1. Introduction
Cavity flow is found in various industrial applications, and numerous researchers have utilized various experimental and numerical methods to investigate the mechanism involved in such flows since the 1950s [
1,
2]. The aero-acoustic mechanism in such flows results from a coupling between the surrounding acoustic modes and the aerodynamic modes. The flow instability present in the cavity shear layer generates pressure waves upon its impact on the trailing corner of the cavity. The generated pressure waves travel the flow upstream (forming a feedback loop) to control the shear layer near its formation at the cavity’s leading corner. The high acoustic levels inside a cavity, or resonance, and the flow fluctuations are caused by the coupling between periodic oscillations of the flow shear layer and the acoustic modes of the surrounding geometry [
3,
4,
5,
6,
7,
8,
9,
10]. Therefore, several approaches have been attempted to explore the impact of these fluctuations on flow properties, including drag and heat transfer [
11,
12].
The highly unsteady flow field, the oscillating shear layer, and the production of acoustic waves are fundamental characteristics of cavity flows. However, cavities were classified in different ways, based on the incoming flow, their geometry or even by the static pressure distribution. Cavity flow has been observed over a wide variety of geometries, Reynolds and Mach numbers. This led to one of the initial classifications, proposed by Charwat et al. [
13], who demonstrated that cavities can be categorized as either open or closed. Rossiter [
14] added a third category known as transitional. This classification was based on the differences in aspect ratio and the static pressure distribution for each category. However, open cavities can be categorized as deep and shallow cavities. Based on earlier experiments [
15,
16,
17], deep cavities have a depth greater than their length (L/H<1), while shallow cavities have aspect ratio greater than 1 (L/H>1). This paper focuses on the open deep cavity type, characterized (aspect ratio L/H< 10 [
15]). All cavities within this range share a common characteristic; with a boundary layer separating at the upstream corner and reattaching at the downstream edge.
Rockwell et al. [
18] stated that both shallow and deep cavities exhibit fluid oscillations know as self-sustaining oscillations. For deep cavities, a forcing mechanism acts in the shear layer with resonant waves propagating in the transverse direction. This may induce further Aero-Acoustic couplings that lead to resonance [
19,
20,
21]. On the other hand, shallow cavities are associated with longitudinal acoustic standing waves [
22]. Experiments have also shown that propagating disturbances also affect the shear layer of shallow cavities at low subsonic speeds [
23].
A reference model for predicting self-sustained cavity oscillations was done by Rossiter [
24] who developed a semi-empirical analytical model to evaluate the proper frequency of the flow. The production of a high level of noise is often the result of coupling between an acoustic resonance and a certain periodic instability in the flow. Indeed, a portion of the energy is extracted from the flow to sustain the acoustic oscillation. The model proposed by Rossiter evaluates the fundamental frequency of the flow over a cavity, based on a comprehensive description of the interaction between the mixing layer and the acoustic waves. The expression of the Rossiter model is given by:
Where is the Strouhal number, is the acoustic frequency, n is the cavity mode, L is the length of the cavity, U0 is the free stream velocity, M represents the Mach number, represents the ratio between the velocities of the external flow and that of the structures convection in the shear layer, α represents the delay in time between the impingement of the vortices and the creation of an acoustic disturbance. The parameter α is considered an empirical value and is corrected based on experiments.
In addition to properly analyzing cavity flow, it is highly important to propose control methods that can eliminate resonance without significant energy cost or a substantial increase in drag. Therefore, numerous control methods have been proposed by various authors [
25]. These methods include passive control techniques, which involve altering the cavity geometry [
26,
27] or adding external devices, particularly on the leading edge (such as spoilers [
24] or cylinder [
28,
29], etc.). These methods have shown their effectiveness in eliminating resonance and modifying the flow behavior in the shear layer. Furthermore, some limitations of passive control in certain applications have drawn attention to active control techniques, which involve the use of devices requiring external energy. Active control techniques can be classified as either open-loop or closed-loop, and they demonstrate significant potential in achieving attenuation [
30].
In this study we utilized the same configurations used by El Hassan et al. [
28], where two passive control techniques were employed: the use of a cylindrical cylinder and the use of a profiled cylinder. The reduction of noise level using such a device has been studied by Stanek et al. ([
31,
32,
33]) for subsonic and supersonic flows. These authors propose the following explanation for the effectiveness of the rod: the high-frequency forcing stabilizes the hydrodynamic stability of the flow and thus reduces the pressure levels inside the cavity. However, this is just one hypothesis among others, as the addition of the rod involves complex, highly nonlinear physics, and several mechanisms in the suppression of pressure fluctuations. Both passive and active control methods were used to attenuate cavity resonance [
34]. The control of a cavity flow has been studied to a limited extent for a deep cavity (El Hassan et al. [
39]). In most studies, the flow velocity was high (applied to military aviation). At relatively low velocities, the control of deep cavity flow finds application in the automotive and railway fields.
