Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “PARALLEL PROCESSING”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Multiphysics simulation of microscale copper printing by confined electrodeposition using a nozzle array

3D printing of metals at the microscale and nanoscale is crucial to produce high-density interconnects and intricate structures in electronic devices. Conventional melting and sintering processes are not suitable for these scales due to a reliance on individual metal particles in the size range of tens of micrometers. Confined electrodeposition (CED) is an established alternative to conventional metal 3D printing processes in which an electrolyte is used to selectively induce deposition of the metal on the printing surface. However, commercialization and efficiency of this process have been limited due to a reliance on sub-micrometer nozzles to achieve desirable deposition rates and single nozzle to achieve uniformity of printed structures. Here, we address these challenges by computationally analyzing an array of microscale nozzles. We tailor the convection within the electrolyte to alter both deposition rate and geometric uniformity of the printed structures. The results show that for large nozzles the evaporation alone is not sufficient to obtain high deposition rates, yet an external pressure can be used to increase deposition and alter uniformity (thickness) of printed structures. Our results can be used to design and analyze new experiments toward parallel multi-nozzle deposition using CED toward high-throughput metal printing.

3D printing↗
DOE OSTI2021

Solidification texture, variant selection, and phase fraction in a spot-melt electron-beam powder bed fusion processed Ti-6Al-4V

The effect of a spot melt strategy on the solidification texture, variant selection, phase fraction, and their variations along the build height was investigated in comparison to a conventional linear melt strategy. Ti-6Al-4V alloy samples were manufactured by an electron-beam powder bed fusion process using two different melt strategies and characterized using high-energy synchrotron x-ray diffraction. The linear and spot melt cases resulted in β<100> cube and fiber textures, respectively, with the cube/fiber axis nearly parallel to the build direction. The α phase exhibited Burgers orientation relationship (BOR) with the parent β phase for both melt strategies. Although all of the key texture components and the BOR between the α and β phases were consistently observed in both cases along the build height, there were several measurable differences between the two. At the top of the build height, the β<100> build texture intensity was stronger in the linear case. However, the texture intensity in the linear case decreased gradually from the top to near the substrate, whereas the spot melt case did not show this trend. Moreover, the BOR was examined along the build height and the results showed that there is a distinct variant selection in the spot melt case, unlike the linear case. Specifically, the planar variants sharing ($\bar101$) β || (0002) α with [$\bar2\bar2\bar2$]β and [$2\bar22$]β were the most prominent in the spot melt, but no preference was identified in the directional variants. The β phase fraction was slightly lower in the spot melt case compared to the linear case. In both the cases, the β phase fraction decreased moving towards the substrate but remained significantly higher than that of the powder feedstock. Overall, the novel spot melt strategy produced a more homogeneous microstructure in terms of both the phase fraction and texture across the build height.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Sustainable aviation fuels from biomass and biowaste via bio- and chemo-catalytic conversion: Catalysis, process challenges, and opportunities

Sustainable aviation fuel (SAF) production from biomass and biowaste streams is an attractive option for decarbonizing the aviation sector, one of the most-difficult-to-electrify transportation sectors. Despite ongoing commercialization efforts using ASTM-certified pathways (e.g., lipid conversion, Fischer-Tropsch synthesis), production capacities are still inadequate due to limited feedstock supply and high production costs. New conversion technologies that utilize lignocellulosic feedstocks are needed to meet these challenges and satisfy the rapidly growing market. Combining bio- and chemo-catalytic approaches can leverage advantages from both methods, i.e., high product selectivity via biological conversion, and the capability to build C-C chains more efficiently via chemical catalysis. Herein, conversion routes, catalysis, and processes for such pathways are discussed, while key challenges and meaningful R&D opportunities are identified to guide future research activities in the space. Bio and chemo-catalytic conversion primarily utilize the carbohydrate fraction of lignocellulose, leaving lignin as a waste product. This makes lignin conversion to SAF critical in order to utilize whole biomass, thereby lowering overall production costs while maximizing carbon efficiencies. Thus, lignin valorization strategies are also reviewed herein with vital research areas identified, such as facile lignin depolymerization approaches, highly integrated conversion systems, novel process configurations, and catalysts for the selective cleavage of aryl C–O bonds. The potential efficiency improvements available via integrated conversion steps, such as combined biological and chemo-catalytic routes, along with the use of different parallel pathways, are identified as key to producing all components of a cost-effective, 100% SAF.

