in satellite-based estimates of the aerosol direct radiative effect (DRE, the direct interaction with solar radiation by all aerosols) and to optimize the design of future observing systems, we build a framework for assessing uncertainty in aerosol DRE and the aerosol direct radiative forcing (DRF, the radiative effect of just anthropogenic aerosols, RF_ari). Shortwave aerosol radiative kernels (Jacobians) were derived using the MERRA-2 reanalysis data. These radiative kernels are used to compute a lower-bound on the systematic uncertainty in observational estimates of the aerosol DRE/DRF by making the optimistic assumption that global aerosol observations can be made with the accuracy found in the Aerosol Robotic Network (AERONET) sun photometer retrievals. The total uncertainty is shown to be dominated by contributions from the aerosol single scattering albedo uncertainty. These uncertainty estimates were compared to a literature survey of mostly satellite-based aerosol DRE/DRF values. Comparisons to previous studies reveal that most have significantly underestimated the aerosol DRE uncertainty. Past estimates of the aerosol DRF uncertainty are smaller (on average) than our optimistic observational estimates, including the aerosol DRF uncertainty given in the Intergovernmental Panel on Climate Change (IPCC) fifth assessment report (AR5).

Tyler James Thorsen↗

NASA NTRSDate not supplied

Uncertainty in Observational Estimates of the Aerosol Direct Radiative Effect and Forcing

Tyler James Thorsen↗

DOE OSTI2021

Noise and error analysis and optimization in particle-based kinetic plasma simulations

In this paper we analyze the noise in macro-particle methods used in plasma physics and fluid dynamics, leading to approaches for minimizing the total error, focusing on electrostatic models in one dimension. We begin by describing kernel density estimation for continuous values of the spatial variable x, expressing the kernel in a form in which its shape and width are represented separately. The covariance matrix of the noise in the density is computed, first for uniform true density. The bandwidth of the covariance matrix C(x,y) is related to the width of the kernel. A feature that stands out is the presence of constant negative terms in the elements of the covariance matrix both on and off-diagonal. These negative correlations are related to the fact that the total number of particles is fixed at each time step; they also lead to the property ∫C(x,y)dy = 0. We investigate the effect of these negative correlations on the electric field computed by Gauss's law, finding that the noise in the electric field is related to a process called the Ornstein-Uhlenbeck bridge, leading to a covariance matrix of the electric field with variance significantly reduced relative to that of a Brownian process. For non-constant density, p(x), still with continuous x, we analyze the total error in the density estimation and discuss it in terms of bias-variance optimization (BVO). For some characteristic length l, determined by the density and its second derivative, and kernel width h, having too few particles within h leads to too much variance; for h that is large relative to l, there is too much smoothing of the density. The optimum between these two limits is found by BVO. For kernels of the same width, it is shown that this optimum (minimum) is weakly sensitive to the kernel shape. Next, we repeat the analysis for x discretized on a grid. In this case the charge deposition rule is determined by a particle shape. An important property to be respected in the discrete system is the exact preservation of total charge on the grid; this property is necessary to ensure that the electric field is equal at both ends, consistent with periodic boundary conditions. We find that if the particle shapes satisfy a partition of unity property, the particle charge deposited on the grid is conserved exactly. Further, if the particle shape is expressed as the convolution of a kernel with another kernel that satisfies the partition of unity, then the particle shape obeys the partition of unity. This property holds for kernels of arbitrary width, including widths that are not integer multiples of the grid spacing. Furthermore, we show results relaxing the approximations used to do BVO optimization analytically, by doing numerical computations of the total error as a function of the kernel width, on a grid in x. The comparison between numerical and analytical results shows good agreement over a range of particle shapes. We discuss the practical implications of our results, including the criteria for design and implementation of computationally efficient particle shapes that take advantage of the developed theory.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Comparative evaluation of deep learning workloads for leadership-class systems

