Acoustic Pressure Data Research Articles

Numerical investigation of the thermal and acoustic effect of material variations on the exhaust muffler

Exhaust mufflers are used in automobiles to prevent the noise arising from exhaust gases resulting from internal combustion engines. With the advancement of the automotive industry, exhaust mufflers have become more complex over time to reduce noise and increase driving comfort. Within the scope of this study, exhaust muffler geometries with different geometries have been designed, and harmonic acoustic analyses have been carried out. In the analysis, the airflow speed has been accepted as 30 m/s. Acoustic pressure and transmission loss data obtained because of analyses performed with 1Pa pressure input have been evaluated. As a result of the evaluations, it has been concluded that the muffler modeled in a complex structure has been better acoustically. Although the main task of exhaust muffler is to reduce the sound level at the exit of exhaust gases, it is also important to reduce the temperature of the air in the exhaust system and have good thermal conductivity so as not to jeopardize the thermal safety of the system. For this reason, CFD thermal flow analysis has been carried out with 4 different materials using a complex design with high acoustic efficiency. Gray cast iron, stainless steel, 1020 steel, and aluminum have been used as materials. In this part of the study, it has been determined that the use of aluminum material has been better in terms of thermal efficiency.

Ocean bottom dual-sensorQ-compensated elastic least-squares reverse time migration based on acoustic and separated-viscoelastic coupling equations

The ocean bottom dual-sensor (OBD) observation system places water detectors (pressure sensors) and land detectors on the irregular seabed interface to receive acoustic pressure fields and elastic wavefields, respectively. These data are usually applied to effectively remove ghost waves and ringing interference in the seawater layer, rather than being jointly used for migration. We propose an ocean bottom dual-sensor viscoacoustic and separated-viscoelastic coupled Q-compensated least-squares reverse time migration (OBD-VSV- QLSRTM) in curvilinear coordinates. In this method, to solve the Q-compensated elastic inverse problem, we jointly use the recorded acoustic pressure and elastic multicomponent seismic data from the OBD observation system. A new misfit function in the sense of least-squares inversion is constructed based on viscoacoustic and separated viscoelastic wavefields. Acoustic and separated viscoelastic equations are used to describe the acoustic wavefield in the acoustic medium and the Q-compensated P and S wavefields in the underlying viscoelastic medium, respectively. On the acoustic-viscoelastic interface, we implement a stable governing equation to ensure continuous and steady transmission of Q-compensated stresses in the subseabed and pressure in the seawater. To address the irregular seabed problem, we mesh the acoustic-viscoelastic models onto curvilinear grids and apply coordinate transformations to map the models and equations to a new curvilinear coordinate system. We derive viscoacoustic and separated-viscoelastic coupled adjoint and demigration operators in the curvilinear coordinates, enabling linear acoustic-viscoelastic modeling and stable receiver-side back-propagated wavefield continuation. Numerical examples conducted on two typical acoustic-viscoelastic models demonstrate that our proposed OBD-VSV- QLSRTM in curvilinear coordinates can produce highly accurate P- and S-wave images efficiently.

Estimation of sediment compressional and shear properties in the New England Mud Patch using acoustic pressure and particle velocity data

New England Mud Patch was the site of the Seabed Characterization Experiment in multiple years (2017, 2021, and 2022). This site is characterized by a layer of very fine grained surficial sediment layer over sand. Analysis of broadband data measured on the ocean bottom recorders (OBXs) and hydrophones will be discussed. OBX is a geophone–hydrophone system which measures three components (two orthogonal horizontal components and a vertical component) in addition to acoustic pressure. Five OBXs were deployed on the seabed and four hydrophones were configured as a tetrahedral array on the bottom mounted Geosled. Data from these sensors will be analyzed and different arrivals corresponding to compressional, shear and interface wave (Scholte and love waves) presence will be examined. Inversion schemes will be implemented to estimate the compressional and shear wave speeds and attenuation. A priori information from seismic surveys and sediment cores will be incorporated into the inversion schemes. Outputs of these inversions will be compared with inversion results from other investigators based on different inversion techniques and using data from the same location. [Work Supported by Office of Naval Research.]