09 BIOMASS FUELS↗
DOE OSTI2024

The development and applications of multidimensional biomolecular spectroscopy illustrated by photosynthetic light harvesting

The parallel and synergistic developments of atomic resolution structural information, new spectroscopic methods, their underpinning formalism, and the application of sophisticated theoretical methods have led to a step function change in our understanding of photosynthetic light harvesting, the process by which photosynthetic organisms collect solar energy and supply it to their reaction centers to initiate the chemistry of photosynthesis. The new spectroscopic methods, in particular multidimensional spectroscopies, have enabled a transition from recording rates of processes to focusing on mechanism. We discuss two ultrafast spectroscopies – two-dimensional electronic spectroscopy and two-dimensional electronic-vibrational spectroscopy – and illustrate their development through the lens of photosynthetic light harvesting. Both spectroscopies provide enhanced spectral resolution and, in different ways, reveal pathways of energy flow and coherent oscillations which relate to the quantum mechanical mixing of, for example, electronic excitations (excitons) and nuclear motions. The new types of information present in these spectra provoked the application of sophisticated quantum dynamical theories to describe the temporal evolution of the spectra and provide new questions for experimental investigation. While multidimensional spectroscopies have applications in many other areas of science, we feel that the investigation of photosynthetic light harvesting has had the largest influence on the development of spectroscopic and theoretical methods for the study of quantum dynamics in biology, hence the focus of this review. We conclude with key questions for the next decade of this review.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Direct evidence of the dominant role of multiphoton permanent-dipole transitions in strong-field dissociation of NO 2 +

We study laser-induced dissociation of a metastable NO 2+ ion-beam target into N + +O + , focusing on the prominent contribution by molecules breaking parallel to the polarization at high peak laser intensity (~10 15 W/cm 2 ). Our experimental results and time-dependent Schrödinger equation calculations show that, contrary to commonly held intuition that electronic transitions always prevail, the dominant process underlying this highly aligned dissociation is a multiphoton permanent-dipole transition involving only the electronic ground state and leading to its vibrational continuum. Strong-field permanent-dipole transitions should thus be considered generally, as they may play a significant role in other heteronuclear molecules. Moreover, their role should only grow in importance for longer wavelengths, a trending direction in ultrafast laser studies.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

High-Fidelity Ion State Detection Using Trap-Integrated Avalanche Photodiodes

Integrated technologies greatly enhance the prospects for practical quantum information processing and sensing devices based on trapped ions. High-speed and high-fidelity ion state readout is critical for any such application. Integrated detectors offer significant advantages for system portability and can also greatly facilitate parallel operations if a separate detector can be incorporated at each ion-trapping location. Here, we demonstrate ion quantum state detection at room temperature utilizing single-photon avalanche diodes (SPADs) integrated directly into the substrate of silicon ion trapping chips. Furthermore, we detect the state of a trapped Sr + ion via fluorescence collection with the SPAD, achieving 99.92(1)% average fidelity in 450 μs, opening the door to the application of integrated state detection to quantum computing and sensing utilizing arrays of trapped ions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

A Performance and Energy Study of GPU-Resident Preconditioners for Conjugate Gradient Solvers: In the Context of Existing and Novel Approaches

Optimizing a particular subprogram out of the set of Basic (sparse) Linear Algebra Subprograms (BLAS) for a given architecture is a common topic of research. In applications, however, these BLAS functions rarely appear in isolation; usually, many of them are used together, in various combinations and with varying inputs. As the need to solve a large, sparse linear system is ubiquitous throughout HPC applications, linear solvers constitute a realistic, sufficiently complex and well-defined representative use case for composite BLAS routines. To this end, based on a representative set of matrices drawn from a diverse set of fields, we present a framework to study, from the performance and energy perspective, the efficacy of GPU- resident parallel Conjugate Gradient (CG) linear solver with different preconditioner options, including Gauss-Seidel, Jacobi, and incomplete Cholesky. We also propose a novel GPU-based preconditioner, in which the triangular solves are approximated by an iterative process. The development of this preconditioner was motivated by solving large graph Laplacian linear systems, for which the existing preconditioners either perform slow on GPU-based platforms or are not applicable. We compare the performance of these preconditioners on different hardware accelerator architectures, i.e., AMD MI250X, MI100, Nvidia A100, V100, and Jetson. Our experiments reveal performance trade-offs and provide information on how to select the best strategy for the given linear system, dictated by its properties, and the platform of interest. We demonstrate the application of our novel preconditioner for solving CG and graph Laplacian systems. Overall, the framework can be utilized as a benchmark to guide informed decisions in choosing a specific preconditioner, i.e., whether it is better to rely on the performance of a triangular solver or on the performance of sparse matrix-vector product. Finally, by considering power consumption to solve the linear systems, we report the energy footprint for the solvers.