Proper Orthogonal Decomposition (POD), presented by Lumley in 1967 [
35], involves decomposing the random vector field into a set of functions that effectively represent the turbulent motion and organization of the flow through POD modes. It enables the capture of the flow’s total fluctuating kinetic energy [
36]. Over the years, various POD methods have been developed for numerous fluid dynamic applications [
37,
38,
39,
40], including cavity flow [
41,
42,
43]. Overall, these studies highlight the diverse applications of POD in analyzing cavity flows, showcasing its efficacy in capturing dominant flow features, identifying coherent structures, and investigating flow instability. By extracting crucial information on flow features such as frequency, amplitude and spatial distribution, POD enables researchers to understand the impact of design parameters on these flow characteristics. However, one of the main objectives of utilizing POD in the case of deep cavity flows is to gain insights into the flow physics of the complex flow phenomena. This involves analyzing and identifying coherent structures and recirculating zones that play an important part in describing the flow behavior. Understanding the flow mechanism leading to flow separation, turbulence, and drag is of utmost importance in deep cavity flow analysis.
In this study, a circular cylinder and a profiled cylinder were employed to alter the Aero-Acoustic resonance within a deep, large cavity subjected to low subsonic flow. Hot wire and pressure measurements were conducted to investigate the acoustic resonance of the cavity. Particle Image Velocimetry (PIV) technique is employed to investigate the flow dynamics in both cases of the circular and profiled cylinders. Examination of the spatio-temporal development of vortical structures is derived from consecutive snapshots. Statistical support for interpreting the primary mechanisms is provided through both spatio-temporal cross-correlation maps and Proper Orthogonal Decomposition (POD).
2. Materials and Methods
2.1. Cavity and Control Mechanism
A deep cavity inside a closed-circuit wind tunnel of a cross-sectional area of 2×2 m2 and a length of 10 m (test section) and allowing a maximum velocity of 60 m/s was used to conduct our experiments. In this study, a freestream velocity of U0 = 43 m/s was used [
44].
The geometry of the considered cavity is defined as follows: length (L) = 10.4 cm, depth (H) = 52 cm, and width (W) = 200 cm. The cavity is located on the lateral wall of the working area of the wind tunnel. Its leading corner is 8 m far from the inlet of the test section. To assess the boundary layer characteristics, velocity profiles were measured using hotwire measurements immediately upstream from the leading corner of the cavity. In the experimental setup, a cylinder of 0.6 cm in diameter was located 3 cm upstream from the cavity leading corner in the transverse direction. The cylinder was placed at a vertical position of yc = 10 from the wall. This specific positioning was selected to achieve an effective control of the resonance of the cavity. The same position was used for the profiled cylinder which has the dimensions shown in
Figure 1.
2.2. Hot-Wire Measurements
A hot wire probe (Dantec55P15), is located at x/L = 0.1 from the leading corner of the cavity. Acquisition and storage of C.T.A. signals (Constant Temperature Anemometry, DANTEC 90C10) were done thanks to the software "Streamline" from DANTEC. Through this procedure, we have a voltage signal related to the flow velocity signal at the sensor location.
2.3. Acoustic Measurements
A nominal sensitivity of Kulite sensors used for this study is 275 mV/bar. For each sensor, the output was connected to a multi-channel conditioner that allows adjusting the gain while keeping an average around zero. At the output of the conditioner, the pressure signal is transferred to an analog-to-digital acquisition card with a resolution of 12 bits. The chosen sampling frequency was 6 KHz, and the number of samples was 180,000 per channel, corresponding to an acquisition time of 30 seconds. A low-pass filter (cut-off at 3 KHz) was implemented to eliminate the aliasing effect.
2.4. PIV Velocity Measurements
Particle Image Velocimetry (PIV) offers non-invasive and highly accurate results for several flow configurations, making it an attractive choice for researchers investigating complex fluid phenomena in numerous applications.
In this study, PIV technique was employed at a sampling rate of 15 Hz, while the freestream velocity was maintained at 43 m/s. PIV differs from other flow measurement and visualization techniques in several ways. It enables the instantaneous determination of the kinematic field, offering good spatial coverage in a 2D or 3D plane but with limited temporal resolution ranging from 1/15 s to 1/1000 s, depending on the experimental setup. PIV works by seeding the flow with particles and capturing images, then analyzing these images to determine the velocity field of the fluid.
A typical PIV system includes a laser sheet that illuminates a plane in the flow, and the particles in this plane are captured by a camera pre-processed to improve the data quality, including filtering and other processing measures. Multiple particle images are then created, and these particles are tracked to determine the displacement of all particles, providing information about the flow velocity.
PIV is subjected to uncertainties in velocity measurements due to multiple sources, such as camera calibration or an incorrect choice of the time interval between two images, among other factors. This makes uncertainty analysis important for minimizing errors through the quantification of uncertainty using statistical methods.