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ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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Input and output data from simulations of 2D valves and 3D inflow-outflow model using particle methods

Authors: Amaro Junior, Rubens Augusto; Sousa, Fabricio Simeoni;

Input and output data from simulations of 2D valves and 3D inflow-outflow model using particle methods

Abstract

Input and output data of open-source softwares for computational fluid dynamics simulation involving fluid-structure interaction. Data from two studies Verifications of the weakly-compressible smoothed particle hydrodynamics (WCSPH) method, open-source code SPHinXsys, when applied to the flow of idealized 2D valve models. Validations of inflow-outflow model in moving particle semi-implicit (MPS) method, open-source code PolyMPS. Folders and Files valve-2D.zip is the folder with data from the idealized models of vertical and curved 2D valves: Vertical valves with parameters provided in Gil et al., 2010 Curved valves with parameters provided in Wick, 2014 source files (.cpp): input data (physical and numerical parameters) for SPHinXsys text files: SPHinXsys (.dat) and Reference (.tsv) results python files (.py): Generates the graphics inflow-outflow-3D.zip is the folder with data from the inflow-outflow model in MPS: Fluid physical properties of water \(\rho=1000kg/m^3 , \,\, \nu=10^{-6}m/s^{-2}\) Pipes of length \(L=0.15m\): circular section of diameter \(D=0.1m\). square section of sides \(S=0.1m\). Constante pressure variation (\(\Delta P = 30 \,\, or \,\, 50 \,\, Pa\)) between inflow and outflow: \(\frac{\partial p}{\partial x} = - \frac{\Delta P}{L}, \\ \Delta P = P_{outflow} - P_{inflow}\) Sinusoidal pressure variation (\(\Delta P =700Pa \,\, , \,\, T = 2.0s\)) between inflow and outflow \(\frac{\partial p}{\partial x} = - \frac{\Delta P}{L} \sin \omega t \, \\ \omega = \frac{2\pi}{T} \\ Delta P = P_{outflow} - P_{inflow}\) input data (.json, .grid, .stl): physical properties, numerical parameters and geometries for PolyMPS can be found at https://github.com/rubensamarojr/polymps/tree/inOutflow/input text files (.txt): PolyMPS and OpenFOAM results python files (.py): Generates the graphics References A. J. Gil. The Immersed Structural Potential Method for haemodynamic applications. J. Comput. Phys., 229 (2010), pp. 8613-8641 T. Wick. Flapping and contact FSI computations with the fluid–solid interface-tracking/interface-capturing technique and mesh adaptivity. Comput Mech 53, 29–43 (2014) D. Kamensky, et al. An immersogeometric variational framework for fluid–structure interaction: Application to bioprosthetic heart valves Comput. Methods Appl. Mech. Engrg., 284 (2015), pp. 1005-1053 C. Kadapa et al. A fictitious domain/distributed Lagrange multiplier based fluid–structure interaction scheme with hierarchical B-Spline grids. Comput. Methods Appl. Mech. Engrg., 301 (2016), pp. 1-27 Jie Liu. A second-order changing-connectivity ALE scheme and its application to FSI with large convection of fluids and near contact of structures. J. Comput. Phys., 304 (2016), pp. 308-423

The authors are grateful for the financial support received from the São Paulo Research Foundation FAPESP/CEPID/CeMEAI (grant 2021/11429-4). The MPS and SPH simulations using the open-source codes PolyMPS and SPHinXsys, to which the authors are very grateful, were carried out using the computational resources of the Center for Mathematical Sciences Applied to Industry (CeMEAI) funded by FAPESP (grant 2013/07375-0).

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Keywords

particle methods, MPS, SPH, heart-valve, inflow-outflow

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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