Experimental and numerical analysis of the emulsification of oil droplets in water with high frequency focused ultrasound

Focused high frequency ultrasound emulsification provides significant benefits such as enhanced stability, finer droplets, elevated focal pressure, lowered power usage, minimal surfactant usage and improved dispersion. Hence, in this study, the high frequency focused ultrasound emulsification of oil droplets in water was investigated through experiments and numerical modeling. The effect of transducer power (74–400 W), frequency (1.1 and 3.3 MHz), oil viscosity (10.6–512 mPas), interfacial tension (25–250 mN/m) and initial droplet radius (10–750 µm) on the emulsification process was assessed. In addition, the mechanism of droplet break-up was examined. The experiments showed that the acoustic pressure increased from 9.01 MPa to 26.24 MPa as the power was raised from 74 W to 400 W. At 74 W, the Weber number (We) at the surface and focal zone are 0.5 and 939.8, respectively. However, at 400 W, the We at the transducer surface and focal region reached 2.7 and 6451.8, respectively. Thus, bulb-like and weak catastrophic break up dominates the emulsification at 74 W. The catastrophic break up at 400 W is more vigorous because the ultrasound disruptive stress and We are higher. The time for the catastrophic dispersion of a single droplet at We = 939.8 and We = 6451.8 are 1.01 ms and 0.45 ms, respectively. The numerical model gives reasonable prediction of the trend and magnitude of the experimental acoustic pressure data. The surface and focal pressure amplitudes were estimated with errors of ∼ 6.5% and ∼ 10%, respectively. The predicted Reynolds number (Re) between 74 and 400 W were 8442 and 21364, respectively. The acoustic pressure at the focal region were ∼ 26 MPa and ∼ 69 MPa at frequencies of 1.1 MHz and 3.3 MHz, respectively. Moreover, the acoustic velocities were ∼ 16 m/s and ∼ 42 m/s at 1.1 MHz and 3.3 MHz, respectively. Hence, smaller droplets could be attained at higher frequency excitation under intense catastrophic modes. The Ohnesorge number (Oh) increased from 0.062 to 3.12 with the viscosity between 10.6 mPas and 530 mPas. However, the We remained constant at 856.14 for the studied range. Generally, higher critical We is required for the different breakup stages as the viscosity ratio is elevated. Moreover, the We increased from 25.68 to 1284.22 as the droplet radius was elevated from 15 to 750 µm. Larger droplets allow for higher possibility and intensity of breakup due to diminished viscous and interfacial resistance.

Optimization of a contra-rotating propeller rig for reduced psychoacoustic impact

With unmanned aerial vehicles emerging as potential alternatives for people and cargo transport, their noise impact will be a determining factor in their acceptance by the general public. Contra-rotating propeller configurations are often explored due to their improved aerodynamic performance and redundancy in case of failure compared to conventional single-propeller aircraft, but can be much noisier than their single-propeller equivalent. This work describes the optimization of a custom-made contra-rotating propeller rig for reduced psychoacoustic impact. The rig consists of two electric motors mounted on a rotating stand, positioned inside an anechoic chamber. A far-field microphone arc is used to collect acoustic pressure data, and a load cell is used to measure total thrust. The axial distance between the propellers is varied between 0.1 and 1 rotor diameters, and the number of blades is varied between 2 and 6 on both propellers. Meanwhile, the rpm is adjusted to maintain constant thrust across the different configurations. Acoustic pressure signals are investigated in terms of their physical acoustic characteristics and psychoacoustic features (such as Loudness, Sharpness, Tonality, Fluctuation Strength, Roughness and Impulsiveness) in order to determine the trade-offs and optimal choices in reducing the psychoacoustic impact of the rig.

A wavelet-based separation method for tonal and broadband components of low Reynolds-number propeller noise

Propeller noise generally exhibits a rich mixture of tonal and broadband components related to different physical mechanisms. Specifically, the tones are characterized by having deterministic and persistent characteristics, while the broadband counterpart has random behaviour. The separation is essential for the experimenters as they provide information on the different noise sources. In this framework, the study presents a novel wavelet-based method able to separate the noise emitted by a low Reynolds number propeller into its tonal and broadband components. The technique is applied to an isolated rotor operating under different loading configurations, including hover and cruise conditions. The acoustic pressure data are obtained in the anechoic tunnel (A-tunnel) of the TU Delft low-speed laboratory with a near-field polar and azimuthal distribution of microphones. The method is based upon a threshold varying procedure that separates the tonal and broadband components through the computation of two-point statistics. Advantages and drawbacks with respect to other methodologies already known from the literature are discussed. The application of the method provides the spectral content of the tonal and broadband components as well as the different polar and azimuthal directivity. Specifically, the observed dipole-like shape directivity for the tonal part and flatter broadband OASPL, confirm that the method can provide quite a good separation. Furthermore, the overall flow behaviour is inferred from the decomposition and validated through benchmarked flow visualizations.

