scholarly journals FEM using projection of physical properties suitable for movement modeling and optimization processes

2020 ◽  
Vol 39 (5) ◽  
pp. 1185-1199
Author(s):  
Baptiste Ristagno ◽  
Dominique Giraud ◽  
Julien Fontchastagner ◽  
Denis Netter ◽  
Noureddine Takorabet ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Transient State ◽  
Mathematical Function ◽  
Processing Unit ◽  
Time Saving ◽  
Content Type ◽  
Central Processing ◽  
Cpu Time ◽  
Simple Geometry ◽  
Evaluation Time

Purpose Optimization processes and movement modeling usually require a high number of simulations. The purpose of this paper is to reduce global central processing unit (CPU) time by decreasing each evaluation time. Design Methodology Approach Remeshing the geometry at each iteration is avoided in the proposed method. The idea consists in using a fixed mesh on which functions are projected to represent geometry and supply. Findings Results are very promising. CPU time is reduced for three dimensional problems by almost a factor two, keeping a low relative deviation from usual methods. CPU time saving is performed by avoiding meshing step and also by a better initialization of iterative resolution. Optimization, movement modeling and transient-state simulation are very efficient and give same results as usual finite element method. Research Limitations Implications The method is restricted to simple geometry owing to the difficulty of finding spatial mathematical function describing the geometry. Moreover, a compromise between imprecision, caused by the boundary evaluation, and time saving must be found. Originality Value The method can be applied to optimize rotating machines design. Moreover, movement modeling is performed by shifting functions corresponding to moving parts.

Analysis of strategies of circuit optimisation on basis of maximum principle

2018 ◽  
Vol 37 (1) ◽  
pp. 484-503
Author(s):  
Alexander Zemliak
Keyword(s):  
Maximum Principle ◽  
Dimensional Case ◽  
Processing Unit ◽  
Local Minima ◽  
Control Vector ◽  
Content Type ◽  
The Maximum Principle ◽  
Central Processing ◽  
Cpu Time ◽  
First Time

Purpose This paper aims to propose a new approach on the problem of circuit optimisation by using the generalised optimisation methodology presented earlier. This approach is focused on the application of the maximum principle of Pontryagin for searching the best structure of a control vector providing the minimum central processing unit (CPU) time. Design/methodology/approach The process of circuit optimisation is defined mathematically as a controllable dynamical system with a control vector that changes the internal structure of the equations of the optimisation procedure. In this case, a well-known maximum principle of Pontryagin is the best theoretical approach for finding of the optimum structure of control vector. A practical approach for the realisation of the maximum principle is based on the analysis of the behaviour of a Hamiltonian for various strategies of optimisation and provides the possibility to find the optimum points of switching for the control vector. Findings It is shown that in spite of the fact that the maximum principle is not a sufficient condition for obtaining the global minimum for the non-linear problem, the decision can be obtained in the form of local minima. These local minima provide rather a low value of the CPU time. Numerical results were obtained for both a two-dimensional case and an N-dimensional case. Originality/value The possibility of the use of the maximum principle of Pontryagin to a problem of circuit optimisation is analysed systematically for the first time. The important result is the theoretical justification of formerly discovered effect of acceleration of the process of circuit optimisation.

An Accelerated 3D Navier–Stokes Solver for Flows in Turbomachines

2010 ◽  
Vol 133 (2) ◽  
Author(s):  
Tobias Brandvik ◽  
Graham Pullan
Keyword(s):  
Graphics Processing Units ◽  
Three Dimensional ◽  
Navier Stokes ◽  
Linear Scaling ◽  
Test Case ◽  
Processing Unit ◽  
Central Processing ◽  
Order Of Magnitude ◽  
Graphics Processing ◽  
Good Agreement

A new three-dimensional Navier–Stokes solver for flows in turbomachines has been developed. The new solver is based on the latest version of the Denton codes but has been implemented to run on graphics processing units (GPUs) instead of the traditional central processing unit. The change in processor enables an order-of-magnitude reduction in run-time due to the higher performance of the GPU. The scaling results for a 16 node GPU cluster are also presented, showing almost linear scaling for typical turbomachinery cases. For validation purposes, a test case consisting of a three-stage turbine with complete hub and casing leakage paths is described. Good agreement is obtained with previously published experimental results. The simulation runs in less than 10 min on a cluster with four GPUs.

