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The Lagrangian approach in CFD offers a compelling alternative for specific problems, particularly those involving discrete phases or complex particle interactions. However, it's crucial to acknowledge the computational cost associated with tracking a large number of particles. Additionally, limitations arise in handling phenomena heavily reliant on the continuous nature of the fluid, like strong shear flows. Here, particle distortion (elements stretching into unrealistic shapes) can become a significant issue. Despite these challenges, ongoing research in addressing these limitations and incorporating Lagrangian methods into advanced frameworks like DEM (Discrete Element Method) holds great promise for future CFD applications.

Dive Deeper: Unveiling the Lagrangian Approach in Modern CFD Software Ever feel limited by the Eulerian approach in CFD? The Lagrangian method offers a powerful alternative for specific scenarios, granting deeper insights into fluid motion. Beyond the Mesh: While the Eulerian approach tracks fluid properties at fixed points in a mesh, Lagrangian CFD follows individual particles or fluid elements as they traverse the domain. Imagine dye tracing a river's current – that's the Lagrangian spirit! Unlocking Hidden Potential: ▪️Particle Tracking: Lagrangian shines in multiphase flows (think oil droplets in water) by meticulously tracking the motion of particles, bubbles, or even droplets. Analyze complex interactions and dispersion patterns with unparalleled precision. ▪️Free Surface Mastery: Simulating free surfaces like waves or liquid sloshing in a tank becomes effortless. Lagrangian methods handle these interfaces seamlessly, eliminating the computational burden of constantly deforming the mesh. ▪️Beyond the Surface: Lagrangian data provides valuable insights into local phenomena like heat transfer or chemical reactions experienced by individual particles as they travel through the flow field. Choosing the Right Tool: The Lagrangian approach isn't a silver bullet. Here's where your CFD expertise comes in: ▪️Complexity Matters: For problems with large deformations or intricate geometries, the mesh-independence of Lagrangian methods can be a double-edged sword. Carefully consider the trade-offs between computational cost and accuracy. ▪️Interphase Intricacies: While Lagrangian excels at tracking particles within a single phase, complex interactions between different fluids (think turbulence at a gas-liquid interface) might require a combined Eulerian-Lagrangian approach. Modern CFD software leverages the strengths of both Eulerian and Lagrangian methods. By understanding their nuances, you can unlock a new level of control and analysis in your CFD simulations. Ready to explore the Lagrangian world? Share your experiences and CFD challenges in the comments below! Figure source: https://lnkd.in/gKakibkg #CFD #Lagrangian #ParticleTracking #FreeSurfaceSimulation #MultiphaseFlows #Engineering #engineering #science #innovation #TurbulenceModeling #Engineering #Simulation #fluids #linkedinlearning #cfdcommunity #NavierStokes #ScienceInAction #physics #FluidDynamics #RANS #ComputationalScience #mechanicalengineering #mechanicalengineer #mechanical #fluidmechanics #fluidflow #computationalfluiddynamics

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

🌉 Golden Gate Bridge - DDES vs. URANS CFD Simulation in RWIND 3 of Dlubal Software! 💡Turbulence modeling plays a crucial role in computational fluid dynamics (CFD) by predicting the behavior of turbulent flows. These models are vital for designing efficient and safe engineering applications, such as analyzing wind-structure interactions for structural design. 👉Three widely used turbulence modeling approaches include Reynolds-Averaged Navier-Stokes (RANS), Unsteady Reynolds-Averaged Navier-Stokes (URANS), and Delayed Detached Eddy Simulation (DDES). ✅RANS (Reynolds-Averaged Navier-Stokes) 👉RANS simplifies the Navier-Stokes equations by averaging them over time, smoothing out turbulence fluctuations, and providing a steady-state solution. ✅URANS (Unsteady Reynolds-Averaged Navier-Stokes) 👉URANS builds on RANS by accounting for time-dependent changes in the flow, capturing unsteady phenomena more effectively. It still uses Reynolds averaging but allows for more time-dependent variations than RANS. ✅DDES (Delayed Detached Eddy Simulation) 👉DDES is a hybrid method that combines the efficiency of RANS with the accuracy of Large Eddy Simulation (LES). In attached boundary layer regions, DDES operates like a RANS model, optimizing computational efficiency. In areas where the flow detaches, and larger turbulent structures emerge, DDES switches to LES mode, allowing for more precise resolution of these structures. 👉This method is particularly valuable for simulating complex flows involving separation, reattachment, and wake regions, such as those around building edges and corners. 👉DDES balances computational cost and accuracy, making it especially useful for high Reynolds number flows with significant unsteady and separated regions. Thanks to Mahyar from the Dlubal team for this brilliant simulation. ℹ Get the 90-day Free trial of RWIND 3 Wind Simulation here: https://lnkd.in/dqvH5bNv ℹFree download model: https://lnkd.in/djHkmXUP #Dlubal #dlubalsoftware #RWIND

