Search NASA⌕ Search

SEARCH · Search NASA

Results for “Unstructured algorithm”

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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Application of a two-equation turbulence model for high speed compressible flows using unstructured grids

Application of a two-equation compressible turbulence model for practical hypersonic flows 18 presented. The solution algorithm is based on solving all of the governing equations simultaneously. Application of the solution procedure to several test cases for compressible flows show good agreement with theoretical predictions and/or other computational results. The solution procedure is employed to investigate the effects of turbulence in Type III and Type IV shock-shock interactions in hypersonic flows in association with adaptive unstructured grids. Computational results for these cases are compared with available experimental data.

Vemaganti, Gururaja R.↗
DOE OSTI2020

Near-optimal ground state preparation

Preparing the ground state of a given Hamiltonian and estimating its ground energy are important but computationally hard tasks. However, given some additional information, these problems can be solved efficiently on a quantum computer. We assume that an initial state with non-trivial overlap with the ground state can be efficiently prepared, and the spectral gap between the ground energy and the first excited energy is bounded from below. With these assumptions we design an algorithm that prepares the ground state when an upper bound of the ground energy is known, whose runtime has a logarithmic dependence on the inverse error. When such an upper bound is not known, we propose a hybrid quantum-classical algorithm to estimate the ground energy, where the dependence of the number of queries to the initial state on the desired precision is exponentially improved compared to the current state-of-the-art algorithm proposed in [Ge et al. 2019]. These two algorithms can then be combined to prepare a ground state without knowing an upper bound of the ground energy. We also prove that our algorithms reach the complexity lower bounds by applying it to the unstructured search problem and the quantum approximate counting problem.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1987

Finite element methods for compressible flows

The problems of mesh generation and developing effective algorithms for the solution of the equations of compressible flow on unstructured meshes are discussed. Adaptive mesh refinement methods can be implemented in a straightforward manner. Possible adaptive strategies are examined. A finite element method adapted to problems involving high speed compressible flow is described. The adaptive mesh regeneration procedure appears to offer the possibility of large computational savings in three dimensional flow computation.

Morgan, K.↗
NASA NTRS1994

Extending HPF for advanced data parallel applications

The stated goal of High Performance Fortran (HPF) was to 'address the problems of writing data parallel programs where the distribution of data affects performance'. After examining the current version of the language we are led to the conclusion that HPF has not fully achieved this goal. While the basic distribution functions offered by the language - regular block, cyclic, and block cyclic distributions - can support regular numerical algorithms, advanced applications such as particle-in-cell codes or unstructured mesh solvers cannot be expressed adequately. We believe that this is a major weakness of HPF, significantly reducing its chances of becoming accepted in the numeric community. The paper discusses the data distribution and alignment issues in detail, points out some flaws in the basic language, and outlines possible future paths of development. Furthermore, we briefly deal with the issue of task parallelism and its integration with the data parallel paradigm of HPF.

Chapman, Barbara↗
NASA NTRS1998

On Convergence Acceleration Techniques for Unstructured Meshes

A discussion of convergence acceleration techniques as they relate to computational fluid dynamics problems on unstructured meshes is given. Rather than providing a detailed description of particular methods, the various different building blocks of current solution techniques are discussed and examples of solution strategies using one or several of these ideas are given. Issues relating to unstructured grid CFD problems are given additional consideration, including suitability of algorithms to current hardware trends, memory and cpu tradeoffs, treatment of non-linearities, and the development of efficient strategies for handling anisotropy-induced stiffness. The outlook for future potential improvements is also discussed.

Mavriplis, Dimitri J.↗
NASA NTRS1996

3D Feature Extraction for Unstructured Grids

Visualization techniques provide tools that help scientists identify observed phenomena in scientific simulation. To be useful, these tools must allow the user to extract regions, classify and visualize them, abstract them for simplified representations, and track their evolution. Object Segmentation provides a technique to extract and quantify regions of interest within these massive datasets. This article explores basic algorithms to extract coherent amorphous regions from two-dimensional and three-dimensional scalar unstructured grids. The techniques are applied to datasets from Computational Fluid Dynamics and those from Finite Element Analysis.

