We’re all familiar with the legendary Concorde that enabled supersonic transcontinental travel, cutting down on travel time across the Atlantic to under 3.5 hours. The sonic boom of the Concorde was the primary reason why it was only used for flights over the ocean, instead of over-the-land travel. Decades later, NASA’s X-59 Quesst (Quiet SuperSonic Technology) aircraft represents a bold attempt to solve the very problem that doomed its predecessor: transforming that disruptive boom into a gentle thump no louder than a car door closing and thus enabling the prospect of supersonic travel for inland flights. This dramatic evolution underscores why aeroacoustics has become critical to modern aerospace design.
But the challenge extends far beyond supersonic flight. From the persistent hum of aircraft engines that shapes community noise regulations, to the whistling turbulence around automotive side mirrors that degrades passenger comfort, to the tonal whine of HVAC systems in commercial buildings, aerodynamic noise influences everything from regulatory compliance to customer satisfaction. Traditional experimental testing of these noise sources is prohibitively expensive and often impossible during early design stages. This is where Computational Fluid Dynamics (CFD), specifically with PowerFLOW, has emerged as an indispensable tool, enabling engineers to predict, visualize, and mitigate flow-induced noise before the first prototype is ever built.
In this blog, let’s take a look at the fundaments of aeroacoustics, some key definitions, and how CFD enables aeroacoustics prediction and design.
What is Aeroacoustics?
Aeroacoustics is the branch of acoustics that studies sound generated by airflow and the interaction between fluid motion and solid surfaces. Unlike traditional acoustics, which focuses on how sound propagates through a medium, aeroacoustics focuses on how moving air creates and propagates noise.
Aerodynamic noise has evolved from just being a side-effect to a formal design constraint. Regulatory requirements impose strict noise limits in many industries; aircraft must meet community noise standards around airports, HVAC systems must comply with building and environmental codes, and automotive designs are subject to pass-by noise regulations. Failing to meet these limits can delay certification, restrict where a product can be used, or even prevent it from reaching the market altogether. Beyond compliance, customer comfort and perception play a major role. People are highly sensitive not just to how loud something is, but to the quality of the sound. For example, tonal noise from a fan or high-frequency whine from electronics can be far more annoying than broadband noise at the same level. This directly impacts user satisfaction, especially in consumer products, vehicles, and indoor environments. As a result, companies increasingly treat acoustic performance as part of the overall user experience, and a core constraint on engineering design.
Fundamentals of Aeroacoustics
What is Sound in a Fluid?
At its core, sound in a fluid is a propagating disturbance in pressure. When a flow becomes unsteady due to turbulence, separation, or interaction with geometry, it generates small fluctuations in pressure and density. These fluctuations travel through the fluid as waves at the local speed of sound. Unlike the bulk motion of the fluid (which transports mass and momentum), acoustic waves carry energy without significant net mass transport.
Key Characteristics of Sound
To analyze and quantify sound, we typically describe it using three primary properties:
- Frequency (f)
This defines how rapidly the pressure oscillates (cycles per second, in Hz). It determines the pitch of the sound. For example, a high-frequency noise (like an electronic whine) is perceived as sharp, while low-frequency noise (like a large fan) sounds deeper. - Wavelength (λ)
The physical distance over which one full pressure cycle occurs. It is related to frequency through the speed of sound,
where 
is the speed of sound. Wavelength is especially important in CFD because it determines how fine your mesh must be to resolve acoustic waves.
- Sound Pressure Level (SPL)
The strength of the pressure fluctuation determines how loud the sound is. In practice, this is expressed in decibels (dB), a logarithmic scale that reflects how humans perceive loudness.
Where p` is the pressure fluctuation amplitude, and pref is the reference pressure (usually 20μPa in air). Some typical reference SPL values are given below:
- 0 dB – Threshold of human hearing (ideal lab condition)
- 20–30 dB – Quiet bedroom / rural night ambience
- 40 dB – Quiet office or residential background noise
- 60 dB – Normal conversation at 3 feet
- 70 dB – Busy street traffic
- 80 dB – Vacuum cleaner / heavy city traffic close by
- 90 dB – Lawn mower / industrial machinery
- 100 dB – Motorcycle / very loud factory environment
- 110 dB – Aircraft takeoff at moderate distance
- 120+ dB – Jet engine close range / pain threshold
- Overall Sound Pressure Level (OASPL)
Overall Sound Pressure Level (OASPL) represents the total acoustic energy of a signal integrated over all frequencies. Instead of examining individual spectral components, OASPL collapses the entire pressure fluctuation signal into a single scalar value, making it useful for comparing overall noise levels between designs or operating conditions. It is based on the RMS of the full pressure time history:
- A-Weighted Sound Pressure Level (A-SPL or dBA)
A-weighted SPL (commonly written as dBA) is a perceptual version of SPL that adjusts the raw acoustic signal to account for the sensitivity of the human ear. Human hearing is less sensitive to very low and very high frequencies, so an A-weighting filter is applied before computing the SPL. The result is a more realistic representation of how loud a sound feels to a human listener rather than its pure physical intensity. The calculation is still based on the SPL formula, but applied after frequency-dependent weighting
where p`A is the A-weighted RMS pressure.