We are excited to share a research paper titled "𝘈𝘥𝘷𝘢𝘯𝘤𝘦𝘥 𝘙𝘉𝘍 𝘔𝘦𝘵𝘩𝘰𝘥𝘴 𝘧𝘰𝘳 𝘔𝘢𝘱𝘱𝘪𝘯𝘨 𝘈𝘦𝘳𝘰𝘥𝘺𝘯𝘢𝘮𝘪𝘤 𝘓𝘰𝘢𝘥𝘴 𝘰𝘯𝘵𝘰 𝘚𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦𝘴 𝘪𝘯 𝘏𝘪𝘨𝘩-𝘍𝘪𝘥𝘦𝘭𝘪𝘵𝘺 𝘍𝘚𝘐 𝘚𝘪𝘮𝘶𝘭𝘢𝘵𝘪𝘰𝘯𝘴," which was just published on ResearchGate. The authors are our esteemed colleagues, Andrea Chiappa, Andrea Lopez, and Corrado Groth. 𝐓𝐡𝐢𝐬 𝐬𝐭𝐮𝐝𝐲 𝐝𝐢𝐯𝐞𝐬 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐨𝐟 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐝𝐚𝐭𝐚 𝐞𝐱𝐜𝐡𝐚𝐧𝐠𝐞 𝐢𝐧 𝐟𝐥𝐮𝐢𝐝-𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐨𝐧 (𝐅𝐒𝐈) 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬, 𝐟𝐨𝐜𝐮𝐬𝐢𝐧𝐠 𝐨𝐧 𝐭𝐡𝐞 𝐜𝐨𝐦𝐩𝐚𝐫𝐢𝐬𝐨𝐧 𝐨𝐟 𝐭𝐰𝐨 𝐦𝐚𝐩𝐩𝐢𝐧𝐠 𝐦𝐞𝐭𝐡𝐨𝐝𝐬—𝐑𝐈𝐁𝐄𝐒 𝐚𝐧𝐝 𝐩𝐫𝐞𝐂𝐈𝐂𝐄—𝐛𝐨𝐭𝐡 𝐥𝐞𝐯𝐞𝐫𝐚𝐠𝐢𝐧𝐠 𝐫𝐚𝐝𝐢𝐚𝐥 𝐛𝐚𝐬𝐢𝐬 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧 (𝐑𝐁𝐅) 𝐢𝐧𝐭𝐞𝐫𝐩𝐨𝐥𝐚𝐭𝐢𝐨𝐧. Our findings highlight the importance of balance preservation in data mapping and its impact on structural outcomes. If you're working in CFD, CSM, or FSI, this paper offers valuable insights! 𝑭𝒆𝒆𝒍 𝒇𝒓𝒆𝒆 𝒕𝒐 𝒅𝒓𝒐𝒑 𝒂 𝒄𝒐𝒎𝒎𝒆𝒏𝒕 𝒃𝒆𝒍𝒐𝒘 𝒊𝒇 𝒚𝒐𝒖 𝒉𝒂𝒗𝒆 𝒂 𝒒𝒖𝒆𝒔𝒕𝒊𝒐𝒏 𝒇𝒐𝒓 𝒖𝒔! We'll be glad to answer. #multiphysics #mapping #radialbasisfunctions #rbf #rbfMorph #aerodynamics #engineering #cae #simulation #research #fsi