Preconditioned Conjugate Gradient, GPUs, iterative↗
DOE OSTI2024

Optimizing the Weather Research and Forecasting Model with OpenMP Offload and Codee

Currently, the Weather Research and Forecasting model (WRF) utilizes shared memory (OpenMP) and distributed memory (MPI) parallelisms. To take advantage of GPU resources on the Perlmutter supercomputer at NERSC, we port parts of the computationally expensive routine Fast Spectral Bin Microphysics (FSBM) to NVIDIA GPUs using OpenMP device offloading directives. To facilitate this process, we explore a workflow for optimization which uses both runtime profilers and a static code inspection tool Codee to refactor the subroutine. We observe an 2.24x overall speedup for the CONUS-12km storm test case.

Wichitrnithed, Chayanon (Namo) [Odin Institute]↗
DOE OSTI2024

pnnl/LAP

A software framework to study, from the performance and energy perspective, the efficacy of GPU-resident parallel Conjugate Gradient (CG) linear solver with different preconditioner options, including Gauss-Seidel, Jacobi, and incomplete Cholesky. We also propose a novel GPU-based preconditioner, in which the triangular solves are approximated by an iterative process

Swirydowicz, Kasia↗
DOE OSTI2020

OpenACC unified programming environment for GPU and FPGA multi-hybrid acceleration

Attached accelerators have been frequently used in recent High Per- formance Computing (HPC) systems because of their high performance/power ratio. In particular, the Graphics Processing Unit (GPU) is the most popu- lar accelerator owing to its high peak FLOPS performance and high memory bandwidth supported by HBM2, etc. However, the performance of GPU depends highly on a large degree of SIMD parallelism and has difficulty sustaining a high performance on programs with frequent branch operations or a partially low degree of parallelism.By contrast, a Field Programmable Gate Array (FPGA) has received attention as a different type of accelerator than GPU as a fully reconfigurable processor fitting the target applications. The high performance of FPGA is mainly provided by a pipelined operation and optimized circuit suitable for any operation even with frequent conditional branches. We have been focusing on the flexibility of FPGA to compensate for the weakness of GPU. We believe that the coupling of GPU with FPGA can result in one of the most powerful accelerating platforms available.However, the program coding of GPU and FPGA coupling can be quite difficult for application users. Traditionally, CUDA by NVIDIA has been the most popular programming language with the largest share of GPUs used in HPC, whereas a hardware description language such as Verilog HDL has been used in FPGA programming. OpenCL coding has recently become available even on high-end FPGAs. Moreover, several recent studies have also enabled the OpenACC coding for use in FPGA. In this study, we provide a unified programming system based on OpenACC for a platform equipped with both GPU and FPGA aiming at the next-generation accelerated supercomputer framework. Our programming environment is called Multi-Hybrid OpenACC Translator (MHOAT), and in this paper, we describe the basic concept and prototype system of MHOAT based on an evaluation on the amount of coding required and the performance of a hybrid multi-device accelerated system.

Tsunashima, Ryuta↗
DOE OSTI2022

Development of ultra-pure gadolinium sulfate for the Super-Kamiokande gadolinium project

This paper reports the development and detailed properties of about 13 metric tons of gadolinium sulfate octahydrate, Gd 2 (SO 4 ) 3 • 8H 2 O⁠, which has been dissolved into Super-Kamiokande (SK) in the summer of 2020. We evaluate the impact of radioactive impurities in Gd 2 (SO 4 ) 3 • 8H 2 O on diffuse supernova neutrino background searches and solar neutrino observation and confirm the need to reduce radioactive and fluorescent impurities by about three orders of magnitude from commercially available high-purity Gd 2 (SO 4 ) 3 • 8H 2 O⁠. In order to produce ultra-high-purity Gd 2 (SO 4 ) 3 • 8H 2 O, we have developed a method to remove impurities from gadolinium oxide, Gd 2 O 3 , consisting of acid dissolution, solvent extraction, and pH control processes, followed by a high-purity sulfation process. All of the produced ultra-high-purity Gd 2 (SO 4 ) 3 • 8H 2 O is assayed by inductively coupled plasma mass spectrometry and high-purity germanium detectors to evaluate its quality. Because of the long measurement time of high-purity germanium detectors, we have employed several underground laboratories for making parallel measurements including the Laboratorio Subterráneo de Canfranc in Spain, Boulby in the UK, and Kamioka in Japan. In the first half of production, the measured batch purities were found to be consistent with the specifications. However, in the latter half, the Gd 2 (SO 4 ) 3 • 8H 2 O contained one order of magnitude more 228Ra than the budgeted mean contamination. This was correlated with the corresponding characteristics of the raw material Gd 2 O 3 , in which an intrinsically large contamination was present. Based on their modest impact on SK physics, they were nevertheless introduced into the detector. To reduce 228 Ra for the next stage of gadolinium loading to SK, a new process has been successfully established.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Parallel Algebraic Multigrid for Fusion and Higher-Order PDEs