PRACTICAL PERFORMANCE ASSESSMENT OF ACCOMPANYING COORDINATE EXPANSION RECURRENCE RELATION ALGORITHM FOR COMPUTATION OF ELECTRON REPULSION INTEGRALS

2005 ◽  
Vol 04 (01) ◽  
pp. 139-149 ◽  
Author(s):  
MICHIO KATOUDA ◽  
MASATO KOBAYASHI ◽  
HIROMI NAKAI ◽  
SHIGERU NAGASE
Keyword(s):  
Computer Program ◽  
Recurrence Relation ◽  
Basis Set ◽  
Basis Sets ◽  
Processing Unit ◽  
Central Processing ◽  
Cpu Time ◽  
Electron Repulsion ◽  
Electron Repulsion Integrals ◽  
Practical Performance

We have developed a computer program for evaluation of electron repulsion integrals (ERIs) based on the accompanying coordinate expansion recurrence relation (ACE-RR) algorithm, which has been recently developed as an efficient algorithm for computation of ERIs using Pople-type basis sets (STO-3G and 6-31G, for example) and derivatives of ERIs [Kobayashi and Nakai, J Chem Phys121:4050 2004]. The computer program can be linked to GAMESS ab initio quantum chemistry program. The practical performance of the ACE-RR method is assessed by means of the central processing unit (CPU) time for the first direct self-consistent field cycle on a model system (4 × 4 × 4 cubic hydrogen lattice), taxol ( C 47 H 51 NO 14), and valinomycin ( C 54 H 90 N 6 O 18) using Pople-type basis sets. The considerable efficiency of the present ACE-RR method is demonstrated by measuring the CPU time. The present ACE-RR method is comparable to or at most 30% faster than the Pople–Hehre method which is also designed for efficient computation of ERIs using Pople-type basis sets. Furthermore, the ACE-RR method is drastically faster than the Dupuis–Rys–King method in the case where the degree of contraction of Pople-type basis sets is high: 7.5 times faster in the case of valinomycin using STO-6G basis set, for example.

scholarly journals Scheduling Two Identical Parallel Machines Subjected to Release Times, Delivery Times and Unavailability Constraints

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1025
Author(s):  
Adel M. Al-Shayea ◽  
Mustafa Saleh ◽  
Moath Alatefi ◽  
Mageed Ghaleb
Keyword(s):  
Parallel Machines ◽  
Release Time ◽  
Processing Unit ◽  
Scheduling Problem ◽  
Identical Parallel Machines ◽  
Central Processing ◽  
Cpu Time ◽  
Release Times ◽  
Delivery Times ◽  
Input Variables

This paper proposes a genetic algorithm (GA) for scheduling two identical parallel machines subjected to release times and delivery times, where the machines are periodically unavailable. To make the problem more practical, we assumed that the machines are undergoing periodic maintenance rather than making them always available. The objective is to minimize the makespan (Cmax). A lower bound (LB) of the makespan for the considered problem was proposed. The GA performance was evaluated in terms of the relative percentage deviation (RPD) (the relative distance to the LB) and central processing unit (CPU) time. Response surface methodology (RSM) was used to optimize the GA parameters, namely, population size, crossover probability, mutation probability, mutation ratio, and pressure selection, which simultaneously minimize the RPD and CPU time. The optimized settings of the GA parameters were used to further analyze the scheduling problem. Factorial design of the scheduling problem input variables, namely, processing times, release times, delivery times, availability and unavailability periods, and number of jobs, was used to evaluate their effects on the RPD and CPU time. The results showed that increasing the release time intervals, decreasing the availability periods, and increasing the number of jobs increase the RPD and CPU time and make the problem very difficult to reach the LB.

