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CFD FEA Service

Name: CFD FEA Service
Address: Via A. Fogazzaro 13, Cologna Veneta, 37047, IT
Telephone: +393783033133

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Informazioni di contatto, mappa e indicazioni stradali, modulo di contatto, orari di apertura, servizi, valutazioni, foto, video e annunci di CFD FEA Service, Servizi di ingegneria, Via A. Fogazzaro 13, Cologna Veneta.

CFD FEA SERVICE is a company specialized in four type of services:
- CloudHPC provider
- Mechanical engineering consulting
- Civil engineering consulting in Fire Prevention
- Training to opensource CFD / FEA / FEM software

17/10/2026

Do you know that , the widely used software, offers various branches, with two of the most common being OpenFOAM.com and OpenFOAM.org?

For those unsure of which branch to utilize, you can sign up for our cloudhpc.cloud platform, where you can access both options along with a plethora of other tools designed to assist you in managing your simulations. Some of these tools include ParaView, cfMesh+, SALOME, preCICE, scilab, humidityRHOTHERMO compiled in v8 and v2012, openInjMoldSim compiled in v7, and OpenQBMM compiled in v2106.

It's important to note that this collection of tools is not endorsed or sanctioned by OpenCFD Limited, the company behind the OpenFOAM software. They are the producers and distributors of OpenFOAM and hold the trademarks for OPENFOAM® and OpenCFD®.

By exploring the various branches of OpenFOAM and utilizing the additional tools available on our platform, you can enhance your simulation capabilities and streamline your workflow in the field of computational fluid dynamics.

13/10/2026

Excited to share our 𝗹𝗮𝘁𝗲𝘀𝘁 𝗱𝗲𝗲𝗽 𝗱𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝗙𝗶𝗻𝗶𝘁𝗲 𝗘𝗹𝗲𝗺𝗲𝗻𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 (𝗙𝗘𝗔) of a drone frame! 🚀 Using open-source powerhouses SALOME for meshing (with the fantastic 𝘯𝘦𝘵𝘨𝘦𝘯 𝘢𝘭𝘨𝘰𝘳𝘪𝘵𝘩𝘮!) and 𝗖𝗼𝗱𝗲_𝗔𝘀𝘁𝗲𝗿 for static (via STAT_NON_LINE) and modal (using CALC_MODES) analyses, we explored its structural dynamics.

A key takeaway? 𝘛𝘩𝘦 𝘪𝘯𝘤𝘳𝘦𝘥𝘪𝘣𝘭𝘦 𝘪𝘮𝘱𝘢𝘤𝘵 𝘰𝘧 𝘱𝘢𝘳𝘢𝘭𝘭𝘦𝘭 𝘱𝘳𝘰𝘤𝘦𝘴𝘴𝘪𝘯𝘨! We supercharged our simulations by leveraging multiple cores and, crucially, the on-demand high-performance computing resources of CloudHPC. ☁️

𝗧𝗵𝗶𝘀 𝗱𝗿𝗮𝘀𝘁𝗶𝗰𝗮𝗹𝗹𝘆 𝗰𝘂𝘁 𝗱𝗼𝘄𝗻 𝗰𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻 𝘁𝗶𝗺𝗲, 𝗺𝗮𝗸𝗶𝗻𝗴 𝗶𝘁𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗱𝗲𝘀𝗶𝗴𝗻 𝗮𝗻𝗱 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗮 𝗯𝗿𝗲𝗲𝘇𝗲.

Read the full story and see the visual results: https://cloudhpc.cloud/2025/07/29/decoding-drone-fem-a-journey-with-code_aster-and-cloudhpc/

13/10/2026

🌊 𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗛𝘆𝗱𝗿𝗼𝗱𝘆𝗻𝗮𝗺𝗶𝗰 𝗟𝗼𝗮𝗱 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗶𝗻 𝗣𝗮𝗿𝗮𝗩𝗶𝗲𝘄

Quantifying the resultant forces acting on complex geometries—like a water pump impeller—is a critical step in assessing structural integrity and hydraulic efficiency.

