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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Super heat kernel and one-loop divergence of super Yang–Mills theory in conformal supergravity

We consider super Yang–Mills (SYM) theory in N=1 conformal supergravity. Using the background field method and the Faddeev–Popov procedure, the quantized action of the theory is presented. Its one-loop effective action is studied using the heat kernel method. We shall develop a non-iterative scheme, generalizing the non-supersymmetric case, to obtain the super heat kernel coefficients. In particular, the first three coefficients, which govern the one-loop divergence, will be calculated. We shall also demonstrate how to schematically derive the higher-order coefficients. The method presented here can be readily applied to various quantum theories. We shall, as an application, derive the full one-loop divergence of SYM in conformal supergravity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Collins-Soper kernel and reduced soft function in lattice QCD

We evaluate the Collins-Soper kernel and the reduced soft function in lattice QCD, incorporating 𝒪⁡(𝛼 𝑠 ) matching corrections. The calculation relies on the evaluation of the quasitransverse momentum–dependent wave function with asymmetric staple-shaped quark bilinear operators and four-point meson form factors. These quantities are computed nonperturbatively using two 𝑁 𝑓 =2 + 1 + 1 twisted-mass fermion ensembles with the same lattice spacing of 𝑎 = 0.093 fm: the first ensemble has a lattice size of 24 3 × 48 and a pion mass of 346 MeV, and the second one has a lattice size of 32 3 × 64 and a pion mass of 261 MeV. The Collins-Soper kernel and the soft function are needed for the determination of the transverse momentum–dependent parton distribution functions.

Lattice QCD↗
DOE OSTI2024

Determination of the Collins-Soper Kernel from Lattice QCD

This Letter presents a determination of the quark Collins-Soper kernel, which relates transverse-momentum-dependent parton distributions (TMDs) at different rapidity scales, using lattice quantum chromodynamics (QCD). This is the first such determination with systematic control of quark mass, operator mixing, and discretization effects. Next-to-next-to-leading logarithmic matching is used to match lattice-calculable distributions to the corresponding TMDs. The continuum-extrapolated lattice QCD results are consistent with several recent phenomenological parametrizations of the Collins-Soper kernel and are precise enough to disfavor other parametrizations. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Attention-Augmented Parametric Kernel Graph Neural Network (APKGNN) for Node Classification

We present a new graph neural network, the Attention-based Parametric-Kernel augmented Graph Neural Network (APKGNN), developed for node classification tasks. Despite extensive work on modeling multi-faceted relationships between connected nodes of a graph, the effect of attention on edge features mapped to relationships has not yet been analyzed through learning representation. This study derives such an attention vector by first calculating node features corresponding to endpoints of an edge and then aggregating these with extracted local intrinsic patches of a given graph to generate augmented local patch vectors. This process uses a parametric kernel based on Gaussian mixture models (GMMs) to embed local neighborhoods of the graph in local patches. The patch vectors then convolve with the above node features to produce an updated node representation. We show that this new learning representation (APKGNN) achieves higher node classification accuracy on tasks - both standard benchmarks (Cora, PubMed, Citeseer) and new experimental short text corpora where nodes correspond to text documents and words. This implementation of the GNN convolution layer outperforms state-of-the-art (SOTA) algorithms, achieving higher training, validation, and test accuracy by a significant margin on three standard benchmark data sets under both SOTA experimental settings and those for new testbeds.

Bose, Avishek↗
DOE OSTI2018

Ramifications of Evolving Misbehaving Convolutional Neural Network Kernel and Batch Sizes

Deep-learners have many hyper-parameters including learning rate, batch size, kernel size - all playing a significant role toward estimating high quality models. Discovering useful hyper-parameter guidelines is an active area of research, though the state of the art generally uses a brute force, uniform grid approach or random search for finding ideal settings. We share the preliminary results of using an alternative approach to deep learner hyper-parameter tuning that uses an evolutionary algorithm to improve the accuracy of a deep-learner models used in satellite imagery building footprint detection. We found that the kernel and batch size hyper-parameters surprisingly differed from sizes arrived at via a brute force uniform grid approach. These differences suggest a novel role for evolutionary algorithms in determining the number of convolution layers, as well as smaller batch sizes in improving deep-learner models.