Acceleration of the numerical solution for the radiative transfer equation using a modified relaxation factor

2020 ◽  
Vol 37 (5) ◽  
pp. 1823-1847 ◽  
Author(s):  
Carlos Enrique Torres-Aguilar ◽  
Jesús Xamán ◽  
Pedro Moreno-Bernal ◽  
Iván Hernández-Pérez ◽  
Ivett Zavala-Guillén ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Numerical Solution ◽  
Transfer Equation ◽  
Radiative Transfer Equation ◽  
Boundary Surface ◽  
Processing Unit ◽  
Content Type ◽  
Cpu Time ◽  
Relaxation Factor ◽  
Factor Method

Purpose The purpose of this study is to propose a novel relaxation modified factor to accelerate the numerical solution of the radiative transfer equation (RTE) with several high-resolution total variation diminishing schemes. The methodology proposed is denoted as the X-factor method. Design/methodology/approach The X-factor method was compared with the technique deferred-correction (DC) for the calculations of a two-dimensional cavity with absorting-emiting-scatteting gray media using the discrete ordinates method. Four parameters were considered to evaluate: the absorption coefficient, the emissivity of boundary surface, the scattering albedo and under-relaxation factor. Findings The results showed the central processing unit (CPU) time of X-factor method was lower than DC. The reductions of CPU time with the X-factor method were observed from 0.6 to 75.4%. Originality/value The superiority of the X-factor method over DC was showed with the reduction of CPU time of the numerical solution of RTE for evaluated cases.

An Accelerated 3D Navier-Stokes Solver for Flows in Turbomachines

2009 ◽  
Author(s):  
Tobias Brandvik ◽  
Graham Pullan
Keyword(s):  
Graphics Processing Units ◽  
Three Dimensional ◽  
Navier Stokes ◽  
Linear Scaling ◽  
Test Case ◽  
Processing Unit ◽  
Central Processing ◽  
Order Of Magnitude ◽  
Graphics Processing ◽  
Good Agreement

A new three-dimensional Navier-Stokes solver for flows in turbomachines has been developed. The new solver is based on the latest version of the Denton codes, but has been implemented to run on Graphics Processing Units (GPUs) instead of the traditional Central Processing Unit (CPU). The change in processor enables an order-of-magnitude reduction in run-time due to the higher performance of the GPU. Scaling results for a 16 node GPU cluster are also presented, showing almost linear scaling for typical turbomachinery cases. For validation purposes, a test case consisting of a three-stage turbine with complete hub and casing leakage paths is described. Good agreement is obtained with previously published experimental results. The simulation runs in less than 10 minutes on a cluster with four GPUs.

scholarly journals Microbial Community Analysis with Ribosomal Gene Fragments from Shotgun Metagenomes

2015 ◽  
Vol 82 (1) ◽  
pp. 157-166 ◽  
Author(s):  
Jiarong Guo ◽  
James R. Cole ◽  
Qingpeng Zhang ◽  
C. Titus Brown ◽  
James M. Tiedje
Keyword(s):  
Community Analysis ◽  
Rrna Genes ◽  
Metagenomic Data ◽  
Rrna Gene ◽  
Processing Unit ◽  
Metagenomic Sequencing ◽  
Ssu Rrna ◽  
Variable Regions ◽  
Content Type ◽  
Central Processing

ABSTRACTShotgun metagenomic sequencing does not depend on gene-targeted primers or PCR amplification; thus, it is not affected by primer bias or chimeras. However, searching rRNA genes from large shotgun Illumina data sets is computationally expensive, and no approach exists for unsupervised community analysis of small-subunit (SSU) rRNA gene fragments retrieved from shotgun data. We present a pipeline, SSUsearch, to achieve the faster identification of short-subunit rRNA gene fragments and enabled unsupervised community analysis with shotgun data. It also includes classification and copy number correction, and the output can be used by traditional amplicon analysis platforms. Shotgun metagenome data using this pipeline yielded higher diversity estimates than amplicon data but retained the grouping of samples in ordination analyses. We applied this pipeline to soil samples with paired shotgun and amplicon data and confirmed bias againstVerrucomicrobiain a commonly used V6-V8 primer set, as well as discovering likely bias againstActinobacteriaand forVerrucomicrobiain a commonly used V4 primer set. This pipeline can utilize all variable regions in SSU rRNA and also can be applied to large-subunit (LSU) rRNA genes for confirmation of community structure. The pipeline can scale to handle large amounts of soil metagenomic data (5 Gb memory and 5 central processing unit hours to process 38 Gb [1 lane] of trimmed Illumina HiSeq2500 data) and is freely available athttps://github.com/dib-lab/SSUsearchunder a BSD license.

scholarly journals Accelerating 3D Medical Image Segmentation by Adaptive Small-Scale Target Localization