Silver, Deborah↗
DOE OSTI2024

Haar-Like Wavelets on Hierarchical Trees

Here, discrete wavelet methods, originally formulated in the setting of regularly sampled signals, can be adapted to data defined on a point cloud if some multiresolution structure is imposed on the cloud. A wide variety of hierarchical clustering algorithms can be used for this purpose, and the multiresolution structure obtained can be encoded by a hierarchical tree of subsets of the cloud. Prior work introduced the use of Haar-like bases defined with respect to such trees for approximation and learning tasks on unstructured data. This paper builds on that work in two directions. First, we present an algorithm for constructing Haar-like bases on general discrete hierarchical trees. Second, with an eye towards data compression, we present thresholding techniques for data defined on a point cloud with error controlled in the $L$ $\infty$ norm and in a Hölder-type norm. In a concluding trio of numerical examples, we apply our methods to compress a point cloud dataset, study the tightness of the $L$ $\infty$ error bound, and use thresholding to identify MNIST classifiers with good generalizability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Preparing an Incompressible-Flow Fluid Dynamics Code for Exascale-Class Wind Energy Simulations

The U.S. Department of Energy has identified exascale-class wind farm simulation as critical to wind energy scientific discovery. A primary objective of the ExaWind project is to build high-performance, predictive computational fluid dynamics (CFD) tools that satisfy these modeling needs. GPU accelerators will serve as the computational thoroughbreds of next-generation, exascale-class supercomputers. Here, we report on our efforts in preparing the ExaWind unstructured mesh solver, Nalu-Wind, for exascale-class machines. For computing at this scale, a simple port of the incompressible-flow algorithms to GPUs is insufficient. To achieve high performance, one needs novel algorithms that are application aware, memory efficient, and optimized for the latest-generation GPU devices. The result of our efforts are unstructured-mesh simulations of wind turbines that can effectively leverage thousands of GPUs. In particular, we demonstrate a first-of-its-kind, incompressible-flow simulation using Algebraic Multigrid solvers that strong scales to more than 4000 GPUs on the Summit supercomputer.

algebraic multigrid↗
NASA NTRS1990

Shock interference studies on a circular cylinder at Mach 16

This paper presents the results of a computational study on the shock interference problems on a cylindrical body typical of an engine inlet cowl leading edge at a nominal Mach number of 16. The two-dimensional Navier-Stokes equations are solved assuming the flow to be in chemical and thermal equilibrium and using a finite element method. The algorithm employs a cell-centered fully implicit upwind scheme. Adaptively generated unstructured meshes of triangles and quadrilaterals are employed. Under certain conditions the finite element code resulted in oscillatory solutions for shock interference at Mach 16. Some of the causes of the unsteady behavior are identified, and to the extent possible, such situations are avoided in the present application. Two shock interference conditions involving a Type IV (supersonic jet) and a Type III (attaching shear layer) are solved. The results are compared with available experimental data and reasonable agreement is observed. A semi-empirical method is also used to estimate the maximum surface heat flux and static pressure.

Prabhu, Ramadas K.↗
NASA NTRS2016

Exploration Clinical Decision Support System: Medical Data Architecture

The Exploration Clinical Decision Support (ECDS) System project is intended to enhance the Exploration Medical Capability (ExMC) Element for extended duration, deep-space mission planning in HRP. A major development guideline is the Risk of "Adverse Health Outcomes & Decrements in Performance due to Limitations of In-flight Medical Conditions". ECDS attempts to mitigate that Risk by providing crew-specific health information, actionable insight, crew guidance and advice based on computational algorithmic analysis. The availability of inflight health diagnostic computational methods has been identified as an essential capability for human exploration missions. Inflight electronic health data sources are often heterogeneous, and thus may be isolated or not examined as an aggregate whole. The ECDS System objective provides both a data architecture that collects and manages disparate health data, and an active knowledge system that analyzes health evidence to deliver case-specific advice. A single, cohesive space-ready decision support capability that considers all exploration clinical measurements is not commercially available at present. Hence, this Task is a newly coordinated development effort by which ECDS and its supporting data infrastructure will demonstrate the feasibility of intelligent data mining and predictive modeling as a biomedical diagnostic support mechanism on manned exploration missions. The initial step towards ground and flight demonstrations has been the research and development of both image and clinical text-based computer-aided patient diagnosis. Human anatomical images displaying abnormal/pathological features have been annotated using controlled terminology templates, marked-up, and then stored in compliance with the AIM standard. These images have been filtered and disease characterized based on machine learning of semantic and quantitative feature vectors. The next phase will evaluate disease treatment response via quantitative linear dimension biomarkers that enable image content-based retrieval and criteria assessment. In addition, a data mining engine (DME) is applied to cross-sectional adult surveys for predicting occurrence of renal calculi, ranked by statistical significance of demographics and specific food ingestion. In addition to this precursor space flight algorithm training, the DME will utilize a feature-engineering capability for unstructured clinical text classification health discovery. The ECDS backbone is a proposed multi-tier modular architecture providing data messaging protocols, storage, management and real-time patient data access. Technology demonstrations and success metrics will be finalized in FY16.