However, extracting these values accurately from CFD data requires a 𝗿𝗶𝗴𝗼𝗿𝗼𝘂𝘀 𝗽𝗼𝘀𝘁-𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄.In my latest technical blog post, I dive deep into the engineering methodology for calculating pressure and shear forces using ParaView.

🛠️ 𝗞𝗲𝘆 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀:

𝗩𝗲𝗰𝘁𝗼𝗿 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: Leveraging the IntegrateVariables filter for precise surface integrals.

𝗡𝗼𝗿𝗺𝗮𝗹 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁: Ensuring correct orientation with Generate Surface Normals for accurate F_p = -p n dA calculations.

𝗦𝗵𝗲𝗮𝗿 𝗦𝘁𝗿𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: Incorporating wall shear stress tensors into the total force vector.

𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: A step-by-step walkthrough using a water pump impeller case study.

Whether you are optimizing for cavitation reduction or conducting FEA prep, understanding the underlying pipeline in 𝘗𝘢𝘳𝘢𝘝𝘪𝘦𝘸 𝘪𝘴 𝘦𝘴𝘴𝘦𝘯𝘵𝘪𝘢𝘭 for high-fidelity engineering.

Read the full technical breakdown here: https://cloudhpc.cloud/2026/01/09/calculating-forces-on-surfaces-in-paraview-a-technical-guide-for-engineers/

12/10/2026

𝗗𝗼𝗲𝘀 𝗘𝗹𝗺𝗲𝗿 𝗙𝗘𝗠 𝗰𝗼𝗻𝘃𝗲𝗿𝗴𝗲 𝗮𝘁 𝘁𝗵𝗲 𝗿𝗮𝘁𝗲 𝘁𝗵𝗲𝗼𝗿𝘆 𝘀𝗮𝘆𝘀 𝗶𝘁 𝘀𝗵𝗼𝘂𝗹𝗱? 𝗪𝗲 𝗰𝗵𝗲𝗰𝗸𝗲𝗱.

We verified Elmer FEM 9.0 on steady heat conduction using two cases with exact solutions, so the error can be measured, not guessed:

• 𝗔 3𝗗 𝗰𝘂𝗯𝗲 with a manufactured solution (sin·sin·sin), solved on linear and quadratic tetrahedra and on hexahedra.
• 𝗔 𝘁𝗵𝗶𝗰𝗸-𝘄𝗮𝗹𝗹𝗲𝗱 𝗵𝗼𝗹𝗹𝗼𝘄 𝗰𝘆𝗹𝗶𝗻𝗱𝗲𝗿 with a convection boundary condition and a curved wall (k = 45 W/mK, h = 100 W/m²K).

𝗪𝗵𝗮𝘁 𝘄𝗲 𝗳𝗼𝘂𝗻𝗱:

• The observed convergence order matches theory in every series: 2 𝗳𝗼𝗿 𝗹𝗶𝗻𝗲𝗮𝗿 𝗲𝗹𝗲𝗺𝗲𝗻𝘁𝘀, 3 𝗳𝗼𝗿 𝗾𝘂𝗮𝗱𝗿𝗮𝘁𝗶𝗰 𝗲𝗹𝗲𝗺𝗲𝗻𝘁𝘀 (relative L2 error of temperature).
• Quadratic elements pay off on smooth problems. On the cube, they reach a 7× 𝘀𝗺𝗮𝗹𝗹𝗲𝗿 𝗲𝗿𝗿𝗼𝗿 𝘄𝗶𝘁𝗵 3× 𝗳𝗲𝘄𝗲𝗿 𝗻𝗼𝗱𝗲𝘀. On the cylinder, at about the same node count, the error is 22× 𝘀𝗺𝗮𝗹𝗹𝗲𝗿.
• The outer wall temperature converges to the exact value of 64.1106 °C. With quadratic triangles we get 64.1106 at the finest mesh.
• A run on 4 MPI processes gives the 𝘀𝗮𝗺𝗲 𝗲𝗿𝗿𝗼𝗿 𝗮𝘀 𝘁𝗵𝗲 𝘀𝗲𝗿𝗶𝗮𝗹 𝗿𝘂𝗻 𝘁𝗼 𝘀𝗲𝘃𝗲𝗻 𝗱𝗶𝗴𝗶𝘁𝘀.