Surface shape reconstruction from phaseless scattered acoustic data using a random forest algorithm.

Recent studies have demonstrated that acoustic waves can be used to reconstruct the roughness profile of a rigid scattering surface. In particular, the use of multiple microphones placed above a rough surface as well as an analytical model based on the linearised Kirchhoff integral equations provides a sufficient base for the inversion algorithm to estimate surface geometrical properties. Prone to fail in the presence of high noise and measurement uncertainties, the analytical approach may not always be suitable in analysing measured scattered acoustic pressure. With the aim to improve the robustness of the surface reconstruction algorithms, here it is proposed to use a data-driven approach through the application of a random forest regression algorithm to reconstruct specific parameters of one-dimensional sinusoidal surfaces from airborne acoustic phase-removed pressure data. The data for the training set are synthetically generated through the application of the Kirchhoff integral in predicting scattered sound, and they are further verified with data produced from laboratory measurements. The surface parameters from the measurement sample were found to be recovered accurately for various receiver combinations and with a wide range of noise levels ranging from 0.1% to 30% of the average scattered acoustical pressure amplitude.

Prediction of acoustic pressure of the annular combustor using stacked long short-term memory network

This paper proposes a data-driven method named stacked long short-term memory (S-LSTM) for predicting the future growth of acoustic pressure signals to detect precursors of combustion instability. The application of S-LSTM is investigated using the acoustic pressure data obtained from an annular combustor. The S-LSTM method is compared with the support vector machine (SVM) in terms of the predictive performance and also provides detailed insights into the influence of input choice by interpreting the results of S-LSTM. It is demonstrated that S-LSTM can effectively predict future pressure signals with a better error control performance compared to the SVM method. Furthermore, the feasibility of the S-LSTM in the thermoacoustic instability problem is verified using acoustic pressure data obtained from industrial combustion tests with a low-emission aero-engine. It is expected that the implementation of S-LSTM provides an early prediction solution to avoid thermoacoustic instability.

Simulations and Experimental Analysis of a High Viscosity Inkjet Printing Device Based on Fabry-Pérot Resonator.

The study investigates the effect of changing various input parameters on the pressure responses at acoustic cavities of a droplet-based acoustic printing device consisting of a Fabry–Pérot (FP) resonator and a standing wave-source chamber. The standing wave of the acoustic radiation pressure at the FP resonator is analyzed. The behavior of the standing wave and acoustic radiation force at the FP resonator is presented and compared with the measured results by varying the position of the standing wave-generating plate. The pressure changes inside the standing wave-source chamber are investigated and discussed to determine the reason for the sudden high-pressure drop at the FP resonator. Furthermore, the effects of inserting the nozzle and droplet inside the FP resonator on the standing wave and acoustic radiation force are analyzed. Experimental analysis is performed by collecting acoustic pressure data at the outlet of the FP resonator. The simulated and measured pressure drop behaviors are compared. The presented numerical approach can be used to set optimal design guidelines for obtaining a higher acoustic pressure inside the acoustic cavities of droplet-based acoustic jetting and other acoustofluidic devices.

Acoustic optimization of a contra-rotating propeller rig

With the advance of unmanned aerial vehicles as a viable alternative for urban transportation, it is imperative that these vehicles have a low impact on community noise. Contra-rotating propeller technologies offer interesting advantages over traditional single-propeller equivalents, such as improved aerodynamic performance and redundancy in case of failure, but can be exceedingly noisy when not carefully designed. This work presents a parametric study for the acoustic optimization of a custom-made contra-rotating propeller rig. The rig consists of two commercially available motors mounted on a rotating stand built in-house and mounted inside an anechoic chamber. A far-field microphone arc is used to collect acoustic pressure data, and a load cell is used to measure total thrust. We investigate the effect of variations in the number of blades (between 2 and 6 on both rotors) and axial spacing (between 0.1 and 1 rotor diameter) on the radiated noise, while adjusting the rpm to maintain constant thrust. Overall sound pressure levels, far-field spectra, and directivities (overall SPL, tones, and broadband) are assessed to determine trade-offs and optimal choices in designing a contra-rotating propeller rig.

