RQ-11 Raven UAV(Drone) Acoustic Analysis: CFD Simulation by Ansys Fluent

Description

Acoustic Analysis: RQ-11 Raven UAV(Drone) CFD Simulation Training

Introduction

In RQ-11 Raven Drone project, we analyze a RQ-11 Raven 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 lightweight unmanned aircraft system (UAV) with excellent mobility, the RQ-11 Raven Drone, is designed for quick deployment.

The RQ-11 Raven is hand-launched into the air like a model airplane in mere minutes, and it lands itself by auto-piloting to a hovering position. It doesn’t need landing strips that have been adequately prepared. The RQ-11 Raven Drone is best suited for forward-deployed units because it doesn’t require complex support facilities.

Thanks to its automated features and GPS technology, it is easy to use and doesn’t require any specialized knowledge or extensive flight training.

An RQ-11 Raven Drone is modeled in this simulation using ANSYS Fluent software. The device moves at a speed of 13.9  m/s while the propeller rotates at an angular velocity of 1200 rev/min.

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 5,300,212.

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 for RQ-11 Raven, we extracted the Acoustic pressure level contour in decibels for the blades and in the FW_H model, we defined 7 receivers around the RQ-11 Drone 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 different 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.

After the main peak, the sound pressure level decreases rapidly and then shows a characteristic pattern of alternating peaks and valleys. These peaks represent the higher harmonics of the blade passage frequency. This means that the blade noise is not only propagated at the BPF, but also produces sound energy at its integer multiples (2xBPF, 3xBPF, etc.). In general, the amplitude of the harmonics decreases with increasing frequency.

This graph clearly shows tonal noise, which is the main characteristic of the sound produced by rotating machinery such as blades. This noise is mainly composed of thickness noise (caused by air displacement by the blade volume) and loading noise (caused by aerodynamic forces on the blade).

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.

Both graphs show a sharp increase in sound level in the lower octave bands (around 0 to 500-1000 Hz). In the higher octave bands (from around 1000 Hz onwards), the sound level remains relatively constant or decreases with a gentle slope. Weighting filters (especially A-Weighting) reduce the impact of very high harmonics or very low frequencies on the overall perception of the butterfly sound.

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. Early in time (near t=0), a sudden and sharp drop in pressure (about -0.00094 Pa) is observed. This is a negative pressure “pulse”. After the initial pulse, the Acoustic pressure settles into a regular and stable oscillation pattern (with an amplitude of about 0.00001 Pa) around a slightly negative mean value (about -0.00081 Pa).

These pulses and oscillations represent the intermittent passage of the impeller blades near the receiver. Each time a blade passes the receiver, it creates a pressure wave (including thickness noise and loading noise) that is observed as a pulse and then oscillations in the acoustic pressure. The negative nature of the initial pressure can be due to the suction effect or the rarefaction of the air as the blade passes.

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.

High dp/dt values ​​indicate rapid and severe pressure changes at those points. These rapid pressure changes are the main source of acoustic noise generation. The concentration of high dp/dt values ​​at the blade tips and edges is quite logical and expected for a rotating propeller.

This contour clearly shows the physical sources of noise that lead to the tonal and broadband peaks observed in the frequency domain plots. That is, these pressure fluctuations at the propeller surface propagate sound waves into the environment that we see in the SPL plots.

This description is a brief overview of one of the receivers defined in this project. In the training video of RQ-11 Raven Drone, we analyze and explain the extracted data and compare them with each other in more detail.