𝗪𝗵𝗮𝘁 𝘁𝗵𝗶𝘀 𝗶𝘀 𝗮𝗻𝗱 𝗶𝘀 𝗻𝗼𝘁: it is 𝘷𝘦𝘳𝘪𝘧𝘪𝘤𝘢𝘵𝘪𝘰𝘯 (are the equations solved correctly?), not 𝘷𝘢𝘭𝘪𝘥𝘢𝘵𝘪𝘰𝘯 (do the equations match experiments?). The results come from a local Elmer 9.0 compilation, one run per mesh, steady and smooth problems only. The next step is to repeat the cases on cloudHPC and add a scalability test.

The post includes the equations, the Elmer input snippets, tables, figures, the references and all the case files, so you can repeat everything.

👉 https://cloudhpc.cloud/2026/10/08/elmer-fem-heat-transfer-verification/

09/10/2026

𝗛𝗼𝘄 𝘄𝗲𝗹𝗹 𝗱𝗼𝗲𝘀 𝗰𝗼𝗱𝗲_𝘀𝗮𝘁𝘂𝗿𝗻𝗲 𝘀𝗰𝗮𝗹𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗹𝗼𝘂𝗱? 𝗪𝗲 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝗱 𝗶𝘁: 12 𝗿𝘂𝗻𝘀, 𝗻𝗼 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗴𝗹𝗼𝘀𝘀.

𝗧𝗵𝗲 𝗰𝗮𝘀𝗲: LES of flow past a circular cylinder at Re = 3900, 20.7 million hexahedral cells, WALE model, code_saturne 9.0.1 on cloudHPC. We ran it on 32, 48 and 96 hypercore cores and on 32, 48 and 112 highcore cores, each with two partitioners (SCOTCH and Hilbert).

𝗪𝗵𝗮𝘁 𝘄𝗲 𝗳𝗼𝘂𝗻𝗱:

• 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: 86-88% at 48 cores, 73-78% at 96-112 cores (SCOTCH). Speed-up tops out at 2.2-2.7x on 3-3.5x the cores.
• 𝗥𝘂𝗹𝗲 𝗼𝗳 𝘁𝗵𝘂𝗺𝗯: keep at least 200-250k cells per core. Below that, you already pay for cores that mostly wait on MPI.
• 𝗛𝘆𝗽𝗲𝗿𝗰𝗼𝗿𝗲 𝘃𝘀 𝗵𝗶𝗴𝗵𝗰𝗼𝗿𝗲: hypercore is 2.5-2.7x faster per step at the same core count. 32 hypercore cores match 112 highcore cores, at a lower cost per result.
• 𝗧𝗵𝗲 𝗽𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗲𝗿 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝗲𝘅𝗽𝗲𝗰𝘁𝗲𝗱: SCOTCH gives faster time steps but took 3.2 hours of preprocessing at 96 cores. Hilbert took 8 seconds, with steps 8-27% slower. For runs under a few thousand steps, Hilbert wins.

𝗛𝗼𝗻𝗲𝘀𝘁 𝗰𝗮𝘃𝗲𝗮𝘁𝘀: these are 400-step start-up runs, so they say nothing about the physical validation (Cd, St), and each configuration was run once.

The post includes the mesh generator approach, all the plots, the full table, the setup details and the references we used.