Far-field pressure measurements of elliptic jets discharged close to a wing

This paper presents a preliminary experimental investigation into the acoustics of two elliptical jet nozzles installed close to a wing model. Acoustic pressure data is obtained for a range of observer polar angles mounted in the far field of the jets. Three nominal jet Mach numbers, namely 0.4, 0.6 and 0.8 are studied. Results suggest that the elliptic jets surveyed provide a noise reduction of the jet-surface installation noise source. The noise reduction is maximum in the forward arc and in the order of 1 dB for the fully-corrected overall sound pressure level data. Additionally, the noise benefit exists only when the minor axis of the elliptical nozzle is mounted parallel to the wing trailing edge. It is hypothesised that the reduction in the jet plume cross-section width limits the scattering of the near pressure field by the wing trailing edge to a lower frequency range. The jet mixing noise source, however, is seen to increase with decreasing nozzle exit-plane aspect ratio. The three jet velocities surveyed suggest the consistency of the key results discussed in the paper. Investigation of the jet turbulent flow structures and jet near pressure field is under way.

BeamLearning: An end-to-end deep learning approach for the angular localization of sound sources using raw multichannel acoustic pressure data.

Sound source localization using multichannel signal processing has been a subject of active research for decades. In recent years, the use of deep learning in audio signal processing has significantly improved the performances for machine hearing. This has motivated the scientific community to also develop machine learning strategies for source localization applications. This paper presents BeamLearning, a multiresolution deep learning approach that allows the encoding of relevant information contained in unprocessed time-domain acoustic signals captured by microphone arrays. The use of raw data aims at avoiding the simplifying hypothesis that most traditional model-based localization methods rely on. Benefits of its use are shown for real-time sound source two-dimensional localization tasks in reverberating and noisy environments. Since supervised machine learning approaches require large-sized, physically realistic, precisely labelled datasets, a fast graphics processing unit-based computation of room impulse responses was developed using fractional delays for image source models. A thorough analysis of the network representation and extensive performance tests are carried out using the BeamLearning network with synthetic and experimental datasets. Obtained results demonstrate that the BeamLearning approach significantly outperforms the wideband MUSIC and steered response power-phase transform methods in terms of localization accuracy and computational efficiency in the presence of heavy measurement noise and reverberation.

Experimental investigation on axial compressor stall phenomena using aeroacoustics measurements via empirical mode and proper orthogonal decomposition methods

In this work, we conduct experimental investigations on a single-stage axial compressor to shed lights on the dynamic stall phenomena via aeroacoustic measurements. For this, 8 acoustic pressure sensors are installed equally around the circumference of the compressor intake. The acoustic pressure data are simultaneously logged in real-time. Classical and conventional Fourier-transform based methods reveal that the compressor stall will occur beyond a critical pressure ratio or a flow coefficient as illustrated on a compressor map. However, there is no warning precursor obtained from the conventional signal analysis methods. Further investigations are conducted using a number of advanced signal processing techniques, such as empirical mode decomposition (EMD) and proper orthogonal decomposition (POD) and continuous wavelet (CW) methods. EMD analysis reveals that the compressor stall is corresponding to a low-frequency intrinsic mode function (IMF). It is growing rapidly from negligible amplitude random disturbances to limit cycle oscillations. POD shows that the number of the dominant acoustic modes contributing to more than 98.5% of the total fluctuation energy is changed from 3 to 7, when the stall occurs. This is insightful for low-order modeling of the compressor stall phenomena. Finally, applying CW transform could provide a warning of 0.05 s precursor on the tested axial compressor. In general, the present work opens up an alternative approach to study the dynamic physics of a compressor stall by applying an array of acoustic sensors with proper advanced data-processing methods implemented.

