June 26, 2016 | CFD WITH A MISSION

This is the first in a series of posts about the computational aeroacoustics that is abbreviated as CAA. Aeroacoustics is mainly concerned with the generation of sound or noise through a fluid flow. Familiar examples are

Aeolian tone
Edge tone
Cavity tone.

They can be studied by both experimental and computational methods. In an experimental approach, the aerodynamically generated sound is measured in the anechoic chamber that is designed to prevent reflections of sound on the wall, making the room echo-free. The following video created by Microsoft will help us to understand the structure of it.

The computational techniques for simulating flow-generated noise can be classified into two broad categories:

Direct Approaches

The governing equations of the compressible fluid flow is the compressible Navier-Stokes equations and they also describe the generation and propagation of acoustic noise, so we can solve the computational aeroacoustics problems by solving the transient compressible Navier-Stokes equations both in the source region where the flow disturbances generate noise and propagation region where the generated acoustic waves propagate. Acoustic waves have to be resolved in both regions so that the noise can be accurately simulated at observation locations. However, the solution of the Navier–Stokes equations with fine mesh over large domains to determine farfield noise is computationally very expensive.

As is the case in the experimental approaches, it is essential to prevent the reflection of the acoustic waves on the artificially truncated boundary (i.e. it does not stretch to infinity) of the computational domain in order to obtain an accurate result. A variety of numerical techniques have been developed for this purpose:

Navier-Stokes Characteristic Boundary Conditions (NSCBC)
Artificial dissipation and damping in an absorbing zone
Grid stretching and numerical filtering in a “sponge layer” or “exit zone”
Perfectly matched layer (PML)

In OpenFOAM, acousticDampingSource is categorized into Ⅱ and the waveTransmissive boundary condition is an approximation of NSCBC.

Hybrid Approaches (Acoustic Analogy)

David P. Lockard and Jay H. Casper [1] states that:

The physics-based, airframe noise prediction methodology under investigation is a hybrid of aeroacoustic theory and
computational fluid dynamics (CFD). The near-field aerodynamics associated with an airframe component are simulated to obtain the source input to an acoustic analogy that propagates sound to the far field. The acoustic analogy employed within this current framework is that of Ffowcs Williams and Hawkings, who extended the analogies of Lighthill and Curle to the formulation of aerodynamic sound generated by a surface in arbitrary motion.

Lighthill’s analogy

Lighthill derived the following wave equation \eqref{eq:Lighthill} from the compressible Navier-Stokes equations:
\begin{align}
\frac{\partial^2 \rho}{\partial t^2} – c_0^2 \nabla^2 \rho = \frac{\partial^2 T_{ij}}{\partial x_i \partial x_j}, \tag{1} \label{eq:Lighthill}
\end{align}
where the so-called Lighthill (turbulence) stress tensor is expressed as

\begin{align}
T_{ij} = \rho u_i u_j + \left( p-c_0^2 \rho \right)\delta_{ij} -\mu \left(\frac{\partial u_i}{\partial x_j} + \frac{\partial u_j}{\partial x_i} – \frac{2}{3}\delta_{ij}\frac{u_k}{x_k} \right), \tag{2} \label{eq:Tij}
\end{align}
\(\delta_{ij}\) is the Kronecker delta and \(c_0\) is the speed of sound in the medium in its equilibrium state.

Curle’s analogy

The existence of the objects (solid walls) is not considered in the Lighthill’s theory and Curle developed the theory so that it can be dealt with. The density variation at the observer location \(\boldsymbol{x}\) is calculated from the following equation \eqref{eq:Curle}
\begin{align}
\acute{\rho}(\boldsymbol{x}, t) &= \rho(\boldsymbol{x}, t)\;- \rho_0 \\
&=\frac{1}{4 \pi c_0^2} \frac{\partial^2}{\partial x_i \partial x_j} \int_{V}\frac{[T_{ij}]}{r}dV – \frac{1}{4 \pi c_0^2} \frac{\partial}{\partial x_i}\int_{S}\frac{[P_i]}{r}dS, \tag{3} \label{eq:Curle}
\end{align}
where
\begin{align}
P_i = -n_j \{ \delta_{ij}p -\mu \left(\frac{\partial u_i}{\partial x_j} + \frac{\partial u_j}{\partial x_i} – \frac{2}{3}\delta_{ij}\frac{u_k}{x_k} \right) \}, \tag{4} \label{eq:Pi}
\end{align}
\(r\) is the distance between the receiver \(\boldsymbol{x}\) and the source position \(\boldsymbol{y}\) in \(V\) (or on \(S\)) and the operator [] denotes evaluation at the retarded time \(t-r/c_0\).