Turbulent Pressure Fluctuations Research Articles

Vibration Characteristics and Structural Optimization of Pipeline Intelligent Plugging Robot under Turbulent Flow Field Excitation

Pipeline maintenance technology based on pipeline intelligent plugging robot (PIPR) has become an effective method for failure accident prevention of high-pressure subsea oil and gas pipelines. However, during the plugging operation, the vortexes and pressure fluctuation are presented under turbulent flow field excitation, which may lead to vortex-induced vibration and failure of the plugging operation. Therefore, in order to ensure the reliability of pipeline plugging, the vibration characteristics are analyzed using numerical simulation, providing guidance on the structural optimization of PIPR’s end face. Firstly, the flow field characteristics under different PIPR’s end faces are investigated. Secondly, an experimental scheme is designed based on Latin Hypercube Sampling Design (LHSD) optimized by greedy strategy. A mathematical model of the end face’s parameters and pressure gradient is established using a back propagation (BP) neural network. Then, an improved whale optimization algorithm (IWOA) is proposed to optimize the end face’s parameters to minimize the pressure gradient of the flow field. Finally, the experimental study is performed to observe the turbulent flow field and pressure fluctuation to validate the optimization results. The results demonstrate that the PIPR’s end face has a great influence on the vortex-induced vibration response. After structural optimization, the average pressure gradient of the optimal PIPR’s end face has decreased by 84.69% and 54.55% before and after the plugging process, compared to the original end face. This study can provide a reference for pipeline plugging operations, which is significant for preventing pipeline failure accidents.

Estimating infrasonic noise levels using a high resolution weather model

The presence of turbulence and other wind-induced pressure fluctuations are considered a nuisance in infrasound monitoring. For this reason, wind noise filters are typically in place for suppression. In this study, we establish a relation between microbarometer observations and in situ turbulence measurements at the Cabauw Atmospheric Research Site in The Netherlands. Using this relation, we forecast turbulent pressure levels using forecasted levels of turbulence kinetic energy (TKE) that are computed as part of the high-resolution (2.5 × 2.5 km grid scale) HARMONIE weather model. The estimates are compared with pressure noise levels as a function of frequency. This approach has two foreseen applications: (1) the forecasted turbulence fields could possibly help in site selection for infrasound arrays and (2) microbarometer observations could possibly be of use in the further refining of sub-grid scale turbulence schemes in weather models.

Investigation of the unsteady pressure fluctuation mechanism in a regenerative flow pump based on proper orthogonal decomposition

The regenerative flow pump (RFP) derives its name from the circular flow pattern akin to a vortex. However, the presence of numerous vortices results in highly turbulent flow and pressure fluctuation. This study delves into the characteristics of the internal unsteady flow in two distinct RFP models featuring different blade shapes. Numerical simulations are employed to obtain the pressure field, which is subsequently scrutinized using the proper orthogonal decomposition (POD) method. The results show that the average pressure in the peripheral direction undergoes minimal variation during the flow developing stage, experiences a sharp increase in the fully developed stage, and finally exhibits substantial changes in the stripper. The transient pressure in the time domain fluctuates periodically, and the dominant blade passing frequency in the frequency domain demonstrates a similar trend along the circumferential direction as the average pressure. Moreover, the fluctuating intensity of pressure diminishes along the impeller's rotating direction but intensifies significantly in the stripper. Comparative analysis indicates that the exchange intensity is influenced by the flow at different developmental stages, and the exchange flow conditions could reflect the fluctuating intensity. Furthermore, the study reveals that the frequency amplitude of the time coefficient gradually decreases as the mode order increases. The first and second modes exhibit a gradually changing trend associated with pressure increase patterns, whereas the third and fourth modes highlight the emergence of localized modulation phenomena linked to exchange flow. Thus, the POD method offers a unique perspective for comprehending the flow mechanisms within RFPs.

Performance of three hydrophone flow shields in a tidal channel.

Pseudosound caused by turbulent pressure fluctuations in fluid flow past a hydrophone, referred to as flow noise, can mask propagating sounds of interest. Flow shields can mitigate flow noise by reducing non-acoustic pressure fluctuations sensed by a hydrophone. We evaluate the performance of three hydrophone flow shields (two nylon fabrics and an oil-filled enclosure) in a tidal channel with peak current speed of 1.3 m s-1. All three flow shields reduced flow noise without attenuating propagating sound below 20 kHz. The oil-filled enclosure performed best, reducing flow noise by over 30 dB at frequencies below 40 Hz.

