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We investigate the purely spatial Lagrangian coordinate transformation from the Lagrangian to the basic Eulerian frame. We demonstrate three techniques for extracting the relativistic displacement field from a given solution in the…

广义相对论与量子宇宙学 · 物理学 2014-12-16 Cornelius Rampf , Alexander Wiegand

We introduce Lagrange2D, a Mathematica package for analysis and characterization of complex fluid flows using Lagrangian transport metrics. Lagrange2D includes built-in functions for integrating ensembles of trajectories subject to…

流体动力学 · 物理学 2019-08-05 William Gilpin

Dynamic mode decomposition (DMD) is a popular approach to analyzing and modeling fluid flows. In practice, datasets are almost always corrupted to some degree by noise. The vanilla DMD is highly noise-sensitive, which is why many…

流体动力学 · 物理学 2025-01-30 Andre Weiner , Janis Geise

Measuring sediment transport in riverbeds has long been a challenging research problem in geomorphology and river engineering. Traditional approaches rely on direct measurements using sediment samplers. Although such measurements are often…

系统与控制 · 电气工程与系统科学 2026-03-31 Shakib Mustavee , Arvind Singh , Shaurya Agarwal

We present an arbitrary updated Lagrangian Material Point Method (A-ULMPM) to alleviate issues, such as the cell-crossing instability and numerical fracture, that plague state of the art Eulerian formulations of MPM, while still allowing…

图形学 · 计算机科学 2021-08-03 Haozhe Su , Tao Xue , Chengguizi Han , Mridul Aanjaneya

We present a Lagrangian-Eulerian scheme to solve the shallow water equations in the case of spatially variable bottom geometry. Using a local curvilinear reference system anchored on the bottom surface, we develop an effective first-order…

数值分析 · 数学 2022-09-09 Eduardo Abreu , Elena Bachini , John Perez , Mario Putti

In this paper we present a conservative cell-centered Lagrangian finite volume scheme for the solution of the hyper-elasticity equations on unstructured multidimensional grids. The starting point of the new method is the Eucclhyd scheme,…

数值分析 · 数学 2021-04-07 Walter Boscheri , Raphaël Loubère , Pierre-Henri Maire

Lagrangian data assimilation of complex nonlinear turbulent flows is an important but computationally challenging topic. In this article, an efficient data-driven statistically accurate reduced-order modeling algorithm is developed that…

大气与海洋物理 · 物理学 2022-03-02 Nan Chen , Shubin Fu , Georgy E Manucharyan

Data-driven dimensionality reduction methods such as proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) have proven to be useful for exploring complex phenomena within fluid dynamics and beyond. A well-known…

流体动力学 · 物理学 2022-12-27 Elena Marensi , Gökhan Yalnız , Björn Hof , Nazmi Burak Budanur

Aerodynamic loads play a central role in many fluid dynamics applications, and we present a method for identifying the structures (or modes) in a flow that make dominant contributions to the time-varying aerodynamic loads in a flow. The…

流体动力学 · 物理学 2025-03-05 Suryansh Prakhar , Jung-Hee Seo , Rajat Mittal

Many physical problems can be modelled by partial differential equations on unknown domains. Several examples can easily be found in the dynamics of free interfaces in fluid dynamics, solid mechanics or in fluid-solid interactions. To solve…

数值分析 · 数学 2018-10-25 Javier Rivero-Rodriguez , Miguel Perez-Saborid , Benoit Scheid

In probability density function (PDF) methods of turbulent flows, the joint PDF of several flow variables is computed by numerically integrating a system of stochastic differential equations for Lagrangian particles. A mathematically exact…

流体动力学 · 物理学 2010-06-17 J. Bakosi

Most researches on fluid dynamics are mostly dedicated to obtain the solutions of Navier-Stokes equation which governs fluid flow with particular boundary conditions and approximations. We propose an alternative approach to deal with fluid…

流体动力学 · 物理学 2007-05-23 A. Sulaiman , L. T. Handoko

Lagrangian multiform theory is a variational framework for integrable systems. In this article we introduce a new formulation which is based on symplectic geometry and which treats position, momentum and time coordinates of a…

数学物理 · 物理学 2025-04-01 Vincent Caudrelier , Derek Harland

A phase proper orthogonal decomposition (Phase POD) method is demonstrated, utilizing phase averaging for the decomposition of spatio-temporal behaviour of statistically non-stationary turbulent flows in an optimized manner. The proposed…

流体动力学 · 物理学 2024-03-01 Yisheng Zhang , Azur Hodzic , Fabien Evrard , Berend Van Wachem , Clara M. Velte

Data-driven modal decompositions are useful tools for compressing data or identifying dominant structures. Popular ones like the dynamic mode decomposition (DMD) and the proper orthogonal decomposition (POD) are defined with continuous…

流体动力学 · 物理学 2025-11-06 Manuel Ratz , Alessandro Parente , Miguel Alfonso Mendez

A novel method for complex fluid-structure interaction (FSI) involving large structural deformation and motion is proposed. The new approach is based on a hybrid fluid formulation that combines the advantages of purely Eulerian (fixed-grid)…

计算工程、金融与科学 · 计算机科学 2018-08-02 Benedikt Schott , Christoph Ager , Wolfgang A. Wall

In this paper, we consider the mean curvature flow of entire Lagrangian graphs with initial data in the pseudo-Euclidean space, which is related to the special Lagrangian parabolic equation. We show that the parabolic equation \eqref{11}…

微分几何 · 数学 2024-10-24 Shanshan Li , Jiaru Lv , Rongli Huang

A study of the relationship between Lagrangian statistics and flow topology in fluid turbulence is presented. The topology is characterized using the Weiss criterion that provides a simplified tool to partition the flow into topologically…

流体动力学 · 物理学 2015-05-19 B. Kadoch , D. del-Castillo-Negrete , W. J. T. Bos , K. Schneider

Lagrangian stochastic methods are widely used to model turbulent flows. Scarce consideration has, however, been devoted to the treatment of the near-wall region and to the formulation of a proper wall-boundary condition. With respect to…

流体动力学 · 物理学 2024-02-07 Guilhem Balvet , Jean-Pierre Minier , Yelva Roustan , Martin Ferrand