Study on Kernel Functions of Pressure Oscillation in MPS Method

2017 ◽  
Author(s):  
Zhu Yue ◽  
Sun Chen ◽  
Jiang Shengyao ◽  
Yang Xingtuan ◽  
Duan Riqiang
Keyword(s):  
Pressure Distribution ◽  
Kernel Function ◽  
Compact Support ◽  
Pressure Oscillation ◽  
Kernel Functions ◽  
Implicit Method ◽  
Computational Time ◽  
Simulation Process ◽  
Moving Particle ◽  
Approach Zero

In this paper,we studied the characteristic of four kernel functions of Moving Particle Semi-implicit method (MPS).In order to find the dependence of pressure oscillation on kernel functions, the dam break problem was selected for as a benchmark.Results showed that proper value of a kernel function was required in the compact support region to smooth the pressure distribution. The value of the kernel function should approach zero when the particle distance was close to the edge of compact support region to avoid large replusive force.When the distance between particles was close to zero,the value of kernel function should be large enough to avoid particle clustering. At the same time,the kernel function should be simple to stabilize the simulation process and decrease the computational time.

scholarly journals Study on divergence approximation formula for pressure calculation in particle method

2018 ◽  
Vol 10 (4) ◽  
pp. 159-169
Author(s):  
Zhu Yue ◽  
Jiang Shengyao ◽  
Yang Xingtuan ◽  
Duan Riqiang
Keyword(s):  
Pressure Oscillation ◽  
Particle Method ◽  
Free Surface Flows ◽  
Implicit Method ◽  
Approximation Formula ◽  
Pressure Poisson Equation ◽  
Pressure Calculation ◽  
Approximation Formulas ◽  
Moving Particle ◽  
Dam Breaking

The moving particle semi-implicit method is a meshless particle method for incompressible fluid and has proven useful in a wide variety of engineering applications of free-surface flows. Despite its wide applicability, the moving particle semi-implicit method has the defects of spurious unphysical pressure oscillation. Three various divergence approximation formulas, including basic divergence approximation formula, difference divergence approximation formula, and symmetric divergence approximation formula are proposed in this paper. The proposed three divergence approximation formulas are then applied for discretization of source term in pressure Poisson equation. Two numerical tests, including hydrostatic pressure problem and dam-breaking problem, are carried out to assess the performance of different formulas in enhancing and stabilizing the pressure calculation. The results demonstrate that the pressure calculated by basic divergence approximation formula and difference divergence approximation formula fluctuates severely. However, application of symmetric divergence approximation formula can result in a more accurate and stabilized pressure.

An Improved Moving Particle Semi-Implicit Method for Free Surface Flows

2014 ◽  
Vol 571-572 ◽  
pp. 682-687
Author(s):  
Qiao Rui Wu ◽  
Xiong Liang Yao
Keyword(s):  
Free Surface ◽  
Hydrostatic Pressure ◽  
Large Deformation ◽  
Pressure Oscillation ◽  
Free Surface Flows ◽  
Dam Break ◽  
Implicit Method ◽  
Surface Flows ◽  
Collision Model ◽  
Moving Particle

The objective of this study is to make some improvements to the original Moving Particle Semi-implicit method (MPS) for free surface flows. Compared to traditional mesh methods, MPS is feasible to simulate surface flows with large deformation, however, during the simulation; the pressure oscillation is quite violent, duo to misjudgment of surface particles as well as particles gathering together. To modify this problem, a new arc method is applied to judge free surface particles, and a collision model is introduced to avoid particles from gathering together. Hydrostatic pressure and classical dam break are investigated by original and improved MPS. The results verify that improved MPS method is more effective for free surface flows.

scholarly journals LNG Tank Sloshing Simulation of Multidegree Motions Based on Modified 3D MPS Method

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Chunhui Wang ◽  
Chunyu Guo ◽  
Fenglei Han
Keyword(s):  
Free Surface ◽  
Weighted Average ◽  
Kernel Functions ◽  
Computational Stability ◽  
Mps Method ◽  
Fluid Sloshing ◽  
Particle Hydrodynamics ◽  
Moving Particle ◽  
Lng Tank ◽  
Stability And Accuracy

Modified 3D Moving Particle Semi-Implicit (MPS) method is used to complete the numerical simulation of the fluid sloshing in LNG tank under multidegree excitation motion, which is compared with the results of experiments and 2D calculations obtained by other scholars to verify the reliability. The cubic spline kernel functions used in Smoothed Particle Hydrodynamics (SPH) method are adopted to reduce the deviation caused by consecutive two times weighted average calculations; the boundary conditions and the determination of free surface particles are modified to improve the computational stability and accuracy of 3D calculation. The tank is under forced multidegree excitation motion to simulate the real conditions of LNG ships, the pressures and the free surfaces at different times are given to verify the accuracy of 3D simulation, and the free surface and the splashed particles can be simulated more exactly.

scholarly journals Scenario reduction methodologies under uncertainties for reservoir development purposes: distance-based clustering and metaheuristic algorithm