LSTM Reconstruction of Turbulent Pressure Fluctuation Signals

This paper concerns the application of a long short-term memory model (LSTM) for high-resolution reconstruction of turbulent pressure fluctuation signals from sparse (reduced) data. The model’s training was performed using data from high-resolution computational fluid dynamics (CFD) simulations of high-speed turbulent boundary layers over a flat panel. During the preprocessing stage, we employed cubic spline functions to increase the fidelity of the sparse signals and subsequently fed them to the LSTM model for a precise reconstruction. We evaluated our reconstruction method with the root mean squared error (RMSE) metric and via inspection of power spectrum plots. Our study reveals that the model achieved a precise high-resolution reconstruction of the training signal and could be transferred to new unseen signals of a similar nature with extremely high success. The numerical simulations show promising results for complex turbulent signals, which may be experimentally or computationally produced.

Physical consistency and invariance in machine learning of turbulent signals

This paper concerns an investigation of the invariance and consistency of deep learning of turbulent pressure fluctuations. The long-short-memory model is employed to predict wall pressure fluctuations across physical regimes featuring turbulence, shock–boundary layer interaction, and separation. The model's sensitivity to the data inputs is examined using different input data sets. Training the deep learning model based on the raw signals from different flow regions leads to large inaccuracies. It is shown that the data must be appropriately pre-processed before training for the deep learning model predictions to become consistent. Removing the mean and using the normalized fluctuating component of the signal, the deep learning predictions not only greatly improved in accuracy but, most importantly, converged and became consistent, provided that the signal sparsity remains within the inertial sub-range of the turbulence energy spectrum cascade. The power spectra of the surface pressure fluctuations reveal that the model provides high accuracy up to a certain frequency for the fully turbulent flow. The deep learning model's consistency is evidenced by being transferable across the various probe positions on the wall despite the significant differences in the turbulent flow properties in the training data set, i.e., signals obtained before, after, and inside the shock–boundary layer interaction regions. The model's prediction consistency and invariance to the turbulent signal training location(s) are promising for applying deep learning models to various turbulent flows.

Thermal Interference When Recording Turbulent Pressure Fluctuations on the Surface of a Floating Device

Thermal interference is studied when recording turbulent pressure fluctuations on the surface of a floating device at specified experimental parameters of temperature stratification of an aquatic medium. The effect of distortion of the spectral levels of pressure fluctuations recorded by a sound receiver in the field of temperature inhomogeneities is studied using the example of measurements of turbulent pressure fluctuations in the boundary layer during the vertical ascent of a device from a specified depth. At moderate flow velocities exceeding 1–2 m/s, the temperature susceptibility of a piezoceramic receiver is shown to be decisively determined by its characteristic “thermal” frequency. The parameters of the threshold critical frequency, below which the temperature signal (thermal interference) prevails over the useful signal generated by pressure fluctuations, are determined. With respect to the receivers used in experiments on a floating device [7], the values of the threshold critical frequency are 130 and 215 Hz.

Thermal Interference When Recording Turbulent Pressure Fluctuations on the Surface of a Floating Device

Thermal interference is studied when recording turbulent pressure fluctuations on the surface of a floating device at specified experimental parameters of temperature stratification of an aquatic medium. The effect of distortion of the spectral levels of pressure fluctuations recorded by a sound receiver in the field of temperature inhomogeneities is studied using the example of measurements of turbulent pressure fluctuations in the boundary layer during the vertical ascent of a device from a specified depth. At moderate flowvelocities exceeding 1–2 m/s, the temperature susceptibility of a piezoceramic receiver is shown to be decisively determined by its characteristic “thermal” frequency. The parameters of the threshold critical frequency, below which the temperature signal (thermal interference) prevails over the useful signal generated by pressure fluctuations, are determined. With respect to the receivers used in experiments on a floating device [7], the values of the threshold critical frequency are 130 and 215 Hz.