I recently read a fascinating article published last week that delves deep into the impact of the Multiple Reference Frame (MRF) technique on steady RANS simulations of a Rushton turbine stirred tank using OpenFOAM. The study offers valuable insights into how the diameter and thickness of the MRF region affect the predicted velocity field and mixing times. 🔑 Key Takeaways: Velocity Profiles: Minimal differences across five MRF diameters, aligning well with experimental data. Turbulence Intensity: Significant artificial turbulence at the MRF boundary in larger diameters. Mixing Times: There is up to a three-fold variation depending on the MRF region size. The results highlight the critical importance of assessing the MRF zone size in RANS simulations of stirred tanks. Larger MRF zones generated spurious high-velocity spots and artificial turbulence, particularly at the interface boundaries, while smaller zones provided more accurate turbulent kinetic energy predictions. 🔄 Conclusions: Turbulence Models: The k-𝜔 SST model, despite being less stable, offered more accurate turbulence predictions than the k-𝜖 model. MRF Zone Impact: The dimensions of the MRF zone significantly affect simulation accuracy, stressing the necessity for further numerical studies to standardize MRF practices. I highly recommend reading this article to explore these findings in detail and understand their implications for future CFD simulations. Source: https://lnkd.in/dG_KwMUv #CFD #OpenFOAM #MRF #RANS #Simulation #Engineering #Research #Turbulence #MixingProcess

Have a look to this interesting paper by Andrea Chiappa Alessandro Lopez Corrado Groth and learn how RBF can support FSI Fluid Structure Interaction challenges!

New Turbulence Modeling URANS, DES and PANS: Modeling concepts, advantages, applications, and limitations in CFD Simulations. URANS and Hybrid Turbulence Modeling DES and PANS are becoming important part of fluid dynamics studies and related technological developments. The attached video is part of our Turbulence Course, on this topic (Full video: WAC CFD Group on https://lnkd.in/ePQ-WEvk , Next Run: https://lnkd.in/ewgQD3uz ) Overview:   Reynolds-Averaged Navier-Stokes (RANS) and Unsteady RANS (URANS): RANS: The roots of RANS modeling trace back to Osborne Reynolds in 1895, who developed the concept of Reynolds decomposition, which separates turbulent flow into mean and fluctuating components. This laid the groundwork for the time-averaged Navier-Stokes equations. In the 1940s and 1950s, with advancements in turbulence theory and numerical methods, the formal RANS equations were applied to engineering problems, using turbulence models like the k-ε and k-ω models. The approach gained widespread use in industries like aerospace and automotive from the 1980s onward due to its ability to predict mean flow fields efficiently. URANS: While RANS is computationally efficient, it cannot capture unsteady flow phenomena such as vortex shedding or flow-induced vibrations due to its time-averaging nature. URANS have been developed since 1980s for this purpose, which includes time-dependent aspects while retaining the RANS framework. URANS became feasible in the 1990s as computational power increased, allowing for transient simulations. It is a suitable compromise when LES is computationally too expensive. Applications: RANS is primarily used in scenarios where steady-state CFD Simulations are sufficient, such as pipeline flows, external aerodynamics, and cooling systems. URANS is better suited for transient flows like oscillating boundary layers, pulsating pipe flows, and unsteady aerodynamic loads. Detached Eddy Simulation (DES) History: DES was developed in the late 1990s by Philippe Spalart and others as a hybrid turbulence modeling approach to combine RANS and LES (https://lnkd.in/ebkv4zvd) to provide a balance between the computational efficiency of RANS near walls and the detailed resolution of large eddies by LES in other regions. Reason for Development: DES was designed to overcome the limitations of LES when applied to high-Reynolds-number flows, which require extremely fine grids in boundary layers. By using RANS near walls and LES in separated regions, DES allows for more accurate simulations of flow separation, wake dynamics, and vortex shedding while reducing computational costs. Applications: DES is particularly useful for aerodynamic flows around complex shapes, such as aircraft and automobiles, where flow separation is significant. It is also applied in turbomachinery, wind turbines, etc. Partially Averaged Navier-Stokes (PANS: https://lnkd.in/ePQ-WEvk) #Turbulence #fluiddynamics #CFD #mechanicalengineering #OpenFOAM