Coletti, Mark↗
DOE OSTI2018

Kernelized Generalized Likelihood Ratio Test for Spectrum Sensing in Cognitive Radio

Spectrum sensing in next-generation wireless radio networks is considered a key technology to overcome the problem of spectrum scarcity. Unfortunately, many approaches to spectrum sensing do not work well in low signal-to-noise ratio (SNR) environments. This paper proposes and analyzes a new algorithm named kernelized generalized likelihood ratio test (KGLRT) for spectrum sensing in cognitive radio systems to overcome this problem. Effectively, KGLRT uses a nonlinear kernel to map input data onto a high-dimensional feature space; then, the widely accepted (linear) generalized likelihood ratio test is used for hypothesis testing. This new algorithm gives a gain of 4 dB in SNR over its linear counterpart. A theoretical analysis for this algorithm is given for the first time and is shown analogous to algorithms used in image signal processing. The detection metrics are found to be concentrated random variables; furthermore, the probability distributions of the detection metrics are proved to follow the F -distributions, which agree with the results obtained using the concentration inequality. The analytical thresholds are derived for target false-alarm probabilities. Here, the thresholds are independent of noise power; thus, the proposed algorithm can overcome noise uncertainty issues at very low SNR levels. Simulations validate the theoretical results.

47 OTHER INSTRUMENTATION↗
DOE OSTI2014

Fast dose kernel interpolation using Fourier transform with application to permanent prostate brachytherapy dosimetry

Purpose: Boyer and Mok proposed a fast calculation method employing the Fourier transform (FT), for which calculation time is independent of the number of seeds but seed placement is restricted to calculation grid points. Here an interpolation method is described enabling unrestricted seed placement while preserving the computational efficiency of the original method. Methods: The Iodine-125 seed dose kernel was sampled and selected values were modified to optimize interpolation accuracy for clinically relevant doses. For each seed, the kernel was shifted to the nearest grid point via convolution with a unit impulse, implemented in the Fourier domain. The remaining fractional shift was performed using a piecewise third-order Lagrange filter. Results: Implementation of the interpolation method greatly improved FT-based dose calculation accuracy. The dose distribution was accurate to within 2% beyond 3 mm from each seed. Isodose contours were indistinguishable from explicit TG-43 calculation. Dose-volume metric errors were negligible. Computation time for the FT interpolation method was essentially the same as Boyer's method. Conclusions: A FT interpolation method for permanent prostate brachytherapy TG-43 dose calculation was developed which expands upon Boyer's original method and enables unrestricted seed placement. The proposed method substantially improves the clinically relevant dose accuracy with negligible additional computation cost, preserving the efficiency of the original method.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Asymptotically Compatible Reproducing Kernel Collocation and Meshfree Integration for Nonlocal Diffusion

Reproducing kernel (RK) approximations are meshfree methods that construct shape functions from sets of scattered data. We present an asymptotically compatible (AC) RK collocation method for nonlocal diffusion models with Dirichlet boundary condition. The numerical scheme is shown to be convergent to both nonlocal diffusion and its corresponding local limit as nonlocal interaction vanishes. The analysis is carried out on a special family of rectilinear Cartesian grids for a linear RK method with designed kernel support. The key idea for the stability of the RK collocation scheme is to compare the collocation scheme with the standard Galerkin scheme, which is stable. In addition, assembling the stiffness matrix of the nonlocal problem requires costly computational resources because high-order Gaussian quadrature is necessary to evaluate the integral. We thus provide a remedy to the problem by introducing a quasi-discrete nonlocal diffusion operator for which no numerical quadrature is further needed after applying the RK collocation scheme. The quasi-discrete nonlocal diffusion operator combined with RK collocation is shown to be convergent to the correct local diffusion problem by taking the limits of nonlocal interaction and spatial resolution simultaneously. The theoretical results are then validated with numerical experiments. We additionally illustrate a connection between the proposed technique and an existing optimization based approach based on generalized moving least squares.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Density-Aware Clustering Based on Aggregated Heat Kernel and Its Transformation

Current spectral clustering algorithms suffer from the sensitivity to existing noise, and parameter scaling, and may not be aware of different density distributions across clusters. If these problems are left untreated, the consequent clustering results cannot accurately represent true data patterns, in particular, for complex real world datasets with heterogeneous densities. This paper aims to solve these problems by proposing a diffusion-based Aggregated Heat Kernel (AHK) to improve the clustering stability, and a Local Density Affinity Transformation (LDAT) to correct the bias originating from different cluster densities. AHK statistically\ models the heat diffusion traces along the entire time scale, so it ensures robustness during clustering process, while LDAT probabilistically reveals local density of each instance and suppresses the local density bias in the affinity matrix. Our proposed framework integrates these two techniques systematically. As a result, not only does it provide an advanced noise-resisting and density-aware spectral mapping to the original dataset, but also demonstrates the stability during the processing of tuning the scaling parameter (which usually controls the range of neighborhood). Furthermore, our framework works well with the majority of similarity kernels, which ensures its applicability to many types of data and problem domains. The systematic experiments on different applications show that our proposed algorithms outperform state-of-the-art clustering algorithms for the data with heterogeneous density distributions, and achieve robust clustering performance with respect to tuning the scaling parameter and handling various levels and types of noise.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation

We evaluate AI-assisted generative capabilities on fundamental numerical kernels in high-performance computing (HPC), including AXPY, GEMV, GEMM, SpMV, Jacobi Stencil, and CG. We test the generated kernel codes for a variety of language-supported programming models, including (1) C++ (e.g., OpenMP [including offload], OpenACC, Kokkos, SyCL, CUDA, and HIP), (2) Fortran (e.g., OpenMP [including offload] and OpenACC), (3) Python (e.g., numpy, Numba, cuPy, and pyCUDA), and (4) Julia (e.g., Threads, CUDA.jl, AMDGPU.jl, and KernelAbstractions.jl). We use the GitHub Copilot capabilities powered by the GPT-based OpenAI Codex available in Visual Studio Code as of April 2023 to generate a vast amount of implementations given simple + + prompt variants. To quantify and compare the results, we propose a proficiency metric around the initial 10 suggestions given for each prompt. Results suggest that the OpenAI Codex outputs for C++ correlate with the adoption and maturity of programming models. For example, OpenMP and CUDA score really high, whereas HIP is still lacking. We found that prompts from either a targeted language such as Fortran or the more general purpose Python can benefit from adding code keywords, while Julia prompts perform acceptably well for its mature programming models (e.g., Threads and CUDA.jl). We expect for these benchmarks to provide a point of reference for each programming model's community. Overall, understanding the convergence of large language models, AI, and HPC is crucial due to its rapidly evolving nature and how it is redefining human-computer interactions.

Godoy, William↗
DOE OSTI2025

Mojo: MLIR-based Performance-Portable HPC Science Kernels on GPUs for the Python Ecosystem

We explore the performance and portability of the novel Mojo language for scientific computing workloads on GPUs. As the first language based on the LLVM’s Multi-Level Intermediate Representation (MLIR) compiler infrastructure, Mojo aims to close performance and productivity gaps by combining Python’s interoperability and CUDA-like syntax for compile-time portable GPU programming. We target four scientific workloads: a seven-point stencil (memory-bound), BabelStream (memory-bound), miniBUDE (compute-bound), and Hartree–Fock (compute-bound with atomic operations); and compare their performance against vendor baselines on NVIDIA H100 and AMD MI300A GPUs. We show that Mojo’s performance is competitive with CUDA and HIP for memory-bound kernels, whereas gaps exist on AMD GPUs for atomic operations and for fast-math compute-bound kernels on both AMD and NVIDIA GPUs. Although the learning curve and programming requirements are still fairly low-level, Mojo can close significant gaps in the fragmented Python ecosystem in the convergence of scientific computing and AI.

Godoy, William [ORNL] (ORCID:0000000225905178)↗
DOE OSTI2019

Linux Kernel Role

This Ansible role install and enable a specific kernel version from OS repositories and to ensure matching kernel headers are installed.

Goutin, J.↗
DOE OSTI2019

Application of a new hybrid particle swarm optimization-mixed kernels function-based support vector machine model for reservoir porosity prediction: A case study in Jacksonburg-Stringtown oil field, West Virginia, USA

Porosity is a fundamental property that characterizes the storage capability of fluid and gas-bearing formations in a reservoir. An accurate porosity value can be measured from core samples in the laboratory; however, core analysis is expensive and time consuming. Well-log data can be used to calculate porosity, but the availability of log suites is often limited in mature fields. Therefore, robust porosity prediction requires integration of core-measured porosity with available well-log suites to control for changes in lithology and fluid content. A support vector machine (SVM) model with mixed kernel function (MKF) is used to construct the relationship between limited conventional well-log suites and sparse core data. Porosity is the desired output, and two conventional well-log responses (gamma ray [GR] and bulk density) and three well-log-derived parameters (the slope of GR, the slope of density, and [Formula: see text]) are input parameters. A global stochastic searching algorithm, particle swarm optimization (PSO), is applied to improve the efficiency of locating the appropriate values of five control parameters in MKF-SVM model. The results of SVM with different traditional kernel functions were compared, and the MKF-SVM model provided an improvement over the traditional SVM model. To confirm the advantage of the hybrid PSO-MKF-SVM model, the results from three models: (1) radial basis function (RBF)-based least-squares SVM, (2) multilayer perceptron artificial neural network (ANN), and (3) RBF ANN, are compared with the result of the hybrid PSO-MKF-SVM model. The results indicate that the hybrid PSO-MKF-SVM model improves porosity prediction with the highest correlation coefficient ([Formula: see text] of 0.9560), the highest coefficient of determination ([Formula: see text] of 0.9140), the lowest root-mean-square error (1.6505), average absolute error value (1.4050), and maximum absolute error (2.717).

