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Numerical Solution of the Navier-Stokes Equations [PDF]

open access: bronze, 1968
A finite-difference method for solving the time-dependent Navier- Stokes equations for an incompressible fluid is introduced. This method uses the primitive variables, i.e. the velocities and the pressure, and is equally applicable to problems in two and
Alexandre J. Chorin
openalex   +2 more sources

Generalizations of incompressible and compressible Navier–Stokes equations to fractional time and multi-fractional space [PDF]

open access: yesScientific Reports, 2022
This study develops the governing equations of unsteady multi-dimensional incompressible and compressible flow in fractional time and multi-fractional space. When their fractional powers in time and in multi-fractional space are specified to unit integer
M. Levent Kavvas, Ali Ercan
doaj   +2 more sources

A Source Term Approach for Generation of One-way Acoustic Waves in the Euler and Navier-Stokes equations. [PDF]

open access: yesWave Motion, 2017
We derive a volumetric source term for the Euler and Navier-Stokes equations that mimics the generation of unidirectional acoustic waves from an arbitrary smooth surface in three-dimensional space. The model is constructed as a linear combination of monopole and dipole sources in the mass, momentum, and energy equations.
Maeda K, Colonius T.
europepmc   +7 more sources

Uniform Finite Element Error Estimates with Power-Type Asymptotic Constants for Unsteady Navier–Stokes Equations [PDF]

open access: yesEntropy, 2022
Uniform error estimates with power-type asymptotic constants of the finite element method for the unsteady Navier–Stokes equations are deduced in this paper.
Cong Xie, Kun Wang
doaj   +2 more sources

Energy equality in the isentropic compressible Navier-Stokes-Maxwell equations

open access: yesElectronic Research Archive, 2023
This paper concerns energy conservation for weak solutions of compressible Navier-Stokes-Maxwell equations. For the energy equality to hold, we provide sufficient conditions on the regularity of weak solutions, even for solutions that may include exist ...
Jie Zhang , Gaoli Huang, Fan Wu
doaj   +1 more source

Global Existence and Uniqueness of The Inviscid Velocity-Vorticity Model of The g-Navier-Stokes Equations

open access: yesSakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2022
In this paper, we prove the global existence and uniqueness of the weak solutions to the inviscid velocity-vorticity model of the g-Navier-Stokes equations.
Meryem Kaya, Özge Kazar
doaj   +1 more source

Analytical Solution to 1D Compressible Navier-Stokes Equations

open access: yesJournal of Function Spaces, 2021
There exist complex behavior of the solution to the 1D compressible Navier-Stokes equations in half space. We find an interesting phenomenon on the solution to 1D compressible isentropic Navier-Stokes equations with constant viscosity coefficient on x,t ...
Changsheng Dou, Zishu Zhao
doaj   +1 more source

Non-uniqueness of Leray solutions of the forced Navier-Stokes equations [PDF]

open access: yesAnnals of Mathematics, 2021
In the seminal work [39], Leray demonstrated the existence of global weak solutions to the Navier-Stokes equations in three dimensions. We exhibit two distinct Leray solutions with zero initial velocity and identical body force.
D. Albritton, Elia Bru'e, Maria Colombo
semanticscholar   +1 more source

An efficient semi-implicit immersed boundary method for the Navier–Stokes equations [PDF]

open access: yesJournal of Computational Physics, 2008
The immersed boundary method is one of the most useful computational methods in studying fluid structure interaction. On the other hand, the Immersed Boundary method is also known to require small time steps to maintain stability when solved with an explicit method. Many implicit or approximately implicit methods have been proposed in the literature to
Hou, T. Y., Shi, Z.
openaire   +4 more sources

Physics-informed neural networks for solving Reynolds-averaged Navier-Stokes equations [PDF]

open access: yesThe Physics of Fluids, 2021
Physics-informed neural networks (PINNs) are successful machine-learning methods for the solution and identification of partial differential equations (PDEs).
Hamidreza Eivazi   +3 more
semanticscholar   +1 more source

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