Biomedical support↗
NASA NTRSDate not supplied

Validation of the Hypersolve CFD Solver for Entry Descent and Landing Applications

The functional equivalence of the HyperSolve unstructured edge-based, finite-volume computational fluid dynamics code to the Langley Aerothermodynamic Upwind Relaxation Algorithm multiblock structured grid code is documented for applications of interest to the Entry, Descent, and Landing community. A suite of cases using a range of thermochemical gas models on relevant vehicle configurations were analyzed with both codes and the results compared. A tolerance of ±4% difference in surface pressure and surface heat flux from a benchmark LAURA solution was used as the criterion for functional equivalence, and comparisons of flowfield quantities are also included to verify that the thermochemical nonequilibrium capabilities in HyperSolve match those of the LAURA code. The functional equivalence of the HyperSolve unstructured edge-based finite-volume computational fluid dynamics (CFD) code to the Langley Aerothermodynamic Upwind Relaxation Algorithm (LAURA) multiblock structured grid code is documented for applications of interest to the Entry, Descent, and Landing (EDL) community. A suite of cases using a range of thermochemical gas models on EDL-relevant vehicle configurations were analyzed with both codes and the results compared. A tolerance of ±4% difference in surface pressure and surface heat flux from a benchmark LAURA solution was used as the criterion for functional equivalence, and comparisons of flow field quantities are also included to verify that the thermochemical nonequilibrium capabilities in HyperSolve match those of the LAURA code. In general, HyperSolve predictions for surface pressure and surface heat flux are in close agreement with those predicted by LAURA.

hypersolve↗
NASA NTRSDate not supplied

Validation of the HyperSolve CFD Solver for Entry Descent and Landing Applications

The functional equivalence of the HyperSolve unstructured edge-based, finite-volume computational fluid dynamics code to the Langley Aerothermodynamic Upwind Relaxation Algorithm multiblock structured grid code is documented for applications of interest to the Entry, Descent, and Landing community. A suite of cases using a range of thermochemical gas models on relevant vehicle configurations were analyzed with both codes and the results compared. A tolerance of ±4% difference in surface pressure and surface heat flux from a benchmark LAURA solution was used as the criterion for functional equivalence, and comparisons of flowfield quantities are also included to verify that the thermochemical nonequilibrium capabilities in HyperSolve match those of the LAURA code. The functional equivalence of the HyperSolve unstructured edge-based finite-volume computational fluid dynamics (CFD) code to the Langley Aerothermodynamic Upwind Relaxation Algorithm (LAURA) multiblock structured grid code is documented for applications of interest to the Entry, Descent, and Landing (EDL) community. A suite of cases using a range of thermochemical gas models on EDL-relevant vehicle configurations were analyzed with both codes and the results compared. A tolerance of ±4% difference in surface pressure and surface heat flux from a benchmark LAURA solution was used as the criterion for functional equivalence, and comparisons of flow field quantities are also included to verify that the thermochemical nonequilibrium capabilities in HyperSolve match those of the LAURA code. In general, HyperSolve predictions for surface pressure and surface heat flux are in close agreement with those predicted by LAURA.

hypersolve↗
DOE OSTI2021

Preparing an Incompressible-Flow Fluid Dynamics Code for Exascale-Class Wind Energy Simulations: Preprint