Statistics of pressure fluctuations in turbulent kinetic plasmas

ABSTRACT In this study, we explore the statistics of pressure fluctuations in kinetic collisionless turbulence. A 2.5D kinetic particle-in-cell simulation of decaying turbulence is used to investigate pressure balance via the evolution of thermal and magnetic pressure in a plasma with β of order unity. We also discuss the behaviour of thermal, magnetic, and total pressure structure functions and their corresponding wavenumber spectra. The total pressure spectrum exhibits a slope of −7/3 extending for about a decade in the ion-inertial range. In contrast, shallower −5/3 spectra are characteristic of the magnetic pressure and thermal pressure. The steeper total pressure spectrum is a consequence of cancellation caused by density-magnetic field magnitude anti-correlation. Further, we evaluate higher order total pressure structure functions in an effort to discuss intermittency and compare the power exponents with higher order structure functions of velocity and magnetic fluctuations. Finally, applications to astrophysical systems are also discussed.

Estimation of wavenumber–frequency spectra of wall pressure due to turbulent flow over a flat plate using large-eddy simulation

The wavenumber–frequency spectra (WFS) of turbulent wall pressure fluctuations are used to design underwater acoustic systems, and a method is presented to determine them for flat plate flow using large-eddy simulations (LES). First, the Reynolds averaged Navier–Stokes (RANS) analysis is carried out for flow over the plate using k–ω shear-stress transport model. Flow parameters are extracted from the output of this analysis and used to impose boundary conditions for large-eddy simulations using a smaller domain with finer mesh. Mesh convergence studies are done to establish the adequacy of the proposed meshing scheme for estimating the turbulent boundary layer wall pressures. The stream-wise velocity profiles, turbulence intensities, and power spectra obtained using LES are compared with other computational and experimental results. The time history of fluctuating pressure on the wall at various stream-wise locations is used to estimate the WFS. Estimations are made of the convective ridge as well as sub-convective and low wavenumber portions of the spectrum. The computations are performed at momentum thickness-based Reynolds numbers (Rθ) of 5761, 7988, and 7709. The effect of downstream distance on the WFS is studied by computing it at three downstream locations and shown that the downstream distance has little effect on the WFS once the flow has become fully turbulent. The use of a desktop workstation for the estimation of WFS has not been reported earlier. The results show that the WFS of turbulent pressure due to flow over more complex geometries can be estimated using a similar method.

SPH modelling of hydraulic jump at high Froude numbers at an abrupt drop: vorticity and turbulent pressure fluctuations

SPH modelling of hydraulic jump at high Froude numbers at an abrupt drop: vorticity and turbulent pressure fluctuations

Noise Reduction on a Model Helicopter Rotor Using Dielectric Barrier Discharge Plasma Actuators

Noise reduction was studied on a model helicopter rotor using pulsed dielectric barrier discharge (DBD) plasma actuators mounted at the blades’ leading edges. The evaluation was performed in a fully anechoic chamber, where thrust and torque were measured concurrently with the acoustic signature using an array of 15 microphones. Acoustic measurements were made for both baseline and actuated cases at a fixed collective pitch angle under conditions of constant thrust. Actuation produced broadband and tonal noise reductions of up to 8 and 3 dB, respectively, while simultaneously increasing the rotor figure of merit by 25%. Broadband noise reductions were due to control of the otherwise separated boundary layer, resulting in smaller and weaker turbulent eddies in the wake and the tip-vortex and, therefore, smaller turbulent surface pressure fluctuations. Noise reduction directivity associated with the blade passing frequency of up to 3 dB was consistent with rotational noise theory. At most of the observer angles, both the plasma pulsation and ionization frequencies were not visible in the spectra above the motor self-noise signature. Peaks in the second harmonic were observed at specific observer angles and were assumed to be due to the geometric mounting of the actuator and amplification of the pulsations in the laminar separation bubble.

Research on wake and potential flow effects of rotor–stator interaction in a centrifugal pump with guided vanes