Deep learning (DL) workloads and their performance at scale are becoming important factors to consider as we design, develop and deploy next-generation high-performance computing systems. Since DL applications rely heavily on DL frameworks and underlying compute (CPU/GPU) stacks, it is essential to gain a holistic understanding from compute kernels, models, and frameworks of popular DL stacks, and to assess their impact on science-driven, mission-critical applications. At Oak Ridge Leadership Computing Facility (OLCF), we employ a set of micro and macro DL benchmarks established through the Collaboration of Oak Ridge, Argonne, and Livermore (CORAL) to evaluate the AI readiness of our next-generation supercomputers. In this paper, we present our early observations and performance benchmark comparisons between the Nvidia V100 based Summit system with its CUDA stack and an AMD MI100 based testbed system with its ROCm stack. We take a layered perspective on DL benchmarking and point to opportunities for future optimizations in the technologies that we consider.

Yin, Junqi↗

DOE OSTI2022

Deep learning the properties of inorganic perovskites

We report the ability to accurately and quickly predict the stability of materials and their structural and electronic properties remains a grand challenge in materials science. Density functional theory is widely used as a means of predicting these material properties, but is known to be computationally expensive and scales as the cube of the number of electrons in the material’s unit cell. In this article, for a previously published dataset of inorganic perovskites, we show that a single neural network model using only the elemental properties of the compounds’ constituents can predict lattice constants to within 0.1 Å, heat of formation to within 0.2 eV, and band gaps to within 0.7 eV RMSE. We also compare the performance of the trained network to two widely used regression techniques, namely random forest and Kernel ridge regression, and find that the neural network’s predictions are more accurate for each of the properties. The simultaneous accurate prediction of multiple key properties of technologically relevant materials is promising for rational design and optimization in known and novel chemical spaces.

36 MATERIALS SCIENCE↗

NASA NTRS2013

Design of a Variational Multiscale Method for Turbulent Compressible Flows

A spectral-element framework is presented for the simulation of subsonic compressible high-Reynolds-number flows. The focus of the work is maximizing the efficiency of the computational schemes to enable unsteady simulations with a large number of spatial and temporal degrees of freedom. A collocation scheme is combined with optimized computational kernels to provide a residual evaluation with computational cost independent of order of accuracy up to 16th order. The optimized residual routines are used to develop a low-memory implicit scheme based on a matrix-free Newton-Krylov method. A preconditioner based on the finite-difference diagonalized ADI scheme is developed which maintains the low memory of the matrix-free implicit solver, while providing improved convergence properties. Emphasis on low memory usage throughout the solver development is leveraged to implement a coupled space-time DG solver which may offer further efficiency gains through adaptivity in both space and time.

Design↗

NASA NTRS1972

Development and verification of design methods for ducts in a space nuclear shield

A practical method for computing the effectiveness of a space nuclear shield perforated by small tubing and cavities is reported. Performed calculations use solutions for a two dimensional transport code and evaluate perturbations of that solution using last flight estimates and other kernel integration techniques. In general, perturbations are viewed as a change in source strength of scattered radiation and a change in attenuation properties of the region.

Cerbone, R. J.↗

NASA NTRS1989

CHIMERA II - A real-time multiprocessing environment for sensor-based robot control

A multiprocessing environment for a wide variety of sensor-based robot system, providing the flexibility, performance, and UNIX-compatible interface needed for fast development of real-time code is addressed. The requirements imposed on the design of a programming environment for sensor-based robotic control is outlined. The details of the current hardware configuration are presented, along with the details of the CHIMERA II software. Emphasis is placed on the kernel, low-level interboard communication, user interface, extended file system, user-definable and dynamically selectable real-time schedulers, remote process synchronization, and generalized interprocess communication. A possible implementation of a hierarchical control model, the NASA/NBS standard reference model for telerobot control system is demonstrated.

