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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Closures for Two-fluid Five-moment Equations for Magnetized Plasmas (Final Scientific Report)

In the proposed work, we will develop a complete set of closures for two-fluid five-moment equations for magnetized plasmas. Specifically, we propose to obtain explicit formulas for electrons and ions to express heat flow, viscosity, frictional force, and collisional heating in terms of density, and temperature, flow velocity. For this purpose, we solve a set of general moment equations for the relevant closure moments. An infinite hierarchy of general moment equations is completely equivalent to the original Landau-Fokker-Planck kinetic equation. In the small gyro-period expansion, the parallel components of the general moment equations agree with the moments of the drift-kinetic equation (DKE). The advantages of solving the moment equations include (i) an exact treatment of the collision operator, (ii) closure and transport relations with no flux-surface average, and (iii) a unified form of closures for general collisionality. Practically we solve a truncated set of moment equations and hence must verify the convergence of the solutions by increasing number of moments. The parallel integral (nonlocal) closures obtained by solving a set of linearized parallel moment equations are useful only when the nonlinear coupling of moments to the temperature and magnetic field gradient terms is negligible. When the nonlinear coupling terms are substantial, we propose a Fourier transform method to solve the parallel moment equations. By Fourier-expanding the magnetic field, we can convert the differential equations of parallel moments to linear algebraic equations for Fourier components of the moments. The convergence of the solutions can be confirmed by increasing the number of Fourier modes. Our preliminary study in axisymmetric circular magnetic geometry with a large aspect ratio shows that (i) the solutions for Fourier modes n=0 and n=1 converge when modes up to n=2 are included in the expansion, and (ii) lower collisionality requires more moments for convergent closures, as in the case of the integral closures. The proposed work will start from this simple magnetic geometry to find general parallel closures and then be generalized to explore a non-circular magnetic field with finite aspect-ratio. With parallel moments known, the perpendicular component of moment closures can be obtained by inverting the generalized cross product operator in the general moment equations. These efforts will be performed in conjunction with several multi-institutional projects, NIMROD, the Center for Extended MHD Modeling (CEMM), and the Plasma Science and Innovation (PSI) Center.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1996

Load Balancing Unstructured Adaptive Grids for CFD Problems

Mesh adaption is a powerful tool for efficient unstructured-grid computations but causes load imbalance among processors on a parallel machine. A dynamic load balancing method is presented that balances the workload across all processors with a global view. After each parallel tetrahedral mesh adaption, the method first determines if the new mesh is sufficiently unbalanced to warrant a repartitioning. If so, the adapted mesh is repartitioned, with new partitions assigned to processors so that the redistribution cost is minimized. The new partitions are accepted only if the remapping cost is compensated by the improved load balance. Results indicate that this strategy is effective for large-scale scientific computations on distributed-memory multiprocessors.

Biswas, Rupak↗
DOE OSTI2014

A rigorous sequential update strategy for parallel kinetic Monte Carlo simulation

The kinetic Monte Carlo (kMC) method is used in many scientific fields in applications involving rare-event transitions. Due to its discrete stochastic nature, efforts to parallelize kMC approaches often produce unbalanced time evolutions requiring complex implementations to ensure correct statistics. In the context of parallel kMC, the sequential update technique has shown promise by generating high quality distributions with high relative efficiencies for short-range systems. In this work, we provide an extension of the sequential update method in a parallel context that rigorously obeys detailed balance, which guarantees exact equilibrium statistics for all parallelization settings. Furthermore, our approach also preserves nonequilibrium dynamics with minimal error for many parallelization settings, and can be used to achieve highly precise sampling.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Considerations on the Use of Custom Accelerators for Big Data Analytics

