Eulerian Based Interpolation Schemes for Flow Map Construction and Line Integral Computation with Applications to Lagrangian Coherent Structures Extraction

You, Guoqiao; Leung, Shingyu

doi:10.1007/s10915-017-0424-9

Cited by 13 publications

(6 citation statements)

References 40 publications

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“…In order to represent this accuracy well, the effectiveness of clustering needs to be analyzed. When evaluating clustering results, the optimal number of clusters is usually an important indicator of effectiveness [ 18 , 19 ].…”

Section: Methodsmentioning

confidence: 99%

Multidimensional Information Network Big Data Mining Algorithm Relying on Finite Element Analysis

2022

Computational Intelligence and Neuroscience

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In recent years, with the rapid development of the Internet, online social networks have been continuously integrated with traditional interpersonal networks and research on information dissemination in social networks has gradually increased. This article studies and analyzes the multidimensional information network big data mining algorithm based on the finite element analysis method. This paper firstly introduces the finite element analysis and calculation process, a finite element data mining simulation application software management system will integrate current data, calculation, and background data into one, then analyzes the data mining clustering algorithm, and conducts an experimental exploration of the influential node mining algorithm in complex networks. The experimental results show that the LIC algorithm is better than the CC algorithm, the DC algorithm, and the BC algorithm; its overall performance is improved by 30%, and the effect is better. The LIC algorithm can effectively and quickly determine the influential nodes, which is helpful for social network analysis.

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Section: Methodsmentioning

confidence: 99%

Multidimensional Information Network Big Data Mining Algorithm Relying on Finite Element Analysis

2022

Computational Intelligence and Neuroscience

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“…According to Equation (26), after mapping, the moment would evolve toward the next time under the Eulerian framework, and the Eulerian moment would be mapped to the parcel under the Lagrangian framework. There are many ways to realize mapping from Eulerian to Lagrangian framework, [33][34][35] and Equation (40) is used in this paper…”

Section: The Coupling Scheme Of Eulerian-lagrangian Methods and Qmommentioning

confidence: 99%

Coupling Eulerian‐Lagrangian method of air‐particle two‐phase flow with population balance equations to simulate the evolution of vehicle exhaust plume

et al. 2018

Numerical Methods in Fluids

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Summary In this paper, we present a new numerical scheme to describe the dynamic evolution of multiphase polydisperse systems in terms of time, space, and properties by coupling the Eulerian‐Lagrangian method for air‐particle two‐phase flow and population balance equations to describe particle property evolution due to microbehaviors (eg, aggregation, breakage, and growth). This coupling scheme was used to comprehensively simulate the two‐phase flow structure, particle size spectrum, particle number, and volume concentrations. These were characterized by a high‐resolution particle tracking using the Lagrangian approach and the high precision of moments of the particle size spectrum by solving the population balance equation with the quadrature method of moments. The algorithm of the coupling scheme was incorporated into the open source computational fluid dynamics software OpenFOAM to simulate the dynamic evolution of vehicle exhaust plume. The impacts of vehicle velocity, exhaust temperature, and aggregation efficiency on the distribution of auto exhaust particles in space and changes in their properties were analyzed. The results indicate that the particle number concentration, volume concentration, and average diameter of particles in the vehicle exhaust plume could be strongly affected by the plume structure and flow properties.

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“…We are not able to quantitatively compare our solution with any exact solution but can only qualitatively examine the effect of D 0 on our numerical solution. In Figure 17, we reproduce Figure 11 from [47] or Figure 7(a) from [46] showing the FTLE e 50 0 for the noiseless velocity, i.e. D 0 = 0.…”

Section: An Application To Real Datasetmentioning

confidence: 99%

“…Following the definition of Haller [13,15,16], one can see that the LCS is closely related to the ridges of the FTLE fields. In a series of recent studies [25,26,44,47,46,45,27,32], we have developed various Eulerian approaches to numerically compute the FTLE on a fixed Cartesian mesh. The idea is to combine the approach with the level set method [34,33] which allows the flow map to satisfy a Liouville equation.…”

Section: Introductionmentioning

confidence: 99%

Computing the finite time Lyapunov exponent for flows with uncertainties

You

Leung

2021

Journal of Computational Physics

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Eulerian Based Interpolation Schemes for Flow Map Construction and Line Integral Computation with Applications to Lagrangian Coherent Structures Extraction

Cited by 13 publications

References 40 publications

Multidimensional Information Network Big Data Mining Algorithm Relying on Finite Element Analysis

Multidimensional Information Network Big Data Mining Algorithm Relying on Finite Element Analysis

Coupling Eulerian‐Lagrangian method of air‐particle two‐phase flow with population balance equations to simulate the evolution of vehicle exhaust plume

Computing the finite time Lyapunov exponent for flows with uncertainties

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