Stewart, David B.↗

DOE OSTI2026

Linking spout fluidization hydrodynamics to pyrolytic carbon deposition characteristics in a fluidized bed chemical vapor deposition reactor

Spout fluidized bed chemical vapor deposition (SFB-CVD) is the dominant method for producing pyrolytic carbon (PyC) coatings on tristructural-isotropic (TRISO) fuel particles, yet the relationship between gas injector design, fluidization hydrodynamics, and resulting coating quality remains poorly quantified. Here, in this work, three spout fluidized bed (SFB) nozzle geometries were designed and fabricated to empirically investigate how injector-driven changes in particle circulation influence PyC deposition. The geometries were first evaluated in a room temperature fluidization apparatus using time-resolved particle image velocimetry, which highlighted distinct differences in particle velocity fields, circulation pathways, and overall fluidization quality. Graphite versions of each injector geometry were subsequently implemented in a laboratory-scale SFB-CVD reactor to deposit PyC onto surrogate fuel kernels under similar conditions. Post-deposition characterization included particle morphology, coating thickness, porosity distribution, optical anisotropy, and microindentation mechanical testing. Overall, the results show clear differences in coating microstructure as a function of changing injector geometry, despite mechanical testing indicating comparable elastic modulus values across all coatings. This study provides one of the first fully experimental, quantitative mappings between SFB nozzle geometry, fluidization hydrodynamics, and resulting PyC coating structure. The framework established here supports rational injector design and offers a pathway toward improved coating control in future pilot- and production-scale TRISO fuel fabrication systems.

Coated particle fuel↗

NASA NTRS2012

Determining Wheel-Soil Interaction Loads Using a Meshfree Finite Element Approach Assisting Future Missions with Rover Wheel Design

A wheel experiencing sinkage and slippage events poses a high risk to rover missions as evidenced by recent mobility challenges on the Mars Exploration Rover (MER) project. Because several factors contribute to wheel sinkage and slippage conditions such as soil composition, large deformation soil behavior, wheel geometry, nonlinear contact forces, terrain irregularity, etc., there are significant benefits to modeling these events to a sufficient degree of complexity. For the purposes of modeling wheel sinkage and slippage at an engineering scale, meshfree finite element approaches enable simulations that capture sufficient detail of wheel-soil interaction while remaining computationally feasible. This study demonstrates some of the large deformation modeling capability of meshfree methods and the realistic solutions obtained by accounting for the soil material properties. A benchmark wheel-soil interaction problem is developed and analyzed using a specific class of meshfree methods called Reproducing Kernel Particle Method (RKPM). The benchmark problem is also analyzed using a commercially available finite element approach with Lagrangian meshing for comparison. RKPM results are comparable to classical pressure-sinkage terramechanics relationships proposed by Bekker-Wong. Pending experimental calibration by future work, the meshfree modeling technique will be a viable simulation tool for trade studies assisting rover wheel design.

Contreras, Michael T.↗

DOE OSTI2025

Improved Weld Residual Stress Modeling System in BlackBear

This report presents enhancements to the MOOSE-based BlackBear application aimed at improving its capability to simulate welding and other thermo-mechanical manufacturing processes. Two primary avenues of improvement are pursued. First, to enhance user accessibility, we introduce a centralized default block restriction mechanism that ensures coverage checks are performed within user-specified default blocks. This default setting is applied consistently to all block-describable objects, such as variables, kernels, and more. In addition, we develop a modular action for moving heat source simulations, which integrates path file parsing, subdomain modification, and heat source kernel enforcement into a single, streamlined configuration. Second, to improve solver robustness, we implement an alternative method for assigning initial conditions to the updated active domain during the simulation, thereby enhancing convergence behavior. To validate the framework, we design and conduct several benchmark simulations, including heat conduction with progressive material addition, linear elasticity with time-dependent material deposition, and viscoplasticity model with isotropic hardening under similar conditions. Finally, we demonstrate the effectiveness of the proposed framework through large-scale thermo-mechanical welding simulations in both two and three dimensions.

