VTOL UAV Acoustic Analysis: CFD Simulation by Ansys Fluent

Description

Acoustic Analysis: VTOL UAV CFD Simulation Training

Introduction

In this project, we analyze a VTOL drone acoustically and examine the sources of sound production. We also define receivers around the drone to observe and examine the amount of sound received by the receivers.

A VTOL is a drone that uses rotors to take off, hover, and land vertically, much like a helicopter. It also has a fixed wing for long flights. Having these two features puts this drone in the Fixed_Wing Hybrid category.

In this simulation, a VTOL drone with four propellers rotating around a vertical axis and one propeller rotating around a horizontal axis is modeled using ANSYS Fluent software. The device moves upwards at a speed of 20 m/s.

The geometry of the present model is 3D and is designed using Design Modeler software. We mesh the present model with Fluent Meshing software. The mesh type is polyhedral and the number of elements is 1,761,160.

Methodology

The topic of acoustics is a very widely used and interesting topic in computational fluid dynamics. In this topic, we deal with waves and consequently with pressure.

For this project, we have used two models, BroadBand Noise and Ffowcs Williams and Hawkings (FW_H), and we have explained the settings for both models and examined the differences between the two models. First, we simulated the BroadBand Noise model steady, and after convergence and aerodynamic stability of the problem, we change the solution model to FW_H and perform the solution transient. If we activate the FW_H model from the beginning, we will hardly reach convergence and the solution may even diverge.

In the BroadBand Noise model, we extracted the Acoustic pressure level contour in decibels for the blades and in the FW_H model, we defined 7 receivers around the drone and extracted the following results:

• Acoustic Pressure vs Time: This pressure is actually the acoustic signal calculated from the Faroukas–Williams–Hawkes (FW–H) equation, which is due to the fluctuations in the flow around the propellers.
• Sound Pressure Level: SPL is the “physical intensity of the produced sound” and is directly proportional to the sound energy, without the interference of the human ear.
• A-Weighted Sound Pressure Level: A-weighting is a filter-like function that simulates the sensitivity of the human ear. Instead of the physical SPL, the sound level in this graph is calculated to represent the “actual loudness perceived by the ear.”
• B-Weighted Sound Pressure Level: The B-weighted filter is weaker than the A-weighted filter and only attenuates a portion of the low frequencies.
• dpdt RMS: The values ​​in this contour indicate the intensity of the time-dependent pressure fluctuations at each point on the surface.

Conclusion

In the BroadBand Noise model, we can observe the Acoustic pressure level contour in decibels. Acoustic Power Level (Lw) actually represents the sound power produced by the entire or part of the surface of an object and is expressed in decibels (dB). Comparing this contour with the Turbulent intensity contour, we find the similarity between them. In reality, the Acoustic pressure level is calculated and displayed based on the Turbulent Intensity. Therefore, wherever the Turbulent intensity is high, the acoustic pressure is also high.

Sometimes, slight differences appear between these two parameters, near the object (source area), the similarity is high. Second, at greater distances, the patterns may differ because the intensity depends on the direction of propagation and wave attenuation.

In the FW_H model, the extracted data for one of the defined receivers can also be viewed.

SPL (Sound Pressure Level)  graph is obtained by applying a Fourier transform (FFT) to the time domain signal and shows the noise characteristics in the frequency domain. What is clearly visible are very sharp and distinct peaks at certain frequencies. These peaks, which have high sound pressure levels, indicate tonal noise.

There is a very high and prominent peak at low frequencies (close to 0 Hz). This peak represents the Blade Passage Frequency (BPF) and its primary harmonics. BPF is the frequency at which each blade passes in front of a given point and is calculated by the formula (RPM / 60) * number of blades.

As the frequency increases, the sound pressure level gradually decreases and then increases and fluctuates again at around 3 to 3.5 kHz; these fluctuations are usually caused by aeroacoustic interference between the blades and the drone body. The peaks indicate the dominant frequencies in the drone noise, for example, the blade passing frequency (BPF). The higher the p_rms at that frequency, the higher the SPL.

The human ear is not equally sensitive to all frequencies. The ear is less sensitive at frequencies below 1000 Hz, and peaks at around 3–4 kHz. A-weighted SPL is a filter-like function that simulates this feature. However, instead of the physical SPL, the sound level is calculated in such a way that it shows the “actual loudness perceived by the ear“.

The B-weighted SPL filter is weaker than the A-weighted SPL filter and only attenuates a portion of the low frequencies. As a result, the B-weighted SPL graph falls between the SPL and A-weighted SPL graphs [ SPL > dBB > dBA ]. This filter is more suitable for medium-intensity sound sources (around 70–90 dB) and shows that the effect of mid- to low-frequency sounds (e.g., the humming of a butterfly) is still relatively effective.

Finally, the SPL is the actual sound intensity from the drone, derived from the pressure field calculated by FW–H, the A-weighted SPL (dBA) is the sound level that humans actually hear (the hearing threshold is taken into account), and the B-weighted SPL (dBB) is an approximation of the perceived sound at medium intensities. Comparing these three shows the difference between the physical energy of the sound and the perceived energy; in drones, the lower frequency (BPF) is usually clear in SPL but less so in dBA.

The Acoustic Pressure graphs show the time variations of the sound pressure ( p’(t) ) at the receiver location. This pressure is actually the acoustic signal calculated from the Ffowcs–Williams–Hawkes (FW–H) equation, which is due to the fluctuations in the flow around the propellers. This graph shows that in this receiver, an oscillating acoustic pressure with a specific frequency and a relatively large amplitude is recorded. This pattern clearly indicates periodic noise caused by blade passing noise.

In simple terms, the drone emits its strongest periodic sound in the direction of this receiver, and its noise is stable and mechanical, not chaotic or random.

The last data that we will discuss in this report is the dpdt RMS contour. The values ​​in this contour indicate the intensity of the time-dependent pressure fluctuations at each point on the surface.The higher this value, the more that point contributes to the generation and emission of acoustic noise. It is clear from the relative symmetry of the two blades that the pattern is almost symmetrical (left and right are almost the same). This indicates mechanical balance and symmetrical flow in the propeller.

However, a small redder section is visible on the left side than on the right. There is probably a slight flow misalignment or a different angle of attack on this side, which has increased the instantaneous pressure change in that area.

This description is a brief overview of one of the receivers defined in this project. In the training video, we analyze and explain the extracted data and compare them with each other in more detail.