Sensor Array Enables Accurate Profiling of Produced Fluids During Drillstem Tests

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 29749, “Determining Produced-Fluid Properties for Accurate Production Profiling During a Drillstem Test Using Thermal-Imaging Technology,” by David Lavery and David Fyfe, Metrol, and Abu Rashid Hasan, SPE, Texas A&M University, prepared for the 2019 Offshore Technology Conference Brasil, Rio de Janeiro, 29–31 October. Copyright 2019 Offshore Technology Conference. Reproduced by permission. This paper describes the use of a downhole temperature-sensor array during a commingled drillstem test (DST) to determine the density of produced fluids accurately. In a typical DST that uses only downhole pressure gauges, any fluid contacts between the pressure gauges would be missed and the produced-fluid density calculated would be erroneous. The complete paper demonstrates the importance of taking fluid properties into account when determining the zonal flow-rate contributions using the mass-enthalpy method. Introduction Downhole temperature-sensor-array data provide accurate fluid contact depths during buildup periods of the DST that typically cannot be observed in pressure gradients. Determination of these fluid contacts permits the calculation of individual produced-fluid densities. In a case study described in the complete paper, the deepest perforated zone produced a fluid with a higher density than that seen in shallower perforated intervals. The higher density of the produced brine caused the wellbore fluids to slug during the flow periods with a measurable response in pressure and temperature data. If this difference in the fluid properties is not taken into account, zonal-allocation flow rate will be in error because it relies on density and specific heat capacity. Qualitative assessment of the temperature-array data identified producing zones and clearly highlighted different fluid interfaces in detail that would remain hidden if the pressure gauges were relied upon solely. High-Resolution Temperature Measurement During DST Operations The deployment of the downhole temperature-sensor array during DSTs provides an in-depth look into reservoir characteristics that go beyond traditional downhole pressure-gauge measurements. The temperature sensors, contained within steel tubing and clamped to the outside of the tubing-conveyed perforating guns, cover the three perforated intervals in this case study. Two temperature sensor arrays were run, each with a sensor spacing of 0.6 m and offset with an effective sensor spacing of 0.3 m. Throughout the DST, the readings from the temperature sensor arrays were recorded on downhole memory devices at 1-minute intervals. The temperature measurement covered the entire perforated interval. High-resolution pressure gauges are placed above and below the perforated interval and above and below the tester valve. Using an acoustic communication system, real-time temperature and pressure data were available during the DST so that observations across the entire test could be made. Zonal Allocation of Produced Reservoir Fluids From Mass-Enthalpy Balance The mass-enthalpy method of calculating zonal contributions relies on intervals higher in the wellbore producing at a lower temperature than deeper producing intervals. The higher- temperature contributions from the deepest producing intervals will flow up the wellbore while losing some heat to the colder wellbore. The rate of cooling is dependent on the flow velocity. The shallower producing intervals contribute at a lower temperature than the mixture and will cool down the mixture. This method is detailed in the complete paper.

Experimental Study on Source Level Estimation Techniques of Underwater Sound Source in Reverberant Water Tank

The acoustic power is used as a primary index characterizing underwater sound sources and could be defined by its source level. The source level has been assessed using various experimental techniques such as the reverberation time method and reverberant tank plot method. While the reverberation time method requires reverberation time data extracted in a preliminary experiment in a reverberant water tank, the reverberant tank plot method only needs acoustic pressure data directly obtained at the reverberation water tank. In this research, these experimental techniques were studied in comparative experiments to estimate the source levels of underwater sources in a reverberant water tank. This paper summarizes the basic theories and procedures of these experimental techniques and presents the experimental results for an underwater source in a long cuboid water tank using each technique, along with a discussion.

Acoustic Classification of Surface and Underwater Vessels in the Ocean Using Supervised Machine Learning.

Four data-driven methods—random forest (RF), support vector machine (SVM), feed-forward neural network (FNN), and convolutional neural network (CNN)—are applied to discriminate surface and underwater vessels in the ocean using low-frequency acoustic pressure data. Acoustic data are modeled considering a vertical line array by a Monte Carlo simulation using the underwater acoustic propagation model, KRAKEN, in the ocean environment of East Sea in Korea. The raw data are preprocessed and reorganized into the phone-space cross-spectral density matrix (pCSDM) and mode-space cross-spectral density matrix (mCSDM). Two additional matrices are generated using the absolute values of matrix elements in each CSDM. Each of these four matrices is used as input data for supervised machine learning. Binary classification is performed by using RF, SVM, FNN, and CNN, and the obtained results are compared. All machine-learning algorithms show an accuracy of >95% for three types of input data—the pCSDM, mCSDM, and mCSDM with the absolute matrix elements. The CNN is the best in terms of low percent error. In particular, the result using the complex pCSDM is encouraging because these data-driven methods inherently do not require environmental information. This work demonstrates the potential of machine learning to discriminate between surface and underwater vessels in the ocean.

