Propeller Noise is a major issue for aircraft, drones, and submarines. The sound comes from the rotating blades hitting the air. This noise annoys passengers and causes environmental pollution. For military vehicles, noise is dangerous because it reveals their location. Therefore, engineers must design quieter propulsion systems. Traditional testing in sound-proof rooms (anechoic chambers) is very expensive. To save money, we use CFD simulation combined with Acoustic analysis.

In this report, we perform a Propeller Noise ANSYS Fluent study. We focus on Broadband noise, which is caused by random turbulence and vortices, not just the blade spinning tone. We use the Broadband noise source model Fluent to predict where the sound comes from. By using the MRF CFD method, we simulate the rotation and calculate the sound power levels. This Propeller Noise Acoustic fluent simulation helps engineers see the noise sources on the computer screen. For more details on sound prediction, please explore our Acoustic tutorials: https://cfdland.com/product-category/engineering/acoustics-cfd-simulation/

Simulation Process: MRF Solver and Broadband Model Setup

For this CFD Propeller Blade noise project, we designed a specific 3D domain. We divided the space into a stationary outer zone and a rotating inner cylinder called the Body of Influence (BOI). This setup is essential for the MRF CFD method. We used Fluent Meshing to generate a high-quality grid. We chose a Poly-Hexa mesh structure. This mesh has 5,936,669 cells. This is critical for Propeller Noise ANSYS Fluent studies because the acoustic model relies on accurate turbulence data. If the mesh is too coarse, the solver cannot see the small turbulent eddies that create the sound. We also refined the mesh in the wake region to capture the spiral vortices that travel downstream.

We configured the physics using the MRF fluent model to simulate rotation at 2000 RPM. The Broadband noise source model Fluent uses this TKE data to estimate the sound. We did not need to run a transient simulation, which takes weeks. Instead, we used the steady-state MRF approach. We selected the “Broadband Noise Sources” option. This model uses empirical formulas to convert the flow turbulence into decibels (dB). It calculates noise from the boundary layer, the blade tips, and the wake. This allows the Propeller Noise Acoustic fluent simulation to output sound power levels on the surfaces and in the surrounding air.

Figure 1: Poly-Hexa computational grid generated on the Propeller Blade surface

Post-processing: Aeroacoustic Source Identification and Directivity Analysis

This section connects the aerodynamic forces to the noise they generate. We analyze the data deeply to understand how to make the propeller quieter for the manufacturer. First, we analyze Table 1 to understand the aerodynamic loads. The simulation shows that one single blade produces 12.73 N of Thrust and requires 0.545 N·m of Torque. These forces are not just for movement because they create pressure waves. A higher thrust usually means stronger pressure differences, which leads to louder noise. The CFD Analysis of Propeller Noise Acoustic confirms that this specific load condition is enough to generate significant sound, requiring acoustic optimization. Next, we examine the Vorticity Contours in Figure 2. The contour shows a helical vortex structure (spiral shape) behind the propeller. The red and yellow colors indicate very high vorticity, reaching up to 485 s⁻¹. These are known as Tip Vortices. When the high-pressure air from the bottom of the blade rolls over to the top, it spins violently. This spinning air is a major source of Broadband noise. Seeing this clearly helps the designer decide to add “winglets” or curved tips to reduce this vortex strength.

Table 1: Aerodynamic forces for a single propeller blade at 2000 RPM from MRF ANSYS Fluent simulation

We also analyze the Acoustic Power Level (dB) on the vertical plane in Figure 3. The contour shows red lobes extending upstream and downstream along the axis. The noise level in these red zones reaches 28.3 dB (Acoustic Power Level). The crucial finding here is that the noise is not a circle but is highly directional. It is loudest in front of and behind the propeller. This information is vital for drone manufacturers. It tells them that the camera or sensor placed directly under the drone might vibrate due to acoustic pressure, while the sides are relatively quieter.

Figure 2: Vorticity contours visualizing the helical tip vortices and wake structure, which are the primary sources of Broadband noise.

Figure 3: Acoustic Power Level contours on a vertical plane, illustrating the directivity pattern and noise propagation lobes along the axis.

Finally, we study the Surface Acoustic Power in Figure 4. This is the most valuable result because it maps the noise in Decibels on the blade itself. The Leading Edge and Tip are colored bright Red. The noise level here hits a maximum of 118.5 dB. The simulation proves that the noise is not coming from the center (Hub), which is Blue and Quiet. It comes from the outer tip where the speed is highest. To reduce noise, the engineer does not need to change the whole blade. They only need to modify the leading-edge geometry or change the tip shape. This targeted approach saves time and money.

Figure 4: Surface Acoustic Power distribution on the blade, identifying the leading edge and tip as the dominant noise sources in the CFD Analysis of Propeller Noise Acoustic.