👉 https://cloudhpc.cloud/2026/10/08/codesaturne-cylinder-les-scalability-cloudhpc/

08/10/2026

To effectively harness the potential of our service (https://cloudhpc.cloud), mastering the skill of scalability is crucial. Scaling your resources up or down based on demand can significantly impact the efficiency and cost-effectiveness of your operations. By optimizing scalability, you can ensure that your computing power aligns seamlessly with your workload requirements, avoiding unnecessary expenses or performance bottlenecks.

One key aspect to consider when aiming for optimal scalability is the utilization of cloud-based services like . These services offer flexible solutions that can adapt to your changing needs, whether it's managing large datasets, processing complex calculations, or running simulations. By leveraging the capabilities of , you can enhance the performance of your high-performance computing tasks while maintaining cost efficiency.

Incorporating best practices for scalability involves understanding your workload patterns, identifying potential bottlenecks, and implementing strategies to address them. Monitoring key performance metrics, such as resource utilization and response times, can provide valuable insights into the effectiveness of your scalability efforts. By continuously optimizing and fine-tuning your scalability strategy, you can ensure that your cloud resources are always aligned with your business objectives.

Furthermore, staying informed about the latest trends and advancements in cloud computing can help you stay ahead of the curve. Industry reports and case studies can offer valuable insights into how other organizations have successfully leveraged scalable cloud solutions to drive innovation and growth. By staying proactive and adaptable, you can position your organization for success in an increasingly dynamic and competitive landscape.

02/10/2026

Running a simulation in the cloud used to mean a checklist: compress the case, pick vCPU and RAM, check the inputs, launch, monitor, read the logs, download.

𝗡𝗼𝘄 𝗶𝘁 𝗰𝗮𝗻 𝗯𝗲 𝗮 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻.

With the new 𝗰𝗹𝗼𝘂𝗱𝗛𝗣𝗖 𝗔𝗜 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻, your assistant (Claude, ChatGPT, Gemini or GitHub Copilot) can:

• 𝗥𝗲𝗮𝗱 𝘆𝗼𝘂𝗿 𝗰𝗮𝘀𝗲 (FDS, OpenFOAM, code_aster, EnergyPlus, OpenRadioss) and suggest the right machine
• 𝗖𝗮𝘁𝗰𝗵 𝘁𝗵𝗲 𝗰𝗹𝗮𝘀𝘀𝗶𝗰 𝗺𝗶𝘀𝘁𝗮𝗸𝗲𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝘆𝗼𝘂 𝗽𝗮𝘆: MPI_PROCESS order, decomposeParDict, IDF version mismatch
• 𝗟𝗮𝘂𝗻𝗰𝗵 𝘁𝗵𝗲 𝗿𝘂𝗻, but only after you confirm it
• 𝗖𝗵𝗲𝗰𝗸 𝘁𝗵𝗮𝘁 𝘁𝗵𝗲 𝘀𝗼𝗹𝘃𝗲𝗿 𝗿𝗲𝗮𝗹𝗹𝘆 𝗳𝗶𝗻𝗶𝘀𝗵𝗲𝗱, not just that the job ended
• 𝗕𝗿𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 𝗯𝗮𝗰𝗸 𝗶𝗻𝘁𝗼 𝘆𝗼𝘂𝗿 𝗳𝗼𝗹𝗱𝗲𝗿, with the cost in euro

In one of our tests, an FDS case with two meshes went from "𝘤𝘩𝘦𝘤𝘬 𝘵𝘩𝘪𝘴 𝘤𝘢𝘴𝘦 𝘢𝘯𝘥 𝘳𝘶𝘯 𝘪𝘵" to results on disk for €0.03, with one confirmation along the way.

It's 𝗼𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲, and it runs locally with your own API key. You stay in control: nothing that costs money or deletes data happens without your OK.