Accelerators, including Graphic Processing Units (GPUs) for gen- eral purpose computation, many-core designs with wide vector units (e.g., Intel Phi), have become a common component of many high performance clusters. The appearance of more stable and reliable tools tools that can automatically convert code written in high-level specifications with annotations (such as C or C++) to hardware de- scription languages (High-Level Synthesis - HLS), is also setting the stage for a broader use of reconfigurable devices (e.g., Field Pro- grammable Gate Arrays - FPGAs) in high performance system for the implementation of custom accelerators, helped by the fact that new processors include advanced cache-coherent interconnects for these components. In this chapter, we briefly survey the status of the use of accelerators in high performance systems targeted at big data analytics applications. We argue that, although the progress in the use of accelerators for this class of applications has been sig- nificant, differently from scientific simulations there still are gaps to close. This is particularly true for the ”irregular” behaviors exhibited by no-SQL, graph databases. We focus our attention on the limits of HLS tools for data analytics and graph methods, and discuss a new architectural template that better fits the requirement of this class of applications. We validate the new architectural templates by mod- ifying the Graph Engine for Multithreaded System (GEMS) frame- work to support accelerators generated with such a methodology, and testing with queries coming from the Lehigh University Benchmark (LUBM). The architectural template enables better supporting the task and memory level parallelism present in graph methods by sup- porting a new control model and a enhanced memory interface. We show that out solution allows generating parallel accelerators, pro- viding speed ups with respect to conventional HLS flows. We finally draw conclusions and present a perspective on the use of reconfig- urable devices and Design Automation tools for data analytics.

Data Analytics↗
DOE OSTI2017

Controlled Selective CVD Growth of ZnO Nanowires Enabled by Mask‐Free Fabrication Approach using Aqueous Fe Catalytic Inks

Abstract Fabrication process that enables selective growth of vertically oriented zinc oxide (ZnO) nanowires (NWs) via chemical vapor deposition method and mask‐free patterning approach is reported. It is shown that synthesis of high‐quality ZnO nanowires in various architectures is achievable by optimizing the growth conditions and by precise patterning of catalytic ink precursors. Parallel direct‐write patterning method is utilized to fabricate arrays of different architectures on Si/SiO 2 substrates and directly on devices at preselected locations. The production of high‐quality, crystalline ZnO NWs is demonstrated using aqueous iron catalytic inks. The composition of the ink and the lateral size of the patterns deposited on substrates are shown to affect the resulting nanowires and thus, allowing to control the geometry (length and diameter) of the individual ZnO NWs in the patterned assemblies. The results indicate that our protocols are tailored to the fabrication of ZnO NWs with specific surface geometries and interface functionalities for variety of targeted device applications.

Alameri, Dheyaa↗
DOE OSTI2014

Grappolo

Community detection has become a fundamental operation in numerous graph-theoretic applications. It is used to reveal natural divisions that exist within real world networks without imposing prior size or cardinality constraints on the set of communities. Despite its potential for application, there is only limited support for community detection on large-scale parallel computers, largely owing to the irregular and inherently sequential nature of the underlying heuristics. In this paper, we present parallelization heuristics for fast community detection using the Louvain method as the serial template. The Louvain method is an iterative heuristic for modularity optimization. Originally developed by Blondel et al. in 2008, the method has become increasingly popular owing to its ability to detect high modularity community partitions in a fast and memory-efficient manner. However, the method is also inherently sequential, thereby limiting its scalability. Here, we observe certain key properties of this method that present challenges for its parallelization, and consequently propose heuristics that are designed to break the sequential barrier. For evaluation purposes, we implemented our heuristics using OpenMP multithreading, and tested them over real world graphs derived from multiple application domains (e.g., internet, citation, biological). Compared to the serial Louvain implementation, our parallel implementation is able to produce community outputs with a higher modularity for most of the inputs tested, in comparable number of iterations, while providing real speedups of up to 8x using 32 threads. In addition, our parallel implementation was able to exhibit weak scaling properties on up to 32 threads.

Halappanavar, Mahantesh↗
DOE OSTI2019

Fast approximate STEM image simulations from a machine learning model

Abstract Accurate quantum mechanical scanning transmission electron microscopy image simulation methods such as the multislice method require computation times that are too large to use in applications in high-resolution materials imaging that require very large numbers of simulated images. However, higher-speed simulation methods based on linear imaging models, such as the convolution method, are often not accurate enough for use in these applications. We present a method that generates an image from the convolution of an object function and the probe intensity, and then uses a multivariate polynomial fit to a dataset of corresponding multislice and convolution images to correct it. We develop and validate this method using simulated images of Pt and Pt–Mo nanoparticles and find that for these systems, once the polynomial is fit, the method runs about six orders of magnitude faster than parallelized CPU implementations of the multislice method while achieving a 1 − R 2 error of 0.010–0.015 and root-mean-square error/standard deviation of dataset being predicted of about 0.1 when compared to full multislice simulations.