In this paper, the wake and potential flow effects of the rotor–stator interaction in a centrifugal pump with guide vanes are investigated from the view of the separation of turbulent and acoustic pressure fluctuations. The highest vibration levels in pumps are, in general, originated in the potential flow and wake effects. However, it is challenging to distinguish their effects on flow evolution. The pellicular mode decomposition method is applied to innovatively separate potential flow and wake disturbances in a centrifugal pump. By pellicular, we mean an infinitely thin layer of air located on the monitoring surface. The pellicular modes are a set of acoustic modes, with which a set of normalized orthogonal basis can be constructed. The impacts of potential flow and wake disturbances are visualized and evaluated quantitatively. The results show that only a very limited region is where the potential flow disturbance works. The higher the harmonics, the smaller the disturbance range. The wake disturbance is responsible for the modal pressure field. Modal pressure fields with low diametrical nodes decay more slowly than those with low harmonics. In addition, special attention is paid to the impact of the geometric asymmetry of the volute on the impeller force. The circular volute with a two-stage pressure drop improves the radial force of the impeller. More deep understandings on the mechanism of the rotor–stator interaction are reached by decoupling the potential flow and wake disturbances. This work serves as a guide for further research in fault diagnosis and vibration control of centrifugal pumps.

Pseudospectrum-based methods for estimating the wind speed and direction based on closely spaced microphone signals

Acoustic array processing can be employed to measure the wind speed and direction based on microphone signals. Turbulent pressure fluctuations picked up by microphones are referred to as wind noise. According to Taylor's frozen turbulence hypothesis, turbulent eddies retain their shape while advecting at nearly the mean wind speed and in the wind direction. It follows that wind noise propagates accordingly across a microphone array when the inter-microphone distance is smaller than the turbulence wavelength. This property can be exploited to track the orientation of the turbulence advection, and hence to characterize the wind flow. We propose beamforming and signal subspace-based methods to estimate the wind speed and direction using a compact microphone array. In particular, the pseudospectrum of measured wind noise is computed against candidate pairs of wind speed and direction. The wind speed and direction estimates are then obtained as the maximizers of the pseudospectrum. In addition, we extend an existing time difference of arrival-based method originally derived for three microphones to an arbitrary number of microphones. We evaluate the estimation accuracy of the proposed methods separately for the wind speed and direction. Possible applications include highly integrable, portable, and inexpensive anemometers for smart sensors or action cameras.

Measurements of turbulent wall pressure fluctuation field in a turbulent boundary layer and the wing-flat plate juncture flow using the many-channel microphone array

Measurements of turbulent wall pressure fluctuation field in a turbulent boundary layer and the wing-flat plate juncture flow using the many-channel microphone array

Tip vortices shed by a hydrofoil in the wake of a marine propeller

Large-eddy simulations on a grid consisting of 3.8 billion points are reported, dealing with a system composed of a propeller and a downstream semi-infinite hydrofoil, mimicking a propeller–rudder system typical of surface ships. The analysis is focused on the tip vortices shed by the hydrofoil across four values of incidence angle. The results of the simulations highlight the generation of two vortices, from the pressure and suction edges of the tip of the hydrofoil, respectively, with the former more intense than the latter. They promote higher turbulence levels and pressure fluctuations at the tip of the suction side of the hydrofoil and especially at its bottom end, while the pressure side is almost unaffected. For large incidence angles, they join in the near wake of the system into a single streamwise-oriented structure. This dominates the wake signature. It is a location of minima of pressure and maxima of turbulence, which affect performance in terms of cavitation and noise. Large levels of turbulent kinetic energy are also achieved in the region of shear of this vortex with the tip vortices shed by the propeller and coming from the pressure side of the hydrofoil, rolling around the vortex from the tip of the hydrofoil. In contrast, at no incidence or for small angles, the wake of the system is dominated by the shear layer shed from the trailing edge of the hydrofoil and by the hub and tip vortices populating the wake of the propeller.

Response regimes in the fluid–structure interaction of wall turbulence over a compliant coating

