RQ-170 UAV Acoustic Analysis: CFD Simulation by Ansys Fluent

Description

Acoustic Analysis: RQ-170 UAV CFD Simulation Training

Introduction

In this project, we analyze a RQ-170 UAV 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.

The RQ-170 is a high-altitude, long-endurance unmanned aerial vehicle (UAV). The UAV can take real-time images and send them to the ground control station through a line-of-sight communication data link. In this simulation, an RQ-170 UAV is modeled using ANSYS Fluent software. The device is moving at a speed of 80 mph.

The geometry of the present model is three-dimensional and has been designed using Design Modeler software. We do the meshing of the present model with Fluent Meshing software. The mesh type is Polyhedra, and the element number is 2,415,175.

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 UAV and extracted the following results:

• Acoustic Pressure vs Time: 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.
• 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.

Results

In the BroadBand Noise model, we can observe the Acoustic pressure level contour in decibels. Acoustic Power Level 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 and 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. No frequency filtering is applied here. The graph starts with a relatively high level at low frequencies (about -15 dB at frequencies near zero) and then decreases slowly and uniformly with increasing frequency, reaching about -40 dB at 5000 Hz.

The decreasing trend of this graph with increasing frequency is very common for many sources of aerodynamic noise (such as turbulent boundary layer noise, vortices, and turbulent jets). This means that most of the sound energy produced by this aircraft is at lower frequencies, and the contribution of sound energy decreases as the frequency increases.

The graphs below show the sound pressure level in wider frequency bands (octave) and use A and B weighting filters.

Octave bands collect noise energy in specific frequency ranges rather than representing individual frequencies.

A-weighting simulates the human auditory response, meaning it reduces low and very high frequencies to make the graph more consistent with what the human ear hears. B-weighting also simulates the human auditory response, but it provides less reduction at low frequencies than A-weighting and was used more for older measurements. The most important factor in the shape of this graph is the application of the A-Weighting filter. This filter simulates the sensitivity of the human ear at different frequencies.

The human ear is most sensitive to mid-range frequencies (about 1 to 5 kHz) and hears low frequencies much less. For this reason, even if there is a lot of noise at low frequencies (below 100 Hz), the A-Weighting filter will greatly attenuate them, which is why the graph starts at very low values ​​(for example, -140 dBA). As the frequency increases, the filter attenuation decreases and the sound pressure level “perceived” by the ear rises rapidly.

The B-Weighting filter is also used to simulate the sensitivity of the human ear, but it attenuates low frequencies less than A-Weighting. For this reason, the B-Weighted graph shows slightly higher values ​​at low frequencies than the A-Weighted graph, and its slope is less steep (for example, starting at -90 dBB instead of -140 dBA). The peak is also slightly higher, because the overall attenuation of frequencies is less. B-weighting is less commonly used in modern analysis, and A-weighting and C-weighting are more common, but it gives us a similar perspective to A-weighting, with a slightly greater emphasis on lower frequencies.

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 plot shows that the acoustic field at the receiver point of interest has reached a steady or quasi-steady state after a very short transient phase at the beginning of the simulation. This stability usually occurs when the fluid flow (CFD) underlying the FW-H analysis has reached a steady state or at least is statistically stable. This means that the noise sources (pressure and velocity changes on the surface) do not change significantly over time. It is noteworthy that the changes on the Y-axis (acoustic pressure) are very small (only 0.003 dB). This means that the system is very stable and the noise generated at this point is either very low or does not fluctuate significantly after the initial transition.

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.At most points on the wing surface, the pressure changes over time are relatively small, meaning that the fluid flow in these areas is more stable and less aerodynamic noise is emitted from these surfaces. At points where flow separation or strong vortex shedding occurs, the pressure fluctuations on the surface increase. Any protrusions, gaps, or sharp edges can also increase the dpdt and thus produce more noise.

This contour shows us that to reduce aircraft noise, we should focus on aerodynamic optimization and design of the central part of the aircraft to minimize surface pressure fluctuations in these areas.

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.