2021 ◽  
Vol 7 (2) ◽  
pp. 35
Author(s):  
Boris Shirokikh ◽  
Alexey Shevtsov ◽  
Alexandra Dalechina ◽  
Egor Krivov ◽  
Valery Kostjuchenko ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Graphics Processing Units ◽  
Medical Image ◽  
State Of The Art ◽  
Three Dimensional ◽  
Medical Image Segmentation ◽  
Small Scale ◽  
Processing Unit ◽  
Central Processing ◽  
Lung Cancer Diagnosis

The prevailing approach for three-dimensional (3D) medical image segmentation is to use convolutional networks. Recently, deep learning methods have achieved human-level performance in several important applied problems, such as volumetry for lung-cancer diagnosis or delineation for radiation therapy planning. However, state-of-the-art architectures, such as U-Net and DeepMedic, are computationally heavy and require workstations accelerated with graphics processing units for fast inference. However, scarce research has been conducted concerning enabling fast central processing unit computations for such networks. Our paper fills this gap. We propose a new segmentation method with a human-like technique to segment a 3D study. First, we analyze the image at a small scale to identify areas of interest and then process only relevant feature-map patches. Our method not only reduces the inference time from 10 min to 15 s but also preserves state-of-the-art segmentation quality, as we illustrate in the set of experiments with two large datasets.

scholarly journals Highly Thermal Conductive and Electrical Insulating Epoxy Composites with a Three-Dimensional Filler Network by Sintering Silver Nanowires on Aluminum Nitride Surface

Polymers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 694
Author(s):  
Wondu Lee ◽  
Jooheon Kim
Keyword(s):  
Thermal Conductivity ◽  
Aluminum Nitride ◽  
Silver Nanowires ◽  
Three Dimensional ◽  
Silver Nanowire ◽  
Thermal Interface Material ◽  
Processing Unit ◽  
Central Processing ◽  
Conductive Composites ◽  
3D Network

In this study, a new fabrication technique for three-dimensional (3D) filler networks was employed for the first time to prepare thermally conductive composites. A silver nanowire (AgNW)– aluminum nitride (AlN) (AA) filler was produced by a polyol method and hot-pressed in mold to connect the adjacent fillers by sintering AgNWs on the AlN surface. The sintered AA filler formed a 3D network, which was subsequently impregnated with epoxy (EP) resin. The fabricated EP/AA 3D network composite exhibited a perpendicular direction thermal conductivity of 4.49 W m−1 K−1 at a filler content of 400 mg (49.86 vol.%) representing an enhancement of 1973% with respect to the thermal conductivity of neat EP (0.22 W m−1 K−1). Moreover, the EP/AA decreased the operating temperature of the central processing unit (CPU) from 86.2 to 64.6 °C as a thermal interface material (TIM). The thermal stability was enhanced by 27.28% (99 °C) and the composites showed insulating after EP infiltration owing to the good insulation properties of AlN and EP. Therefore, these fascinating thermal and insulating performances have a great potential for next generation heat management application.

A Hybrid Optimization Based on GPU Parallel Computing Method and its Application of Three Dimensional Large Eddy Simulations

2013 ◽  
Vol 275-277 ◽  
pp. 2589-2594 ◽  
Author(s):  
Yu Xuan Zhang ◽  
Song Ping Wu
Keyword(s):  
High Speed ◽  
Data Transfer ◽  
Three Dimensional ◽  
Processing Unit ◽  
Desktop Computer ◽  
Central Processing ◽  
Eddy Simulation ◽  
Turbulence Flow ◽  
Large Eddy ◽  
Optimization Schemes

Using compute Unified device architecture (CUDA), a traditional computational fluid dynamics (CFD) program is paralleled and optimized based on graphic processing unit (GPU). The calculation process is divided into two parts as serial and parallel. Their main characteristics are analyzed and different optimization schemes are given. CPU (central processing unit) and GPU work respectively as flow control and high-speed parallel computation. Bandwidth between devices is applied effectively. Data transfer between devices is moderately improved to simplify algorithm. Finally, the method is verified by simulating a three-dimensional isotropic homogeneous turbulence flow field. The calculation uses large eddy simulation (LES) method with secondary filter and solves the three-dimensional N-S equations. The maximum grid number achieves 8,000,000 and takes 33 seconds each step. All calculations are using ordinary single desktop computer, optimized acceleration ratio can reach 9.

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