Predicting the Amplitude of Limit-Cycle Oscillations in Thermoacoustic Systems with Vortex Shedding

Thermoacoustic instability is a plaguing problem encountered in many combustion systems. The large-amplitude acoustic oscillations, which are a noted aspect of this instability, can have a detrimental effect on the performance of the system; and they sometimes even cause lasting damage to the system components. The aim of this study is to estimate the amplitude of the limit-cycle oscillations observed during thermoacoustic instability corresponding to the axial acoustic modes using pressure measurements acquired during stable combustion. First, an equation is derived that describes the slow-varying amplitude of oscillations in certain reduced-order models of combustion systems involving vortex shedding. Subsequently, a procedure is detailed, wherein this equation is used in conjunction with the measured pressure time series and some information about the system to predict the instability amplitude. The estimation capability of this technique is then tested using acoustic pressure data obtained from laboratory-scale bluff-body and swirl-stabilized combustors. It is observed that the estimated amplitudes are in good agreement with actual values.

Suppressing the onset of instabilities in the time-domain equivalent source method using a multistep approach.

Numerical instability is an important issue that should be addressed in the time-domain equivalent source method (TESM). This study proposes a multistep method to stabilize TESM when using the measured acoustic pressure data to optimize equivalent source strengths. Unlike the conventional single-step method that solves each time step in the time-marching process of TESM, the proposed method performs a one-time solution for several time steps. The multistep solution can potentially reduce the accumulation rate of error, and improves filtering effects by changing the structure of the matrix that needs to be inverted when the truncated singular value decomposition or Tikhonov regularization is used in the time-marching process. Numerical simulations with three examples demonstrate the effectiveness of the multistep method in improving the stability of solutions compared with the single-step method. Effects of the number of merged time steps on the solutions are also discussed to guide the selection process. Finally, the sensitivity of the multistep method to numerical parameters is investigated to demonstrate its consistency under different configurations of numerical parameters.

Acoustic imaging in application to reconstruction of rough rigid surface with airborne ultrasound waves.

Accurate reconstruction of the surface roughness is of high importance to various areas of science and engineering. One important application of this technology is for remote monitoring of open channel flows through observing its dynamic surface roughness. In this paper a novel airborne acoustic method of roughness reconstruction is proposed and tested with a static rigid rough surface. This method is based on the acoustic holography principle and Kirchhoff approximation which make use of acoustic pressure data collected at multiple receiver points spread along an arch. The Tikhonov regularisation and generalised cross validation technique are used to solve the underdetermined system of equations for the acoustic pressures. The experimental data are collected above a roughness created with a 3D printer. For the given surface, it is shown that the proposed method works well with the various number of receiver positions. In this paper, the tested ratios between the number of surface points at which the surface elevation can be reconstructed and number of receiver positions are 2.5, 5, and 7.5. It is shown that, in a region comparable with the projected size of the main directivity lobe, the method is able to reconstruct the spatial spectrum density of the actual surface elevation with the accuracy of 20%.

Far-Field Performances Prediction of High Frequency Projectors Using Secondary Source Array Method

This paper investigates the prediction of the far-field performances of high frequency projectors using the second source array method (SSAM). The far-field parameters can be calculated accurately using the complex acoustic pressure data of two very close parallel planes which lie in the near-field region of the projector. The paper simulates the feasibility of predicting the far-field parameters such as transmitting voltage response and the far-field directivity pattern. The predicting results are compared with that calculated using boundary element method (BEM). It shows very good agreement between the two methods. A planar high frequency projector is measured using the near-field method. In order to verify the predicting results, the far-field measurement is performed for the same projector. The comparison of the results shows that the near-field method is capable to precisely predict the far-field parameters of the projector.