42 ENGINEERING↗

NASA NTRS2001

Laser Ignition Technology for Bi-Propellant Rocket Engine Applications

The fiber optically coupled laser ignition approach summarized is under consideration for use in igniting bi-propellant rocket thrust chambers. This laser ignition approach is based on a novel dual pulse format capable of effectively increasing laser generated plasma life times up to 1000 % over conventional laser ignition methods. In the dual-pulse format tinder consideration here an initial laser pulse is used to generate a small plasma kernel. A second laser pulse that effectively irradiates the plasma kernel follows this pulse. Energy transfer into the kernel is much more efficient because of its absorption characteristics thereby allowing the kernel to develop into a much more effective ignition source for subsequent combustion processes. In this research effort both single and dual-pulse formats were evaluated in a small testbed rocket thrust chamber. The rocket chamber was designed to evaluate several bipropellant combinations. Optical access to the chamber was provided through small sapphire windows. Test results from gaseous oxygen (GOx) and RP-1 propellants are presented here. Several variables were evaluated during the test program, including spark location, pulse timing, and relative pulse energy. These variables were evaluated in an effort to identify the conditions in which laser ignition of bi-propellants is feasible. Preliminary results and analysis indicate that this laser ignition approach may provide superior ignition performance relative to squib and torch igniters, while simultaneously eliminating some of the logistical issues associated with these systems. Further research focused on enhancing the system robustness, multiplexing, and window durability/cleaning and fiber optic enhancements is in progress.

Thomas, Matthew E.↗

NASA NTRS2009

Multi-Core Processor Memory Contention Benchmark Analysis Case Study

Multi-core processors dominate current mainframe, server, and high performance computing (HPC) systems. This paper provides synthetic kernel and natural benchmark results from an HPC system at the NASA Goddard Space Flight Center that illustrate the performance impacts of multi-core (dual- and quad-core) vs. single core processor systems. Analysis of processor design, application source code, and synthetic and natural test results all indicate that multi-core processors can suffer from significant memory subsystem contention compared to similar single-core processors.

Simon, Tyler↗

DOE OSTI2022

Microstructural heterogeneity of the buffer layer of TRISO nuclear fuel particles

Tristructural isotropic (TRISO) nuclear fuel particles contain a layered spherical shell designed to retain fission products; however, failure occurs in rare cases—commonly initiated in the porous pyrocarbon buffer layer. Achieving a comprehensive understanding of the buffer-initiated failure mechanisms requires detailed characterization of the buffer porosity and its heterogeneous distribution across multiple length scales. Here we performed FIB-SEM tomography across the buffer layer thickness (~100 µm) to produce 3D reconstructions of the buffer microstructure with 50 nm spatial resolution. We found an average overall porosity of ~14%, which does not solely account for the low density of the buffer (50% of the theoretical density). Additionally, the local porosity and its fluctuation increase from the kernel interface towards the inner pyrocarbon (IPyC) layer, which we attribute to the chemical vapor deposition process conditions during the TRISO particle fabrication. Detailed characterization of the porous microstructure—including analysis of the pore size, distribution, shape, and orientation—provides insight into the process-structure-property-performance relations of TRISO nuclear fuel particles and will inform multiscale models designed to predict the failure of TRISO particles under irradiation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

SlimIO: Lightweight I/O Path Design for Write Isolation in FDP-backed In-Memory Databases

In-Memory Databases (IMDBs) are widely used with HPC applications to manage transient data, often using snapshot-based persistence for backups. Redis, a representative IMDB, employs both snapshot and Write-Ahead Log (WAL) mechanisms, storing data on persistent devices via the traditional kernel I/O path. This method incurs syscall overhead, I/O contention between processes, and SSD garbage collection (GC) delays. To address these issues, we propose SlimIO, which adopts I/O passthru to minimize syscall overhead and inter-process I/O interference. Additionally, it leverages Flexible Data Placement (FDP) SSDs as backup storage to avoid performance degradation from SSD GC. Experimental results show that SlimIO reduces snapshot time by up to 25%, increases query throughput by up to 30% during non-snapshot periods, and lowers 99.9%-ile latency by up to 50%. Furthermore, it achieves a write amplification factor (WAF) of 1.00, indicating no redundant internal writes, thus extending SSD lifespan.