The US Department of Energy has identified Exascale-Class wind farm simulation tools as critical to wind energy scientific discovery. A primary objective of the Exawind project is to build high-performance, predictive Computational Fluid Dynamics tools that satisfy these modeling needs. GPU accelerators will serve as the computational thoroughbreds of next generation, Exascale-Class, platforms. Here, we report on our efforts for preparing the Exawind unstructured mesh solver, Nalu-Wind, for Exascale-Class machines. For computing at this scale, a simple port of the incompressible-flow algorithms to GPUs is not sufficient. One needs novel algorithms that are application aware, memory efficient, and optimized for latest generation GPU devices to get high-performance. The result of our efforts are unstructured mesh simulations of wind turbines that use 1/6 the compute resources of Summit supercomputer at Oak Ridge National Lab. In particular, we demonstrate a first-of-its-kind, simulation using Algebraic Multigrid solvers on over 4000 GPUs.

algebraic multigrid↗
DOE OSTI2022

Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning.

This paper describes an efficient reverse-mode differentiation algorithm for contraction operations of tensor networks that may have arbitrary and unconventional network topologies. The approach leverages the tensor contraction tree of Evenbly and Pfeifer (2014), which provides an instruction set for the contraction sequence of a network. We show that this tree can be efficiently leveraged for differentiation of a full tensor network contraction using a recursive scheme that exploits (1) the bilinear property of contraction and (2) the property that trees have single path from root to leaves. While differentiation of tensor-tensor contraction is already possible in most automatic differentiation packages, we show that exploiting these two additional properties in the specific context of contraction sequences can improve efficiency. Following a description of the algorithm and computational complexity analysis, we investigate its utility for gradient-based supervised learning for low-rank function recovery and for fitting real-world unstructured datasets. We demonstrate improved performance over alternating least-squares optimization approaches and the capability to handle heterogeneous and arbitrary tensor network formats. When compared to alternating minimization algorithms, we find that the gradient-based approach requires a smaller oversampling ratio (number of samples compared to number model parameters) for recovery. This increased efficiency extends to fitting unstructured data of varying dimensionality and when employing a variety of tensor network formats. Here, we show improved learning using the hierarchical Tucker method over the tensor-train in high-dimensional settings on a number of benchmark problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Performance tuning of CEED software and first wave apps

The goal of this milestone was the performance tuning of the CEED software and first wave apps. In this milestone, the CEED team developed optimization techniques and tuned for performance the CEED software. Specifically, the focus was on the following: (1) Fast finite element operator storage and evaluation using partial assembly/matrix-free algorithms that take advantage of tensor-product element structure; (2) Architecture optimizations, including the development of performant discretization libraries targeting heterogeneous systems with multi-core CPUs, GPUs, and/or many-core CPUs in single node, cluster, or large-scale ECP supercomputer configurations; and (3) Global kernels for finite element operators, including the development of algorithms and libraries for fast gather-scatter exchanges for action assembly on unstructured graphs and for components needed for global linear solvers. A main part of this milestone was the performance tuning of the CEED first-wave ECP applications. This included the ExaSMR application – Coupled Monte Carlo Neutronics and Fluid Flow Simulation of Small Modular Reactors (ORNL), and the MARBL application – Next-Gen Multi-physics Simulation Code (LLNL). The artifacts delivered include performance improvements in CEED’s 1st wave of applications, and tuned CEED software for various architectures through a number of backends, freely available in the CEED’s repository on GitHub. See the CEED website, http://ceed.exascaleproject.org and the CEED GitHub organization, http://github.com/ceed for more details. In addition to details and results from the above R&D efforts, in this document we are also reporting on other project-wide activities performed in Q4 of FY18 including: CEED’s second annual meeting, a successful minisymposium at the premier international conference on high-order methods (ICOSAHOM), making the CEED milestone reports publicly available, new Laghos release, and other outreach efforts.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

A finite element solution algorithm for nonlinear thermal problems with severe gradients

An implicit enthalpy - flux component finite element algorithm is presented for nonlinear thermal problems with severe gradients. The algorithm is formulated to permit efficient solution of nonlinear heat transfer problems for unstructured meshes with a large range of element sizes. Two transient conduction examples illustrate the effectiveness of the approach for problems with high temperatures and steep gradients.

Thornton, Earl A.↗