👉 Read how it works and set it up in two minutes: [link to the blog post]

01/10/2026

🏎️ 𝗘𝗡𝗚𝗜𝗡𝗘𝗘𝗥𝗜𝗡𝗚 𝗛𝗜𝗚𝗛-𝗣𝗘𝗥𝗙𝗢𝗥𝗠𝗔𝗡𝗖𝗘 𝗠𝗔𝗡𝗜𝗙𝗢𝗟𝗗𝗦 𝗪𝗜𝗧𝗛 𝗖𝗙𝗗
In modern ICE development, the manifold is more than just a pipe; it’s a critical tuning element. Using OpenFOAM, we can dive deep into the three pillars of manifold design:

1. 𝗘𝗦𝗧𝗜𝗠𝗔𝗧𝗜𝗡𝗚 𝗣𝗥𝗘𝗦𝗦𝗨𝗥𝗘 𝗗𝗥𝗢𝗣𝗦
The primary goal for intake manifolds is minimizing pumping losses. By solving the Navier-Stokes equations, we can identify regions of flow separation or excessive turbulence that choke the engine.

𝗧𝗵𝗲 𝗚𝗼𝗮𝗹: 𝘌𝘯𝘴𝘶𝘳𝘦 𝘵𝘩𝘦 𝘴𝘵𝘢𝘵𝘪𝘤 𝘱𝘳𝘦𝘴𝘴𝘶𝘳𝘦 𝘳𝘦𝘤𝘰𝘷𝘦𝘳𝘺 𝘪𝘴 𝘮𝘢𝘹𝘪𝘮𝘪𝘻𝘦𝘥 𝘣𝘦𝘧𝘰𝘳𝘦 𝘵𝘩𝘦 𝘪𝘯𝘵𝘢𝘬𝘦 𝘷𝘢𝘭𝘷𝘦.

2. 𝗘𝗩𝗔𝗟𝗨𝗔𝗧𝗜𝗡𝗚 𝗙𝗟𝗢𝗪 𝗗𝗜𝗦𝗧𝗥𝗜𝗕𝗨𝗧𝗜𝗢𝗡
𝘜𝘯𝘪𝘧𝘰𝘳𝘮𝘪𝘵𝘺 𝘪𝘴 𝘬𝘪𝘯𝘨. 𝘐𝘧 𝘰𝘯𝘦 𝘤𝘺𝘭𝘪𝘯𝘥𝘦𝘳 𝘪𝘴 𝘳𝘶𝘯𝘯𝘪𝘯𝘨 𝘭𝘦𝘢𝘯 𝘸𝘩𝘪𝘭𝘦 𝘢𝘯𝘰𝘵𝘩𝘦𝘳 𝘪𝘴 𝘳𝘪𝘤𝘩 𝘥𝘶𝘦 𝘵𝘰 𝘶𝘯𝘦𝘷𝘦𝘯 𝘮𝘢𝘴𝘴 𝘧𝘭𝘰𝘸 𝘳𝘢𝘵𝘦𝘴, 𝘺𝘰𝘶 𝘭𝘰𝘴𝘦 𝘱𝘰𝘸𝘦𝘳 𝘢𝘯𝘥 𝘳𝘪𝘴𝘬 𝘬𝘯𝘰𝘤𝘬. 𝘞𝘦 𝘶𝘴𝘦 𝘊𝘍𝘋 𝘵𝘰 𝘲𝘶𝘢𝘯𝘵𝘪𝘧𝘺 𝘵𝘩𝘦 𝗙𝗹𝗼𝘄 𝗨𝗻𝗶𝗳𝗼𝗿𝗺𝗶𝘁𝘆 𝗜𝗻𝗱𝗲𝘅 𝘢𝘤𝘳𝘰𝘴𝘴 𝘢𝘭𝘭 𝘳𝘶𝘯𝘯𝘦𝘳𝘴 𝘶𝘯𝘥𝘦𝘳 𝘵𝘳𝘢𝘯𝘴𝘪𝘦𝘯𝘵 𝘤𝘰𝘯𝘥𝘪𝘵𝘪𝘰𝘯𝘴.