Lee, Sangyun [Sogang University]↗

DOE OSTI2024

Designing complex concentrated alloys with quantum machine learning and language modeling

Designing novel complex concentrated alloys (CCAs) is an essential topic in materials science. However, due to the complicated high-dimensional component-property relationship, tuning material properties by researchers’ experience is challenging, even when guided by physical or empirical rules. Here, we adopt quantum computing (QC) technology and machine learning models to provide a proof-of-concept application of QC in physical metallurgy. We propose a quantum support vector machine (QSVM) model to predict single-phase CCAs. We show that fine-tuned quantum kernels with entanglement deliver promising performance, with a maximum accuracy of 89.4%. The QSVM model is then used to identify 1,741 lightweight CCAs jointly with a new text-mining-based method. Meanwhile, we devise a controllable approach to study the effect of noise on model performance and find that the noise level needs to be minimized for high-performance QSVM models. Finally, this study provides a practical and general approach to designing CCAs based on quantum technologies.

36 MATERIALS SCIENCE↗

DOE OSTI2021

EXAGRAPH: Graph and combinatorial methods for enabling exascale applications

Combinatorial algorithms in general and graph algorithms in particular play a critical enabling role in numerous scientific applications. However, the irregular memory access nature of these algorithms makes them one of the hardest algorithmic kernels to implement on parallel systems. With tens of billions of hardware threads and deep memory hierarchies, the exascale computing systems in particular pose extreme challenges in scaling graph algorithms. The codesign center on combinatorial algorithms, ExaGraph, was established to design and develop methods and techniques for efficient implementation of key combinatorial (graph) algorithms chosen from a diverse set of exascale applications. Algebraic and combinatorial methods have a complementary role in the advancement of computational science and engineering, including playing an enabling role on each other. In this paper, we survey the algorithmic and software development activities performed under the auspices of ExaGraph from both a combinatorial and an algebraic perspective. In particular, we detail our recent efforts in porting the algorithms to manycore accelerator (GPU) architectures. We also provide a brief survey of the applications that have benefited from the scalable implementations of different combinatorial algorithms to enable scientific discovery at scale. We believe that several applications will benefit from the algorithmic and software tools developed by the ExaGraph team.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

PETSc/TAO developments for GPU-based early exascale systems

The Portable Extensible Toolkit for Scientific Computation (PETSc) library provides scalable solvers for nonlinear time-dependent differential and algebraic equations and for numerical optimization via the Toolkit for Advanced Optimization (TAO). PETSc is used in dozens of scientific fields and is an important building block for many simulation codes. During the U.S. Department of Energy’s Exascale Computing Project, the PETSc team has made substantial efforts to enable efficient utilization of the massive fine-grain parallelism present within exascale compute nodes and to enable performance portability across exascale architectures. We recap some of the challenges that designers of numerical libraries face in such an endeavor, and then discuss the many developments we have made, which include the addition of new GPU backends, features supporting efficient on-device matrix assembly, better support for asynchronicity and GPU kernel concurrency, and new communication infrastructure. In conclusion, we evaluate the performance of these developments on some pre-exascale systems as well as the early exascale systems Frontier and Aurora, using compute kernel, communication layer, solver, and mini-application benchmark studies, and then close with a few observations drawn from our experiences on the tension between portable performance and other goals of numerical libraries.

Exascale Computing Project (ECP)↗