scholarly journals A Scheme of Computational Time Reduction on Back-End Server Using Computational Grid

2012 ◽  
Vol 16 (12) ◽  
pp. 2695-2701
Author(s):  
Seong-Pyo Hong ◽  
Seung-Jo Han
Keyword(s):  
Computational Grid ◽  
Computational Time ◽  
Time Reduction

scholarly journals Improvement of anisotropic porosity models with a merging technique

2018 ◽  
Vol 40 ◽  
pp. 06023
Author(s):  
Martin Bruwier ◽  
Pierre Archambeau ◽  
Sébastien Erpicum ◽  
Michel Pirotton ◽  
Benjamin Dewals
Keyword(s):  
Shallow Water ◽  
Flow Characteristics ◽  
Threshold Value ◽  
Computational Grid ◽  
Computational Time ◽  
Time Step ◽  
Shallow Water Models ◽  
The Cost ◽  
The Impact ◽  
Porosity Model

Anisotropic porosity shallow-water models are used to take into account detailed topographic information through porosity parameters multiplying the various terms of the shallow-water equations. A storage porosity is assigned to each cell to reflect the void fraction in the cell and a conveyance porosity is used at each edge to reproduce the impact of subgrid obstacles on the flux terms. To guaranty the numerical stability, the time step depends on the value of the porosity parameters. This may hamper severely the computational efficiency in the presence of cells with low values of storage porosity. Cartesian grids are particularly sensitive to such a case since the meshing stems directly from the choice of the grid size. In this paper, this problem is addressed by using an original merging technique consisting in merging cells with a storage porosity lower than a threshold value with neighbouring cells. The model was tested for modelling a prismatic channel with different orientations between the Cartesian computational grid and the channel direction. The results show that the standard anisotropic porosity model (without merging) improves the reproduction of the flow characteristics; but at the cost of a significantly higher computational time. In contrast, the computational time is drastically reduced and the accuracy preserved when the merging technique is used with the porosity model.

A Study on Computational Time Reduction of Road Obstacle Detection by Parallel Image Processor

2014 ◽  
Vol 18 (5) ◽  
pp. 849-855 ◽  
Author(s):  
Yutaro Okamoto ◽  
◽  
Chinthaka Premachandra ◽  
Kiyotaka Kato
Keyword(s):  
Detection Algorithm ◽  
Obstacle Detection ◽  
Transport Systems ◽  
Computational Time ◽  
Intelligent Transport Systems ◽  
Image Processor ◽  
Intelligent Transport ◽  
The Road ◽  
Parallel Image ◽  
Time Reduction

Automatic road obstacle detection is one of the significant problem in Intelligent Transport Systems (ITS). Many studies have been conducted for this interesting problem by using on-vehicle cameras. However, those methods still needs a dozens ofmillisecondsfor image processing. To develop the quick obstacle avoidance devices for vehicles, further computational time reduction is expected. Furthermore, regarding the applications, compact hardware is also expected for implementation. Thus, we study on computational time reduction of the road obstacle detection by using a small-type parallel image processor. Here, computational time is reduced by developing an obstacle detection algorithm which is appropriated to parallel processing concept of that hardware. According to the experimental evaluation of the new proposal, we could limit computational time for eleven milliseconds with a good obstacle detection performance.

423 Finite Element Modeling for Computational Time Reduction in Brain Shift Analysis

2009 ◽  
Vol 2009 (0) ◽  
pp. _423-1_-_423-5_
Author(s):  
Youhei AZUMA ◽  
Kazuhiko ADACHI ◽  
Yu HASEGAWA ◽  
Atsushi FUJITA ◽  
Eiji KOHMURA
Keyword(s):  
Finite Element ◽  
Finite Element Modeling ◽  
Computational Time ◽  
Brain Shift ◽  
Element Modeling ◽  
Shift Analysis ◽  
Time Reduction

scholarly journals Computational-Time Reduction of Fourier-Based Analytical Models

2018 ◽  
Vol 33 (1) ◽  
pp. 281-289 ◽  
Author(s):  
Bert Hannon ◽  
Peter Sergeant ◽  
Luc Dupre
Keyword(s):  
Analytical Models ◽  
Computational Time ◽  
Time Reduction

Model- and Full-Scale RANS Simulations for a Drift Tanker

2017 ◽  
Vol Vol 159 (A2) ◽  
Author(s):  
J Yao
Keyword(s):  
Scale Effect ◽  
Eddy Viscosity ◽  
Grid Point ◽  
Computational Grid ◽  
Measured Data ◽  
Full Scale ◽  
Computational Time ◽  
Wall Function ◽  
Ship Hydrodynamics ◽  
Sst Model

The flow around a full scale (FS) ship can be simulated by mean s of Reynolds Averaged Naiver Stokes (RANS) method, which provides a way to obtain more knowledge about scale effect s on ship hydrodynamics. In this work, the viscous flow around a static drift tanker in full scale is simulated by using the RANS solver based on the open source platform OpenFOAM. The 𝑘−𝜔 SST model is employed to approximate the eddy viscosity. To reduce computational time, wall function approach is applied for the FS simulation. The flow around the ship in model scale is simulated as well, but without using any wall function, i.e. using Low Reynolds number mode. In order to verify the computations, de-tailed studies on the computational grid including investigation of the sensitivity of computed forces to 𝑦+ (dimension-less distance of first grid point to wall) and grid dependency study are carried out. The computed forces are compared with available measured data The scale effect s are analysed and discussed by comparisons.

scholarly journals Computational time reduction for sequential batch solutions in GNSS precise point positioning technique

2017 ◽  
Vol 105 ◽  
pp. 34-42
Author(s):  
Angel Martín Furones ◽  
Ana Belén Anquela Julián ◽  
Alejandro Dimas-Pages ◽  
Fernando Cos-Gayón
Keyword(s):  
Precise Point Positioning ◽  
Computational Time ◽  
Positioning Technique ◽  
Time Reduction ◽  
Point Positioning
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