47 OTHER INSTRUMENTATION↗
NASA NTRS1992

r-adaptive methods based on element-level error indicators for parallel analysis of plates and shells

The formulation and application of element-level error indicators based on parametrized variational principles are investigated. These indicators are ideally suited to drive adaptive mesh refinment on parallel computers where access to neighboring elements resident on different processors may incur significant computational overhead. Furthermore, such estimators are not affected by physical jumps at wavefronts or interfaces. An estimator derived from the higher-order element energy is applied to r adaptation of meshes in plates and shell structures where the exact solution is known.

Militello, Carmelo↗
NASA NTRS1995

Three-dimensional object tracking system and method employing plural sensors and plural processors for performing parallel processing

A multiple sensor/multiple processor 3-D tracking system includes multiple sensor (camera) and DSP combinations and a controller connected so as to create a parallel computer from the multiple DSPs. The camera/DSP combinations and the controller are connected together to form a ring. For 3-D position measurement and tracking applications, each camera/DSP combination is programmed with an identical program to function as a node in a true parallel computer, and the transformation equations are solved in a distributed fashion using the entire parallel machine. The distributed processor processes the camera x and y point measurements in a distributed and parallel fashion to yield a solution to the coordinate transformation equations much more quickly than is possible with multiple camera/DSP combinations and a single processor.

Welch, Sharon S.↗
NASA NTRS2000

A Two Colorable Fourth Order Compact Difference Scheme and Parallel Iterative Solution of the 3D Convection Diffusion Equation

A new fourth order compact difference scheme for the three dimensional convection diffusion equation with variable coefficients is presented. The novelty of this new difference scheme is that it Only requires 15 grid points and that it can be decoupled with two colors. The entire computational grid can be updated in two parallel subsweeps with the Gauss-Seidel type iterative method. This is compared with the known 19 point fourth order compact differenCe scheme which requires four colors to decouple the computational grid. Numerical results, with multigrid methods implemented on a shared memory parallel computer, are presented to compare the 15 point and the 19 point fourth order compact schemes.

Zhang, Jun↗
DOE OSTI2014

Parallel Heuristics for Scalable Community Detection

Community detection has become a fundamental operation in numerous graph-theoretic applications. It is used to reveal natural divisions that exist within real world networks without imposing prior size or cardinality constraints on the set of communities. Despite its potential for application, there is only limited support for community detection on large-scale parallel computers, largely owing to the irregular and inherently sequential nature of the underlying heuristics. In this paper, we present parallelization heuristics for fast community detection using the Louvain method as the serial template. The Louvain method is an iterative heuristic for modularity optimization. Originally developed by Blondel et al. in 2008, the method has become increasingly popular owing to its ability to detect high modularity community partitions in a fast and memory-efficient manner. However, the method is also inherently sequential, thereby limiting its scalability to problems that can be solved on desktops. Here, we observe certain key properties of this method that present challenges for its parallelization, and consequently propose multiple heuristics that are designed to break the sequential barrier. Our heuristics are agnostic to the underlying parallel architecture. For evaluation purposes, we implemented our heuristics on shared memory (OpenMP) and distributed memory (MapReduce-MPI) machines, and tested them over real world graphs derived from multiple application domains (internet, biological, natural language processing). Experimental results demonstrate the ability of our heuristics to converge to high modularity solutions comparable to those output by the serial algorithm in nearly the same number of iterations, while also drastically reducing time to solution.

clustering, graph algorithms↗
NASA NTRS2002

Software Engineering Support of the Third Round of Scientific Grand Challenge Investigations: Earth System Modeling Software Framework Survey