3. 𝗖𝗢𝗡𝗝𝗨𝗚𝗔𝗧𝗘 𝗛𝗘𝗔𝗧 𝗧𝗥𝗔𝗡𝗦𝗙𝗘𝗥 (𝗖𝗛𝗧)
𝘍𝘰𝘳 𝘦𝘹𝘩𝘢𝘶𝘴𝘵 𝘮𝘢𝘯𝘪𝘧𝘰𝘭𝘥𝘴, 𝘮𝘢𝘯𝘢𝘨𝘪𝘯𝘨 𝘩𝘦𝘢𝘵 𝘪𝘴 𝘢 𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘢𝘭 𝘯𝘦𝘤𝘦𝘴𝘴𝘪𝘵𝘺. 𝘜𝘴𝘪𝘯𝘨 𝘴𝘰𝘭𝘷𝘦𝘳𝘴 𝘭𝘪𝘬𝘦 𝗰𝗵𝘁𝗠𝘂𝗹𝘁𝗶𝗥𝗲𝗴𝗶𝗼𝗻𝗙𝗼𝗮𝗺, 𝘸𝘦 𝘴𝘪𝘮𝘶𝘭𝘢𝘵𝘦 𝘵𝘩𝘦 𝘩𝘦𝘢𝘵 𝘵𝘳𝘢𝘯𝘴𝘧𝘦𝘳 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘵𝘩𝘦 𝘩𝘪𝘨𝘩-𝘷𝘦𝘭𝘰𝘤𝘪𝘵𝘺 𝘦𝘹𝘩𝘢𝘶𝘴𝘵 𝘨𝘢𝘴𝘦𝘴 𝘢𝘯𝘥 𝘵𝘩𝘦 𝘴𝘰𝘭𝘪𝘥 𝘮𝘢𝘯𝘪𝘧𝘰𝘭𝘥 𝘸𝘢𝘭𝘭.

𝗧𝗵𝗲 𝗚𝗼𝗮𝗹: Predict thermal stresses and prevent cracking or gasket failure.

🛠️ 𝗧𝗘𝗖𝗛𝗡𝗜𝗖𝗔𝗟 𝗛𝗜𝗡𝗧𝗦: 𝗣𝗢𝗦𝗧-𝗣𝗥𝗢𝗖𝗘𝗦𝗦𝗜𝗡𝗚 𝗜𝗡 𝗣𝗔𝗥𝗔𝗩𝗜𝗘𝗪
Once your simulation is complete, the real engineering happens in 𝗣𝗮𝗿𝗮𝗩𝗶𝗲𝘄. Here is how to extract the data that matters:

𝗧𝗼 𝗖𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲 𝗣𝗿𝗲𝘀𝘀𝘂𝗿𝗲 𝗗𝗿𝗼𝗽:
𝘜𝘴𝘦 𝘵𝘩𝘦 '𝗦𝗹𝗶𝗰𝗲' 𝘧𝘪𝘭𝘵𝘦𝘳 𝘢𝘵 𝘺𝘰𝘶𝘳 𝘪𝘯𝘭𝘦𝘵 𝘢𝘯𝘥 𝘰𝘶𝘵𝘭𝘦𝘵 𝘱𝘢𝘵𝘤𝘩𝘦𝘴. 𝘈𝘱𝘱𝘭𝘺 𝘵𝘩𝘦 '𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀' 𝘧𝘪𝘭𝘵𝘦𝘳 𝘵𝘰 𝘣𝘰𝘵𝘩. 𝘛𝘩𝘦 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘪𝘯 𝘵𝘩𝘦 𝘢𝘳𝘦𝘢-𝘸𝘦𝘪𝘨𝘩𝘵𝘦𝘥 𝘢𝘷𝘦𝘳𝘢𝘨𝘦 𝘱𝘳𝘦𝘴𝘴𝘶𝘳𝘦 𝘸𝘪𝘭𝘭 𝘨𝘪𝘷𝘦 𝘺𝘰𝘶 𝘺𝘰𝘶𝘳 𝘱𝘳𝘦𝘴𝘴𝘶𝘳𝘦 𝘥𝘳𝘰𝘱.
𝗧𝗼 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝗙𝗹𝗼𝘄 𝗥𝗮𝘁𝗲𝘀:
Don't just look at velocity streamlines. Use the '𝗦𝘂𝗿𝗳𝗮𝗰𝗲 𝗙𝗹𝗼𝘄' or '𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀' filter on a slice of your runner. This gives you the precise mass flow rate in kilograms per second.

