One way to go about answering these questions involves performing experiments in the lab with actual fluids and studying properties of their flow using various imaging instruments. Can we understand anything about the fluid flow in these cases? Can we predict how a fluid would move under certain conditions? And more importantly, can we prevent future coffee splashes?
Your first thought after looking at the coffee on your ruined keyboard may not be “How interesting!” but on the other hand, the serene yet dynamic nature of the flow of water from a cliff can certainly evoke a sense of wonder. Introductionįluid flow can be observed in many natural phenomena, from a soothing waterfall to an exasperating coffee splash on your computer keyboard. If you are already familiar with the theory and mathematics behind fluid mechanics and want to go through the code, you can skip to section 5 of this article. Lorena Barba and “A guide to writing your first CFD solver” by Prof. If you find it helpful in your work, you can cite it using the format proposed in Zenodo.This code has been written with the help of two incredibly informative references - “12 Steps to Navier Stokes” by Prof. Vortex-induced vibrations: a soft coral feeding strategy? arXiv. Direct interception or inertial impaction? A theoretical derivation of the efficiency power law for a simple and practical definition of capture modes. On the Mechanics of Flow-Induced Vibration of Soft Corals and Particle Interception. There is the possibility to save an image sequence of the simulations, then convert it to either video or an animated GIF image. Animationsĭepending on the simulation case, the animation-scripts show the animation of advected particles, pathlines, or frontlines. We also provide scripts that calculate pathlines and frontlines of the flow in pathline.py and frontline.py.
Couette, Poiseuille, inviscid flow around cylinder, etc.) Pathlines and frontlines In msh_process.py, you can create your own mesh with Gmsh, and assign to each node the solution of a classical flow (e.g. I included in my thesis an appendix that explains the method I used to detect particles hitting the wall. The implementation of this criterion is general of bodies of any shape. We adopted the solid contact as a capture criterion: a particle is captured as soon as it touches the wall of the body. The calculation method is based on dichotomy, and explained in chapter 4 in my thesis. The file automatic_search_scripts.py contains all necessary function to calculate the capture rate, which can be executed from automatic_search.py. We would like to mention that this code uses the neighbour-to-neighbour particle tracer algorithm proposed by Löhner and Ambrosiano (1990), and implemented in element_search.py. In fact, this code integrates the trajectory of a single particle in the flow. There is no interaction between particles. The integer factor is a refinement factor that refines the timeline, so that the integration time step gets smaller. Particle: contains the particle characteristics (diameter, density, launch time, etc.)Īfter loading a flow and creating a particle, you can compute the advection trajectory simply with pute_trajectory(flow, factor).Notice that this code works only for triangular elements. Flow: contains the flow solution (timeline, mesh, velocities and pressures at nodes, geometry of bodies, etc.).PARADVECT works on Python (version 3.8.2) with NumPy (version 1.17.4), SciPy (version 1.4.1), and Matplotlib (version 3.1.2) modules. If a body is present in the flow, it can also calculate the rate at which particles are captured. PARADVECT (PARticle ADVECTion) is a Python code that computes trajectories of particles advected by a flow. Examples of particles advected by a flow around a vibrating cylinder.
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