Multigrid methods play a key role in large-scale scientific simulation because they are among the fastest and most scalable approaches for solving the underlying sparse linear systems of equations that arise from a wide array of Partial Differential Equation (PDE) discretizations. Algebraic multigrid (AMG) is a special type of multigrid method that depends only on the description of the linear system, giving it better portability and broader applicability than geometric multigrid, as it requires no explicit knowledge of the problem geometry. Even though these methods are widely used today, there are still applications where further development is needed. In this report, we focus on PDEs with higher-order terms (e.g., fourth order), concentrating on a PDE that arises in tokamak edge plasma simulations (a tokamak is a machine that confines a plasma using magnetic fields and is believed to be the leading plasma confinement concept for future fusion power plants). General multigrid relaxes a linear system on coarser grids and reverses this process with interpolation, but standard AMG methods struggle with the aforementioned higher-order PDEs. We investigate cyclic coarsening and interpolation heuristics, as well as new iterative approximation methods of refining the solution at each grid to improve the existing multigrid approach. To this end, we ensure that these techniques are transferable to a parallelized setting with LLNL’s supercomputers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

On-lattice kinetic Monte Carlo approaches for modeling molecular anisotropy in resveratrol crystallization

Stilbenes are a class of organic compounds with broad-ranging pharmaceutical and agricultural applications, which are typically isolated and purified through recrystallization. We are motivated by reducing experimental waste and optimizing yield via developing predictive simulations for processing-dependent crystal morphologies. Using resveratrol as a model stilbene system, we have developed an approach for simulating crystallization with molecular resolution using on-lattice kinetic Monte Carlo. In this work, we highlight modifications to the Stochastic Parallel PARticle Kinetic Simulator (SPPARKS) software package, which were essential to this application. Key enhancements include the incorporation of non-orthogonal cell shapes and monomer anisotropy approximations using bound hard spheres. This new SPPARKS application has been applied to resveratrol with attachment energy libraries obtained from density functional theory, resulting in excellent agreement with experimental morphology prediction.

crystallization↗
DOE OSTI2025

Noncollinear ground states of solids with a source-free exchange correlation functional

In this paper, we expand upon the source-free (SF) exchange correlation (XC) functional developed by Sangeeta Sharma and coworkers to plane-wave density functional theory (DFT) based on the projector augmented wave (PAW) method. This constraint is implemented by the current authors within the VASP source code, using a fast Poisson solver that capitalizes on the parallel three-dimensional fast Fourier transforms (FFTs) implemented in VASP. Using this modified XC functional, we explore the improved convergence behavior that results from applying this constraint to the GGA-PBE+U+J functional. In the process, we compare the noncollinear magnetic ground state computed by each functional and their SF counterpart for a select number of magnetic materials in order to provide a metric for comparing with experimentally determined magnetic orderings. We observe significantly improved agreement with experimentally measured magnetic ground-state structures after applying the source-free constraint. Furthermore, we explore the importance of considering probability current densities in spin-polarized systems, even under no applied field. We analyze the XC torque as well, in order to provide theoretical and computational analyses of the net XC magnetic torque induced by the source-free constraint. Along these lines, we highlight the importance of properly considering the real-space integral of the source-free local magnetic XC field. Our analyses on probability currents, net torque, and constant terms draw additional links to the rich body of previous research on spin-current density functional theory (SCDFT), and pave the way for future extensions and corrections to the SF corrected XC functional.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Direction-optimizing Label Propagation Framework for Structure Detection in Graphs: Design, Implementation, and Experimental Analysis