> 𝗣𝗿𝗼 𝗧𝗶𝗽: Use the '𝗣𝗹𝗼𝘁 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗢𝘃𝗲𝗿 𝗧𝗶𝗺𝗲' filter if you are running a transient simulation to see how the pulses interact with the manifold geometry.

01/10/2026

📢 𝗡𝗲𝘄 𝗕𝗹𝗼𝗴 𝗣𝗼𝘀𝘁 𝗔𝗹𝗲𝗿𝘁! 🔥 𝗣𝗮𝗿𝗮𝗩𝗶𝗲𝘄 𝗨𝘀𝗲𝗿𝘀, 𝗧𝗵𝗶𝘀 𝗢𝗻𝗲'𝘀 𝗙𝗼𝗿 𝗬𝗼𝘂! 🔥

Struggling to visualize your Open FDS fire simulations? Our latest blog post provides a clear guide on how to seamlessly load and 𝘷𝘪𝘴𝘶𝘢𝘭𝘪𝘻𝘦 𝘺𝘰𝘶𝘳 𝘍𝘋𝘚 𝘳𝘦𝘴𝘶𝘭𝘵𝘴 𝘥𝘪𝘳𝘦𝘤𝘵𝘭𝘺 𝘸𝘪𝘵𝘩𝘪𝘯 𝘗𝘢𝘳𝘢𝘝𝘪𝘦𝘸! 🚀

Unlock the advanced visualization capabilities of ParaView for your fire dynamics studies. Learn step-by-step how to import your data and create compelling visual representations for deeper insights.

Level up your fire simulation analysis with ParaView: https://cloudhpc.cloud/2024/03/04/open-fds-in-paraview/

30/09/2026

Stop letting your hardware dictate your CFD accuracy. 🛑💻

Transient simulations, like the 𝗩𝗼𝗻 𝗞á𝗿𝗺á𝗻 𝗩𝗼𝗿𝘁𝗲𝘅 𝗦𝘁𝗿𝗲𝗲𝘁, are the gold standard for capturing real-world fluid behavior. But they come with a catch: they are incredibly resource-intensive.

If you've ever had to "coarsen" your mesh or limit your time-steps just to get a simulation to finish on your local workstation, you're compromising on your engineering data.

In our latest technical guide, we break down:
🔹 The physics of vortex shedding at .
🔹 How to configure 𝗢𝗽𝗲𝗻𝗙𝗢𝗔𝗠 for stable transient analysis.
🔹 The exact U, d and ν and parameters to replicate this phenomenon.

🚀 𝗧𝗵𝗲 𝗖𝗹𝗼𝘂𝗱𝗛𝗣𝗖 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲:
Why wait 48 hours for a solution on 8 cores when you can run it in 2 hours on 128 cores? With 𝗖𝗹𝗼𝘂𝗱𝗛𝗣𝗖.𝗰𝗹𝗼𝘂𝗱, you get:

✅ Pre-configured OpenFOAM environments.
✅ Massive CPU scalability on demand.
✅ Full control over your budget with "pay-per-use" high-performance computing.

Don't let a "Computing..." progress bar slow down your innovation. Scale your transient simulations today.

🔗 Read the full article here: https://cloudhpc.cloud/2026/02/23/the-dance-of-the-fluid-simulating-von-karman-vortex-shedding/

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