3D Axial Fan: A Fluent MRF Simulation for Performance Analysis
3D Axial Fan: A Fluent MRF Simulation for Performance Analysis
- Upon ordering this product, you will be provided with a geometry file, a mesh file, and an in-depth Training Video that offers a step-by-step training on the simulation process.
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€210 Original price was: €210.€155Current price is: €155.
A 3D Axial Fan CFD Simulation Using MRF Fluent is a computer model of a fan that moves air. This type of Axial Fan CFD analysis is very important for designing many products, from computer cooling to large CFD for HVAC Systems. An axial fan has blades that spin to create a pressure difference and push air in a straight line. A FAN CFD simulation helps engineers see the airflow, which is invisible. For this Rotating Machinery CFD analysis, we use a special method called MRF (Multiple Reference Frames). This study follows the methods in the key reference paper by Yadegari et al. [1] to make sure our model is accurate.
- Reference [1]: Yadegari, Mehdi, et al. “Reducing the aerodynamic noise of the axial flow fan with perforated surface.” Applied Acoustics215 (2023): 109720.
Figure 1: A schematic showing the geometry of the Axial Fan CFD simulation, including the fan and its housing.
Simulation Process: Fluent MRF Setup, Modeling a Steady-State Rotating Fan
erform this 3D Axial Fan CFD Simulation Using MRF CFD, we first built the 3D geometry. This geometry was made of two main parts. The first part was a smaller, circular zone that contained only the spinning fan rotor. The second part was a larger, stationary zone that represented the fan housing and the air around it. We then used a modern meshing technique to create a polyhedral grid with 4,889,858 cells to accurately model the complex flow. In ANSYS Fluent, we used the Multiple Reference Frames (MRF) method. The MRF model is a steady-state approach that is perfect for Turbomachinery CFD. It works by solving the flow in the spinning fan zone in a rotating frame of reference, and the flow in the stationary zone in a global frame of reference, and then connecting them. This is much faster than a full transient simulation. We set the fan’s rotational speed to 2818 rpm. Because the airflow around the fan blades is very chaotic, we used the SST k-ω turbulence model, which is excellent for predicting flows close to walls.
Figure 2: The high-quality polyhedral mesh with 4,889,858 cells used for the FAN MRF CFD simulation.
Post-processing: CFD Analysis, Flow Dynamics, Performance, and Vorticity
The velocity contour acts as a performance map of the fan. From an engineering standpoint, it immediately shows the fan is doing its job of accelerating the air. The speed ranges from zero to a maximum of 70.3 m/s. This peak velocity is located exactly where we expect it: at the tips of the fan blades. This is because the blade tips travel the fastest, transferring the most energy to the air and creating a powerful jet of flow. In contrast, the air near the central hub is much slower, shown by the blue colors. This velocity difference is what generates the fan’s thrust. The simulation also calculated a torque coefficient (Cm) of 10.93. This number is not just a result; it is a direct measure of the fan’s power consumption. It tells us how much turning force is needed to spin the fan at 2818 rpm against the air’s resistance.
The vorticity contour tells the other half of the story, showing us where the energy is being lost. The contour reveals strong, concentrated areas of fluid rotation, especially at the blade tips. These are called tip vortices. They are created by the high-pressure air on one side of the blade leaking around the tip to the low-pressure side. These vortices are the primary source of aerodynamic noise and also represent a significant energy loss, which reduces the fan’s efficiency. By identifying the exact location and strength of these vortices, engineers can see the direct cause of noise and inefficiency. The most important achievement of this simulation is its ability to provide a complete engineering picture: it not only confirms the fan’s performance with a solid metric (Torque Coefficient = 10.93) but also precisely pinpoints the aerodynamic problems (the tip vortices) that engineers must solve to design a quieter and more efficient fan.
Figure 3: A contour of the vorticity field from the Aerodynamic Analysis Fluent, showing areas of high fluid rotation like tip vortices.
Figure 4: A contour showing the velocity distribution from the Fan Performance Simulation, highlighting the high-speed flow at the blade tips.
We pride ourselves on presenting unique products at CFDLAND. We stand out for our scientific rigor and validity. Our products are not based on guesswork or theoretical assumptions like many others. Instead, most of our products are validated using experimental or numerical data from valued scientific journals. Even if direct validation isn’t possible, we build our models and assumptions on the latest research, typically using reference articles to approximate reality.
Yes, we’ll be here . If you have trouble loading files, having technical problems, or have any questions about how to use our products, our technical support team is here to help.
You can load geometry and mesh files, as well as case and data files, using any version of ANSYS Fluent.
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