Label Propagation is not only a well-known machine learning algorithm for classification but also an effective method for discovering communities and connected components in networks. We propose a new Direction-optimizing Label Propagation Algorithm (DOLPA) framework that enhances the performance of the standard Label Propagation Algorithm (LPA), increases its scalability, and extends its versatility and application scope. As a central feature, the DOLPA framework relies on the use of frontiers and alternates between label push and label pull operations to attain high performance. It is formulated in such a way that the same basic algorithm can be used for finding communities or connected components in graphs by only changing the objective function used. Additionally, DOLPA has parameters for tuning the processing order of vertices in a graph to reduce the number of edges visited and improve the quality of solution obtained. We present the design and implementation of the enhanced algorithm as well as our shared-memory parallelization of it using OpenMP. We also present an extensive experimental evaluation of our implementations using the LFR benchmark and real-world networks drawn from various domains. Compared with an implementation of LPA for community detection available in a widely used network analysis software, we achieve at most five times the F-Score while maintaining similar runtime for graphs with overlapping communities. We also compare DOLPA against an implementation of the Louvain method for community detection using the same LFR-graphs and show that DOLPA achieves about three times the F-Score at just 10% of the runtime. For connected component decomposition, our algorithm achieves orders of magnitude speedups over the basic LP-based algorithm on large-diameter graphs, up to 13.2× speedup over the Shiloach-Vishkin algorithm, and up to 1.6× speedup over Afforest on an Intel Xeon processor using 40 threads.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Description of Sensor Assignment Optimization Method as Deployed on a Multi-Node Cluster

Data analytic methods are being developed to address the problem of how to assign a sensor set in a nuclear facility such that a requisite level of process monitoring capability is realized and that the sensor set is sufficiently rich to determine the status of the individual sensors with respect to need for calibration. There is an awareness in the nuclear industry that data analytics combined with rich sensor sets represent a means to improve operations and reduce costs. In the industry the calibration problem has been previously approached as an empirical data-driven problem with several methods having been developed. However, the experience of the utilities over the past ten years with these methods indicates that the absence of physics-based information renders the data-driven approach less reliable. Complicating factors such as the inherent variability of operation (both equipment alignment and operating condition) can confound a pure data-driven approach while there are no rigorous guidelines for determining what constitutes an adequate sensor set. The solution under development to overcome these shortcomings supplements the data analytic method with process information in a so-called process-constrained data-analytic approach. Simple balance equations are written for generic components (e.g., mechanical pump, valve, and heat exchanger). These do not require a priori knowledge of process parameters, such as heat transfer coefficients or friction factors. All that is needed on the part of the utility user is to identify the components and how they are connected. This report describes the development of a parallel computing capability for determining the optimal sensor set. The optimal sensor set problem suffers from the curse of dimensionality. Computation time increases exponentially as the size of the system grows. To overcome this difficulty a pre-conditioner algorithm is developed to find an approximate solution close the actual solution. This serves as a seed for the full-blown algorithm and acts to constrain the space that must searched. The optimization algorithms are described and the implementation on a parallel computing platform is described. The application of the method to a use case we are solving in collaboration with our utility partner served to illustrate how the default sensor set in a nuclear plant may not provide sufficient coverage to infer sensor calibration status.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

OpenSn: A massively parallel, open-source simulation environment for discrete ordinates radiation transport

OpenSn is an open-source, massively parallel deterministic radiation transport code for solving the discrete-ordinates ( S N ) form of the Boltzmann transport equation on unstructured, arbitrary polyhedral meshes. It supports high-fidelity simulations involving steady-state, eigenvalue, and adjoint problems for neutral particles (e.g., neutrons, photons, multi-particles), using the multigroup approximation in energy. OpenSn combines angular discretization via discrete ordinates with a discontinuous Galerkin finite element method (DGFEM) in space, enabling accurate resolution of transport physics on arbitrary polyhedral cells, included locally refined spatial grids. It includes multiple angular quadrature types, including locally refined angular quadratures. Written in modern C++ with a Python API, OpenSn runs efficiently on platforms ranging from laptops to supercomputers. The transport sweep algorithm is implemented using a task-based, directed-acyclic-graph (DAG) approach for each angle and supports asynchronous parallelism across thousands of MPI ranks. Group-set aggregation improves compute intensity, and synthetic acceleration techniques (e.g., diffusion synthetic acceleration, second-moment method) enhance solver convergence. OpenSn has been verified on reactor physics problems and demonstrated excellent weak and strong scaling performance on more than 32,768 processes, making it a versatile and robust platform for large-scale transport simulations in complex geometries.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