One of the most significant challenges in large-scale climate modeling, as well as in high-performance computing in other scientific fields, is that of effectively integrating many software models from multiple contributors. A software framework facilitates the integration task, both in the development and runtime stages of the simulation. Effective software frameworks reduce the programming burden for the investigators, freeing them to focus more on the science and less on the parallel communication implementation. while maintaining high performance across numerous supercomputer and workstation architectures. This document surveys numerous software frameworks for potential use in Earth science modeling. Several frameworks are evaluated in depth, including Parallel Object-Oriented Methods and Applications (POOMA), Cactus (from (he relativistic physics community), Overture, Goddard Earth Modeling System (GEMS), the National Center for Atmospheric Research Flux Coupler, and UCLA/UCB Distributed Data Broker (DDB). Frameworks evaluated in less detail include ROOT, Parallel Application Workspace (PAWS), and Advanced Large-Scale Integrated Computational Environment (ALICE). A host of other frameworks and related tools are referenced in this context. The frameworks are evaluated individually and also compared with each other.

Talbot, Bryan↗
DOE OSTI2013

Parallel multiscale simulations of a brain aneurysm

Cardiovascular pathologies, such as a brain aneurysm, are affected by the global blood circulation as well as by the local microrheology. Hence, developing computational models for such cases requires the coupling of disparate spatial and temporal scales often governed by diverse mathematical descriptions, e.g., by partial differential equations (continuum) and ordinary differential equations for discrete particles (atomistic). However, interfacing atomistic-based with continuum-based domain discretizations is a challenging problem that requires both mathematical and computational advances. We present here a hybrid methodology that enabled us to perform the first multiscale simulations of platelet depositions on the wall of a brain aneurysm. The large scale flow features in the intracranial network are accurately resolved by using the high-order spectral element Navier–Stokes solver NεκTαr. The blood rheology inside the aneurysm is modeled using a coarse-grained stochastic molecular dynamics approach (the dissipative particle dynamics method) implemented in the parallel code LAMMPS. The continuum and atomistic domains overlap with interface conditions provided by effective forces computed adaptively to ensure continuity of states across the interface boundary. A two-way interaction is allowed with the time-evolving boundary of the (deposited) platelet clusters tracked by an immersed boundary method. The corresponding heterogeneous solvers (NεκTαr and LAMMPS) are linked together by a computational multilevel message passing interface that facilitates modularity and high parallel efficiency. Results of multiscale simulations of clot formation inside the aneurysm in a patient-specific arterial tree are presented. We also discuss the computational challenges involved and present scalability results of our coupled solver on up to 300 K computer processors. Validation of such coupled atomistic-continuum models is a main open issue that has to be addressed in future work.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1993

Use of networked workstations for parallel nonlinear structural dynamic simulations of rotating bladed-disk assemblies

The principal objective of this research is to investigate, develop and demonstrate coarse-grained, parallel-processing strategies for nonlinear dynamic simulations for rotating bladed-disk assemblies. The parallel -processing strategies addressed include numerical algorithms for parallel nonlinear solutions and techniques to effect load balancing among processors. The parallel environment employed is a distributed-memory, coarse-grained one consisting of networked workstations. A parallel explicit time integration method has been implemented for transient nonlinear solutions of rotationg bladed-disk assemblies. Automatic domain partitioning techniques have been investigated for load balancing among processors. Advanced computing environments, data structures and interactive computer graphics all contribute to an integrated parallel finite element analysis system to facilitate more efficient and powerful dynamic simulations.

Hsieh, Shang-Hsien↗
NASA NTRS1994

A Parallel Incompressible Navier-Stokes Solver with Multigrid Iterations

We developed a parallel, numerically accurate and stable, and computationally efficient finate-difference incompressible Navier-Stokes (N-S) fluid flow solver. The solver runs on both sequential and massively parallel computers. The numerical method used here is a second-order projection method on a staggered grid. The code is highly modular and it can be used either as a stand-alone flow solver and or a template code which can be adapted or expanded to a specific application. Numerical results and parallel performances of our code on Intel Delta and Paragon are reported.

Navier-Stokes solver↗