The interaction between a turbulent boundary layer flow and compliant surfaces is investigated experimentally. Three viscoelastic coatings with different material stiffnesses are used to identify the general surface response to the turbulent flow conditions. For the softest coating, the global force measurements show two obvious regimes of interaction with an indicated transition at $U_b/C_t\sim 3.5$ , where $U_b$ is the bulk flow velocity and $C_t$ is the coating shear velocity. The one-way coupled regime shows friction values comparable to those of the rigid wall, while the two-way coupled regime indicate a significant increase in fluid friction. Within the one-way coupled regime for $U_b/C_t>1.2$ , the flow measurements show a low level of two-way coupling represented by the change of the velocity profile as well as the increase in the Reynolds stresses in the near-wall region. This is supported by the surface deformation measurements. Initially, the turbulent flow structures induce only an imprint on the coating surface, while a change in surface response occurs when the surface wave propagation velocity $c_w$ equals the shear wave velocity of the coating $C_t$ (i.e. $c_w/C_t\sim 1$ ). Above $U_b/C_t>1.2$ , a growth in wavelength is observed with increasing flow velocity, most probably due to the surface wave formation generated downstream the pressure features of the flow. The surface response is stable and correlates with the high-intensity turbulent pressure fluctuations in the turbulent boundary layer, with a wave propagation velocity $c_w\sim 0.7\unicode{x2013}0.8$ $U_b$ . Within the two-way coupled regime, additional fluid motions and a downward shift in the logarithmic region of the velocity profile are observed due to substantial surface deformation and confirm the frictional drag increase. Another type of surface response is initiated by phase-lag instability in combination with surface undulations that start to protrude the viscous sublayer, where the propagation velocity of surface wave trains is $c_w\sim 0.17\unicode{x2013}0.18$ $U_b$ .

Effects of form-induced velocity in rough-wall turbulent channel flows

Wall roughness induces form-induced (or dispersive) velocity and pressure perturbations inside the roughness sublayer of a wall-bounded turbulent flow. This work discusses the role played by the form-induced velocity in influencing turbulence statistics and structure, using existing direct numerical simulation data of transient half channels in response to an impulse acceleration (Mangavelli et al. Effects of surface roughness topography in transient channel flows. J Turbul 2021;22:434–460). Focuses are given to (i) reshaping of turbulent coherent motions by the rate-of-strain of the mean velocity, and (ii) contributions of different velocity sources to turbulent pressure fluctuations. Half-channel flows in both fully-developed and non-equilibrium, transient states are discussed. Results show that form-induced velocity gradients not only form an important source of turbulent pressure in an equilibrium flow, but also lead to turbulence production and potentially direct structural change of turbulent eddies in a non-equilibrium flow under acceleration.

From atmospheric profile statistics to transmission loss statistics

Infrasound propagation depends critically on the temperature and wind velocity profiles at the time of signal propagation. These vary wildly, showing at best qualitative systematic behavior. It follows that any predictions of signal detection capability are necessarily statistical in nature. One approach is to collect a large number of historical temperature and wind velocity profiles, for a specific time and location, and use this as a sample space from which to generate a statistical model for the atmosphere as a propagation medium. To generate a statistical model for the expected transmission loss one must run a propagation model through a sampling of atmospheric profiles sufficient to reproduce the statistical behavior. The sampling is done using an Empirical Orthogonal Function (EOF) decomposition. The advantages of the use of an EOF decomposition will be discussed and examples from transition zone (ranges less than 100 km) and regional (ranges of several hundreds of kilometers) will be presented. Given a model for the turbulent pressure fluctuation levels near a receiving array, signal detection probability functions can be estimated.

Response of a plate with piezoelectric elements to turbulent pressure fluctuation in supersonic flow

The aeroelastic response of a plate with supersonic freestream flow on one side and a shallow cavity on the other to turbulent pressure fluctuations is investigated computationally and experimentally. An empirical model is developed for the pressure fluctuations in a turbulent boundary layer that accounts for spatial and spectral variations in the pressure field. Supersonic wind tunnel tests were conducted in a Mach 2.5 flow with and without an impinging shock at the plate surface. In both cases the boundary layer was turbulent. The impinging shock creates shock-wave boundary-layer interaction, which alters the characteristics of the pressure fluctuations. Pressure-sensitive paint was used to measure the unsteady pressure on the surface of a rigid plate and characterize the pressure field (local mean, rms, and the spatial coherence length) and piezoelectric patches were used as sensors to measure the response of an elastic plate. The extracted pressure parameters were used to simulate the fluid–structure response and correlate with experiments. The computed pressure perturbation due to plate motion is found to be small relative to the natural pressure fluctuation for the fluid/structural configuration studied. Computed and measured power spectra of the piezoelectric element voltage show good agreement over a wide range of structural natural frequencies. Aeroelastic response sensitivity to pressure fluctuation coherence length was also investigated computationally. It is found that with small fluid elements, which represent small-scale uncorrelated noise, the structural response is relatively small because the excitation is filtered by the plate dynamics. Experimental results suggest that the effective excitation spatial scale is on the order of the boundary layer thickness.