2021 ◽  
Author(s):  
Seyed Kourosh Mahjour ◽  
Antonio Alberto Souza Santos ◽  
Manuel Gomes Correia ◽  
Denis José Schiozer
Keyword(s):  
Flow Simulation ◽  
Reduction Process ◽  
Computational Time ◽  
Metaheuristic Algorithm ◽  
Dynamic Data ◽  
Scenario Reduction ◽  
Field Development ◽  
Data Usage ◽  
Simulation Process ◽  
Uncertainty Space

AbstractThe simulation process under uncertainty needs numerous reservoir models that can be very time-consuming. Hence, selecting representative models (RMs) that show the uncertainty space of the full ensemble is required. In this work, we compare two scenario reduction techniques: (1) Distance-based Clustering with Simple Matching Coefficient (DCSMC) applied before the simulation process using reservoir static data, and (2) metaheuristic algorithm (RMFinder technique) applied after the simulation process using reservoir dynamic data. We use these two methods as samples to investigate the effect of static and dynamic data usage on the accuracy and rate of the scenario reduction process focusing field development purposes. In this work, a synthetic benchmark case named UNISIM-II-D considering the flow unit modelling is used. The results showed both scenario reduction methods are reliable in selecting the RMs from a specific production strategy. However, the obtained RMs from a defined strategy using the DCSMC method can be applied to other strategies preserving the representativeness of the models, while the role of the strategy types to select the RMs using the metaheuristic method is substantial so that each strategy has its own set of RMs. Due to the field development workflow in which the metaheuristic algorithm is used, the number of required flow simulation models and the computational time are greater than the workflow in which the DCSMC method is applied. Hence, it can be concluded that static reservoir data usage on the scenario reduction process can be more reliable during the field development phase.

Integrity Assessment of a Free-Fall Lifeboat Launched From a FPSO

2015 ◽  
Author(s):  
Guomin Ji ◽  
Nabila Berchiche ◽  
Sébastien Fouques ◽  
Thomas Sauder ◽  
Svein-Arne Reinholdtsen
Keyword(s):  
Finite Element ◽  
Pressure Distribution ◽  
Structural Integrity ◽  
Dynamic Effect ◽  
Sensitivity Study ◽  
Computational Time ◽  
Load Factor ◽  
Time Varying ◽  
Integrity Assessment

The paper addresses the structural integrity assessment of lifeboat launched from floating production, storage and offloading (FPSO) vessels. The study is based on long-term drop lifeboat simulations accounting for more than 50 years of hindcast data of metocean conditions and corresponding FPSO motions. Selection of the load cases and strength analyses with high computational time is a challenge. The load cases analyzed are those corresponding to the 99th percentile of long term distribution of indicators for large slamming loads (CARXZ) or large submergence (Imaxsub). For six selected cases, the time-varying pressure distribution on the lifeboat hull during and after water impact is calculated by CFD simulations using StarCCM+. The finite element model (FEM) of the composite structure of the lifeboat is modelled by ABAQUS. Quasi-static finite element (FE) analyses are performed for the selected load cases. The structural integrity is assessed by the maximum stress and Tsai-Wu failure measure. In the present study, the load and resistance factors are combined and applied to the response. A sensitivity study is performed to investigate the non-linear load/response effects when the load factor is applied to the load. In addition, dynamic analysis is performed with the time-varying pressure distribution for selected case and the dynamic effect is investigated.

scholarly journals A Kernel-Based Approach for Biomedical Named Entity Recognition

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Rakesh Patra ◽  
Sujan Kumar Saha
Keyword(s):  
Kernel Function ◽  
Text Processing ◽  
Named Entity Recognition ◽  
Kernel Functions ◽  
Entity Recognition ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Named Entity ◽  
Tree Kernel

Support vector machine (SVM) is one of the popular machine learning techniques used in various text processing tasks including named entity recognition (NER). The performance of the SVM classifier largely depends on the appropriateness of the kernel function. In the last few years a number of task-specific kernel functions have been proposed and used in various text processing tasks, for example, string kernel, graph kernel, tree kernel and so on. So far very few efforts have been devoted to the development of NER task specific kernel. In the literature we found that the tree kernel has been used in NER task only for entity boundary detection or reannotation. The conventional tree kernel is unable to execute the complete NER task on its own. In this paper we have proposed a kernel function, motivated by the tree kernel, which is able to perform the complete NER task. To examine the effectiveness of the proposed kernel, we have applied the kernel function on the openly available JNLPBA 2004 data. Our kernel executes the complete NER task and achieves reasonable accuracy.

scholarly journals A COMPARISON STUDY OF DIFFERENT KERNEL FUNCTIONS FOR SVM-BASED CLASSIFICATION OF MULTI-TEMPORAL POLARIMETRY SAR DATA

2014 ◽  
Vol XL-2/W3 ◽  
pp. 281-285 ◽  
Author(s):  
B. Yekkehkhany ◽  
A. Safari ◽  
S. Homayouni ◽  
M. Hasanlou
Keyword(s):  
Kernel Function ◽  
Temporal Integration ◽  
Experimental Tests ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Rbf Kernel ◽  
Multi Temporal ◽  
Dimension Space

In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). <br><br> The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

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