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11 results for “Navier--Stokes”
Some Similarities and Differences between the Observed Alfvénic Fluctuations in the Fast Solar Wind and Navier-Stokes Turbulence
<p>Three text data sets are uploaded: wind tunnel data (modane1.txt), solar-wind magnetic-field data (Flat1maginterp.txt) and solar-wind velocity data (Flat15interp3DP.txt)</p>
Convolutional Neural Networks for LPV-Approximations of Semi-discrete Navier-Stokes Equations
<pre><code>A `python` module with * a dynamic setup of *Convolutional Neural Networks* in `PyTorch` * an interface to `FEniCS` to generate data from FEM simulations of flows and * a numerical realization of FEM norm in the training neural networks developed to design very low-dimensional LPV approximations of incompressible Navier-Stokes equations.</code></pre> <p> </p> <p><code>These files contain the core module </code>and the scripts that produce the numerical examples of the paper with <a href="https://doi.org/10.3389/fams.2022.879140">doi:10.3389/fams.2022.879140</a></p> <p> </p> <pre><code>> Benner, Heiland, Bahmani (2022): *Convolutional Neural Networks for Very Low-dimensional LPV Approximations of Incompressible Navier-Stokes Equations* </code></pre> <p> </p>
A linearized Navier-Stokes input output model
<p>Matrices for the experiments reported in DOI:10.1137/140980016</p>
Riccati-Based Feedback Stabilized Navier-Stokes Flow
Closed-loop simulation of Navier-Stokes flow within "von Kármán vortex street". The vertical component of the velocity field is illustrated. The top video shows the velocity field without any feedback. The middle video shows the influence of the initial feedback, compute via a Bernoulli equation. The bottom video shows the stabilizing influence of the Riccati-based feedback for $\lambda=2$.
Final Version of the Data associated to the publication "A Benchmark for Finite Prandtl Number Convection: Comparison of Boltzmann and Navier-Stokes Solutions"
<p>Final Version of V_rms and Nu for the three codes, GAIA, StreamV, TLBM, used for a benchmark manuscript titled "A Benchmark for Finite Prandtl Number Convection: Comparison of Boltzmann and Navier-Stokes Solutions", Authors: Gabriele Morra, Peter Mora, Christian Huttig, Nicola Tosi, Henri Samuel, David A. Yuen</p> <p>The Zip file contains both npy and out versions of V_rms and Nu. In the out files, time is embedded in each file. In the "npy" version, time is a separate npy file. In this final version, pdf figures for each case are also embedded.</p>
Navier-Stokes Dataset for Paper "Variable-Input DeepONets for Operator Learning"
<p>This repository contains all datasets required to reproduce the Navier-Stokes tests presented in the paper "Variable-Input DeepONets for Operator Learning".</p>
Navier-Stokes problem for distributed memory AMGCL benchmarks
<p>The system matrix and the right hand side used in the distributed Navier-Stokes benchmark at https://github.com/ddemidov/amgcl_benchmarks.</p> <p>This is an incompressible Navier-Stokes problem discretized on a non uniform 3D mesh with a finite element method, using an equal-order tetrahedral Finite Elements, stabilized employing an ASGS-type (algebraic subgrid-scale) approach. This results in a discretized linear system of equations with a 4x4 block structure. The overall system matrix for the problem was assembled in the [Kratos](http://www.cimne.com/kratos/) multi-physics package developed in CIMNE, Barcelona. The matrix contains 4'773'588 unknowns and 281'089'456 nonzeros.</p>
Stochastic Navier-Stokes dataset for probablisitic forecasting
<p>This is the Stochastic Navier-Stokes dataset generated and used in our recent paper: <a href="https://arxiv.org/abs/2403.13724">https://arxiv.org/abs/2403.13724</a>. </p>
Navier-Stokes problem for shared memory AMGCL benchmarks
<p>The system matrix and the right hand side used in the shared memory Navier-Stokes benchmark at https://github.com/ddemidov/amgcl_benchmarks.</p> <p>This is an incompressible Navier-Stokes problem discretized on a non uniform 3D mesh with a finite element method. The overall system matrix for the problem was assembled in the [Kratos](http://www.cimne.com/kratos/) multi-physics package developed in CIMNE, Barcelona. The matrix has block structure with blocks of 4-by-4 elements, and contains 713'456 unknowns and 41'277'920 nonzeros.</p>
Files for "On exactly incompressible DG FEM pressure splitting schemes for the Navier-Stokes equation"
<p>Input files and scripts sufficient to reproduce the results shown in the paper "On exactly incompressible DG FEM pressure splitting schemes for the Navier-Stokes equation".</p> <p>A version of Ocellaris similar to version 2019.0.1 was used and all results should be reproducible by this version of Ocellaris. See <a href="https://bitbucket.org/ocellarisproject/ocellaris/">the Ocellaris source code repository</a>, <a href="https://pypi.org/project/ocellaris/2019.0.1/">Ocellaris 2019.0.1 on PyPi</a>, or DOI <a href="https://zenodo.org/record/2558303">10.5281/zenodo.2558303</a>.</p> <p>The Ocellaris user guide can be found at <a href="https://www.ocellaris.org">www.ocellaris.org</a> and it includes guides to how to run Ocellaris in Singularity or Docker containers for ease of installation and testing. Containers for Ocellaris 2019.0.0 are available, for Docker run:</p> <pre><code class="language-bash"># On the host machine: docker run -it trlandet/fenics-dev:2018.1.0.r3 # Inside the Docker container pip3 install ocellaris==2019.0.1 --user </code></pre> <p> </p>
Navier-Stokes Extension Equation
<p>The Navier-Stokes Extension Equation enhances the classical Navier-Stokes equations by incorporating explicit smoothness conditions, , ensuring no physical singularities and guaranteeing stable solutions for real-world flow problems. The fundamental equation is:</p> <p> </p> <p><strong>\rho \frac{\partial u}{\partial t} + \rho (u \cdot \nabla) u = - \nabla p + \mu \Delta u</strong></p> <p> </p> <p>This describes the dynamics of incompressible fluids, addressing pressure gradients, viscosity, and mass conservation (). It is applicable in fields such as meteorology, astrophysics, and biomechanics, providing stable and precise flow simulations.</p>
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