Ozen Engineering, Inc. - Ansys Elite Channel Partner’s Post

💦Today we bring another simulation of particle-based approaches: CFD vs. DEM. Both CFD and DEM offer valuable contributions to the fully Lagrangian approach to multiphase flows, While CFD provides insights into fluid dynamics at a macroscopic level, DEM offers a detailed understanding of particle behavior and interactions. Combining these approaches allows for comprehensive simulations of complex multiphase systems. 🌊The computational efficiency of CFD and DEM simulations on CUDA depends on various factors, including the number of particles, the complexity of particle-fluid interactions, and the specific CUDA implementation. In general, DEM simulations tend to be more computationally intensive due to the explicit modeling of particle interactions, making them challenging for large-scale simulations. 🌑The choice between CFD and DEM on CUDA depends on the specific application requirements, the scale and complexity of the system being simulated, and the computational resources available. Leveraging CUDA for parallel computing can significantly enhance the performance of particle-based simulations. 🔗 https://lnkd.in/g7NK225S 📩 For more information on our CFD software contact us at support@metariver.kr or visit metariver.kr   #cfd #particles #lagrangian #mps #dem #simulation #particle #cuda #cae #flow #computationalfluiddynamics #metariver #fluidmechanics #fluiddynamics #multiphysics #sph #simulationsoftware #fluidflow #couplings #cfddem #CFDEMcoupling #simulationmodeloftheday #aerodynamics #software #samadii #samadiifluid #메타리버테크놀러지 #시뮬레이션 #사마디

Setting up a Computational Fluid Dynamics (CFD) simulation for your problem requires attention to details and careful consideration of several factors to ensure accurate and meaningful results. In this post, I will remind you with the things you must be careful about when using CFD, whether or not it’s open-source, commercial or in-house code. https://lnkd.in/gCeuybfA

Great insights shared in this article about the key steps for setting up a CFD simulation, summarized as: understanding the physics behind the problem, ensuring clear geometry, having a robust mesh, setting proper boundary conditions, and documenting everything. #CFD #physics #meshing #Makhbar

Firearms suppressor simulation. Traditionally difficult to predict the performance until you've machined, build and take it to the firing range. Here is a short blog on how we can quickly simulate this in Ansys CFD! #ansys #firearms #CFD https://lnkd.in/gaJz4wf2

⚡ 2D Navier Stokes Simulation ⚡ - - - - - - - - - - - - - - - - - - - - - - The video shows the result of a numerical simulation of a compressible air flow around the silhouettes of four different geometries, i.e. a disk, a droplet shape, a rectangle, and the Stanford bunny. The heatmap plot illustrates the speed at the current location while the streamlines and tracer particles visualize the flow direction. The time scale was slowed down by roughly a factor of three. The simulation was done using a pseudo-spectral approach. The no-slip boundary was implemented using the method of volume penalisation. Introduction to CFD & Navier-Stokes Equations: https://lnkd.in/dTcAvdU Source: https://lnkd.in/dCRBGqN #engineeredmind #engineering #cfd #simulation #turbulence #fluidflow

The Reynold-Averaged Turbulence Modelling chapter of our book, Notes on CFD: General Principles, helps to configure turbulence models in #CFD simulations with #OpenFOAM https://cfd.tips/1700

Steady-state CFD simulation was a paradigm in 1980-1990s for fluidization research, but mainly constrained into fully developed flows without axial development. The past decades have seen a rapid growth of transient CFD simulation, whereas steady-state simulation almost disappears due to its limitation in predicting axial development of fluidization. This work is to revisit/revive the steady-state CFD simulation of fluidized beds with a careful multiscale modeling, thus enabling a fast solution to predicting steady-state flow states in industrial practice. The S-shaped, axial profile of void fraction and the “choking” flow regime transition can be well predicted using our steady EMMS drag modeling. For details, please refer to the personalized Share Link: https://lnkd.in/grmXDKe3

🌊💦Combining Computational Fluid Dynamics (CFD) with Discrete Element Method (DEM) in a two-way coupling particularly accelerated with CUDA (Compute Unified Device Architecture) for parallel computing, offers a powerful approach for simulating debris sedimentation. 💦🌑🟤This simulation allows the modeling of both the fluid flow and the motion of individual debris particles, capturing their interactions and mutual influence. Check this CFD+DEM 2-Way Coupling analysis👉 https://lnkd.in/dtBwc7eJ 📩 For more information on our CFD software contact us at support@metariver.kr or visit metariver.kr   #cfd #particles #lagrangian #mps #dem #simulation #particle #cuda #cae #flow #computationalfluiddynamics #metariver #fluidmechanics #fluiddynamics #multiphysics #sph #simulationsoftware #fluidflow #couplings #cfddem #CFDEMcoupling #debris #debrisremoval #simulationmodeloftheday #aerodynamics #software #메타리버테크놀러지 #시뮬레이션 #사마디

The Reynold-Averaged Turbulence Modelling chapter of our book, Notes on CFD: General Principles, helps to configure turbulence models in #CFD simulations with #OpenFOAM https://cfd.tips/1700

Discover how we treat dimensionality issues in complex CFD simulations: https://lnkd.in/dyjDmNdJ Read more on application of this technology to real world problems: https://lnkd.in/dSxJRGJ2 https://lnkd.in/d82rKi9R