Sources of Noise Generation in a Flow
Aeroacoustic noise is primarily generated by unsteady, nonlinear flow phenomena. The following flow features are some of the most dominant sources of aeracoustic noise.
Turbulence
Turbulent flows are inherently chaotic, containing a wide range of eddy sizes and time scales. These random fluctuations create pressure disturbances across a broad spectrum of frequencies, leading to broadband noise. This is common in jets, wakes, and high-speed internal flows.
Vortex Shedding
When flow passes over bluff bodies (like cylinders or sharp edges), vortices are shed in a periodic manner. This creates a repeating pressure signal at a characteristic frequency, often described using the Strouhal number. The result is a tonal noise, such as the classic “whistling” of wind past a structure.
Boundary Layer Interactions
As fluid flows along a surface, a boundary layer develops. When this boundary layer interacts with sharp edges (like the trailing edge of an airfoil) or surface roughness, it generates pressure fluctuations that radiate as sound. This is a major source of noise in airfoils, fans, and automotive components.
Flow Separation
When the flow detaches from a surface, it forms large-scale, unsteady structures. These separated regions can produce strong, low-frequency pressure fluctuations that contribute significantly to noise, especially in bluff bodies and stalled airfoils.
Categories of Aeroacoustic Noise
Although the underlying physics can be complex, aeroacoustic noise is often grouped into two main categories:
Tonal Noise
- Characterized by distinct, narrow frequency peaks
- Associated with periodic or coherent flow structures
- Common sources:
- Blade Passing Frequency (BPF) in fans and compressors
- Vortex shedding from bluff bodies
- Often perceived as more annoying due to its pure, repetitive nature
Broadband Noise
- Spread over a wide range of frequencies
- Generated by random, turbulent fluctuations
- Common sources:
- Fully developed turbulence
- Shear layers and wakes
- Typically sounds like “whooshing” or “hissing”
How PowerFLOW is Used for Aeroacoustics Prediction
CFD plays a central role in modern aeroacoustics because it allows engineers to resolve the unsteady flow structures that generate sound and then predict how that sound propagates into the far field. In practice, CFD-based aeroacoustic analysis is usually a two-step process: (1) resolve the unsteady flow field, and (2) compute the resulting acoustic field using an acoustic analogy or propagation model.
Resolving the Source/Near Flow Field
Unlike steady CFD simulations focused on mean forces or pressure drops, aeroacoustic simulations require time-resolved flow data. This is because sound is fundamentally linked to unsteady pressure fluctuations. Depending on the frequency range of interest and computational cost, engineers typically use:
- LES (Large Eddy Simulation) for high-fidelity broadband noise prediction
- DES / DDES (Detached Eddy Simulation) for a balance between cost and accuracy
- URANS (Unsteady RANS) for lower-frequency tonal content, though with limitations in broadband accuracy
The key output from these simulations is a time history of pressure and velocity fields, which serve as inputs to acoustic prediction methods.
Computing the Far-field Acoustic Prediction
Once the source field is computationally resolved using one of the techniques mentioned above, the far-field noise is computed using an acoustic analogy.
One of the most widely used approaches for far-field aeroacoustic prediction is the Ffowcs Williams-Hawkings (FW-H) analogy. This method reformulates the compressible Navier-Stokes equations into an acoustic wave equation with source terms representing different physical noise mechanisms.
In simplified form, the FW-H equation can be written as:
where:
- Q represents monopole (thickness) sources
- F represents dipole (loading) sources
- T represents quadrupole (volume turbulence) sources
A major advantage of FW-H is that it avoids directly simulating acoustic waves in the entire domain, which would otherwise require extremely fine meshes and prohibitively small time steps due to the large disparity between flow and acoustic length scales.
Final Thoughts
With modern engineering systems becoming increasingly constrained by noise regulations and user comfort requirements, aeroacoustic performance is no longer a secondary consideration, it is a core design driver. This is where CFD has become indispensable. By resolving the unsteady flow field and coupling it with acoustic analogies such as FW-H, engineers can predict not only where noise is generated, but also how it propagates and what it will sound like in real-world conditions.
However, the accuracy of aeroacoustic prediction is tightly linked to the fidelity of the underlying flow simulation. Capturing the correct turbulent structures, wake dynamics, and unsteady pressure fields is essential to producing reliable acoustic outputs.
At Fidelis, we support aeroacoustic analysis workflows by combining high-fidelity CFD expertise with advanced simulation tools such as PowerFLOW. PowerFLOW is particularly well-suited for aeroacoustic applications because it is based on the Lattice Boltzmann Method (LBM), which naturally resolves transient, compressible flow phenomena and captures fine-scale turbulence structures with high temporal accuracy. This makes it especially effective for predicting broadband noise and resolving coherent structures responsible for tonal noise generation. When coupled with PowerACOUSTICS and PowerVIZ, the combination provides visualization capabilities to identify noise sources and provides insight into how design adjustments affect noise output so you can optimize your engineering design for noise constraints and certify whether it meets noise-level regulations and requirements.
Need help with aerodynamic noise reduction and optimization? Get in touch with our expert team today!