Geochemistry & Geophysics↗
DOE OSTI2019

Fréchet kernels based on a fractional viscoacoustic wave equation

Incorporating the seismic attenuation into the waveform inversion framework could not only improve the accuracy of the velocity model but also provide an additional Q model. Recently, we proposed a viscoacoustic wave equation assisted by the fractional Laplacian operators to accurately model the wave propagation in heterogeneous attenuating media with computational efficiency. The explicit presence of Q as a coefficient in this equation suggests the potential to conveniently develop its full waveform inversion scheme. In this study, we utilize the adjoint-state method to formulate the computation of the Fréchet kernels with respect to both velocity and attenuation based on this wave equation. These kernels will play a fundamental role in the viscoacoustic multiparameter waveform inversion.

Xing, Guangchi↗
DOE OSTI2015

Verification and Large Deformation Analysis Using the Reproducing Kernel Particle Method

The reproducing kernel particle method (RKPM) is a meshless method used to solve general boundary value problems using the principle of virtual work. RKPM corrects the kernel approximation by introducing reproducing conditions which force the method to be complete to arbritrary order polynomials selected by the user. Effort in recent years has led to the implementation of RKPM within the Sierra/SM physics software framework. The purpose of this report is to investigate convergence of RKPM for verification and validation purposes as well as to demonstrate the large deformation capability of RKPM in problems where the finite element method is known to experience difficulty. Results from analyses using RKPM are compared against finite element analysis. A host of issues associated with RKPM are identified and a number of potential improvements are discussed for future work.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Performance Analysis of Traditional and Data-Parallel Primitive Implementations of Visualization and Analysis Kernels

Measurements of absolute runtime are useful as a summary of performance when studying parallel visualization and analysis methods on computational platforms of increasing concurrency and complexity. We can obtain even more insights by measuring and examining more detailed measures from hardware performance counters, such as the number of instructions executed by an algorithm implemented in a particular way, the amount of data moved to/from memory, memory hierarchy utilization levels via cache hit/miss ratios, and so forth. This work focuses on performance analysis on modern multi-core platforms of three different visualization and analysis kernels that are implemented in different ways: one is "traditional", using combinations of C++ and VTK, and the other uses a data-parallel approach using VTK-m. Our performance study consists of measurement and reporting of several different hardware performance counters on two different multi-core CPU platforms. The results reveal interesting performance differences between these two different approaches for implementing these kernels, results that would not be apparent using runtime as the only metric.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Examination of HIP for production of high density UN kernels

Depleted uranium nitride (UN) kernels with diameters ranging from 420 to 858 microns and theoretical densities between 87 and 91 percent were post-processed using hot isostatic pressing (HIP) in argon gas. This treatment was shown to increase the TD up to 96%. Uranium nitride is highly reactive with oxygen. Therefore, a novel crucible design was implemented to remove impurities in the argon gas via in situ gettering to avoid oxidation of the UN kernels. Established characterization techniques for determining average weight, volume, and ellipticity of particle fuel was used for calculating the density before and after HIPing. Micrographs confirm the nearly full densification of the particles using the gettering approach and processing parameters investigated in this work.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Temperature Effect of Gas Bubble Evolution in UCN Fuel Kernels Irradiated by Swift Xe Ions

UC1-xNx fuel kernels provided by Oak Ridge National Laboratory (ORNL) were irradiated by 84 MeV Xe ions at two different temperatures (450°C & 750°C) at the Argonne Tandem Linac Accelerator System (ATLAS) at Argonne National Laboratory, followed by post-irradiation examination. The primary goal of this study was to understand gas bubble formation (due to accumulation of Xe gas) and corresponding size evolution dependent upon net Xe deposition at the two different temperatures. From the post-irradiation examinations of the samples, it can be concluded at 750°C, with same amount of dose received, the Xe gas bubbles seems to coarsen much more easily compared to 450°C. The results generated for fission gas bubble evolution observed in this study can be used to support fuel performance models for UC1-xNx fuel kernels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