🔍 Understanding Y+ in CFD: Why It Matters for Accurate Simulations In Computational Fluid Dynamics (CFD), Y+ is a crucial parameter that measures the distance from a wall to the first cell center near the wall. It plays a vital role in capturing the complexity of turbulent flow in boundary layers, which directly impacts the accuracy of your simulation. 👉 Why is Y+ Important?   Turbulent flows near walls are highly complex, with different regions requiring different levels of mesh refinement: Viscous Sub-layer (Y+ < 5): Dominated by viscous forces, where the velocity profile is linear. Buffer Layer (5 < Y+ < 30): A transition zone where both viscous and turbulent forces are significant. Log-Law Region (Y+ > 30): Dominated by turbulent forces, with a logarithmic velocity profile. 👉 Choosing the Right Y+: Y+ ≈ 1: Best for resolving the viscous sub-layer directly. Used in Direct Numerical Simulation (DNS), Large Eddy Simulation (LES), or RANS models with enhanced wall treatment. Y+ ≈ 30-100: Common in RANS simulations with wall functions, allowing for coarser mesh and reduced computational cost. Y+ > 100: May indicate poor resolution near the wall, leading to inaccurate predictions. 👉 Why Does This Matter?  Achieving the correct Y+ value ensures that your CFD model accurately captures near-wall phenomena like drag, heat transfer, and separation. A mesh that’s too coarse (high Y+) can miss critical details, while an overly fine mesh (low Y+) can drive up computational costs without substantial benefits. In Practice:  When setting up your CFD simulations, carefully refine your mesh to hit the appropriate Y+ target for your turbulence model. This balance is key to delivering accurate and efficient results. Are you considering Y+ in your CFD simulations? Feel free to share your thoughts or experiences in the comments below! 🌟 #CFD #Engineering #TurbulenceModeling #Simulation #Yplus #ComputationalFluidDynamics #EngineeringTips

Eulerian and Lagrangian Approaches in Fluid Dynamics and CFD Simulations: Concepts and modeling strategy for single and multiphase systems. When you know "Where you are", then you know "Where and How to go". It is very important to deeply understand Eulerian and Lagrangian Approaches in Fluid Dynamics and CFD (Computational Fluid Dynamics) Simulations. Although there are very good descriptions in reference books and fluid dynamics literature (https://lnkd.in/gKqeRpA6 ), still not enough clear for many students and experts for modeling and CFD simulations. 1-First point: Eulerian and Lagrangian approaches are mostly discussed for multiphase CFD modeling and simulations. However indeed these are introduced for basic fluid dynamics modeling, not necessarily for multiphase conditions. 2- Attached video is part of our recent CFD course describing Lagrangian and Eulerian approaches in simple words, physically and mathematically for fluid dynamics modeling and CFD simulations (Next Run: https://lnkd.in/easMNVHu ). 3-In summary: For both approach we apply Newton 2nd Law F=m*a as the base of Fluid Dynamics and Navier Stokes Equations (https://lnkd.in/eg2i3Z9v) as following: - Eulerian Action Box Shooting Fluid Particles: Either we consider a box in which the fluid particle are surrounded with several forces and action from 6 faces of the box, and considering Newton second Law, we can say what is the net of force actions on our fluid particle in the box, to calculate the acceleration and mobility destination, like shooting a particle by Slingshot. Then another particle comes in the box, for next shoot. Although the position of our Eulerian Box is fix in space, however the action force isn't constant with time, and depends on the interaction of our box via 6 faces with other boxes/articles surrounding our box (pressure, friction and momentum exchange, plus body force). Eulerian approach is commonly used in continuum mechanics and CFD simulations in particular Volume of Fluid (VOF) for multiphase systems which is a powerful technique when we have complex interface breakups and deformations. -Lagrangian Tracking Moving Particles: We focus on a moving particle of fluid, or a discrete particle (droplet, bubble, solid particles) moving in the fluid. We calculate the net of action forces applied to the surface of our particle by the surrounding fluid or other particles at each moment, to predict moving acceleration and direction. Then we move with it to the new position, which can receive different forces and actions, according to the changes of flow field and surrounding particles. Lagrangian approach is relevant for problems involving particle tracking, droplets, or dispersed phases. Related Courses: https://lnkd.in/easMNVHu #Fluiddynamics #Eulerian #Lagrangian #CFD #aerospace #mechanicalengineering #processengineering #petroleum #onlinecourse #OpenFOAM #ANSYSFLUENT #thermalengineering #waterground #biomedicalengineering