Frequency: Quarterly E- ISSN: 2250-2874 P- ISSN: Awaited IBI Factor: 4.09 Abstracted/ Indexed in: Ulrich's International Periodical Directory, Google Scholar, SCIRUS, getCITED, InfoBase Index, Genamics JournalSeek, EBSCO Information Services
Quarterly published in print and online "Inventi Impact: Acoustics" publishes high quality unpublished as well as high impact pre-published research and reviews catering to the needs of researchers and professionals. It focuses on multidimensional aspects of acoustics, particularly - acoustic materials, acoustic problems in information science, acoustic transducers, acoustic measurements and methods, architectural acoustics and electroacoustics, infrasonics, musical acoustics, physical, psychological acoustics and bioacoustics, etc.
A simulator for analyzing the interaction between the oil sand terrain and a pipe wagon articulating (PWA) system has been\r\ndeveloped in this paper. An elastic-plastic oil sand model was built based on the finite element analysis (FEA) method and von\r\nMises yield criterion using the Algor mechanical event simulation (MES) software. The three-dimensional (3D) distribution of\r\nthe stress, strain, nodal displacement, and deformed shape of the oil sands was animated at an environmental temperature of\r\n25?C. The 3D behavior of the oil sand terrain was investigated with different loading conditions. The effect of the load and contact\r\narea on the stress and nodal displacement was analyzed, respectively. The results indicate that both the max stress and max nodal\r\ndisplacement increase with the load varying from 0 to 3.6 E + 7 N and decrease with the contact area varying from 2 to 10m2. The\r\nmethod presented in this paper forms the basis for evaluating the bearing capacity of oil sand ground....
In order to avoid the dependence of mesh method on grids, a 3D global weak-form mesh-free method (MFM) is applied to study\nthe three-dimensional acoustic characteristics of silencers. For the expansion chamber silencers, the 3D acoustic modes are\nextracted and the transmission loss results are computed by using the 3D global weak-form (MFM), which is based on the radial\nbasis function point interpolation method (RPIM) for calculating the shape functions and Galerkin method for discretizing the\nsystem equation. The first 15 order 3D acoustic modes and TL results of a special expansion chamber silencer are presented to\nvalidate the computational accuracy of the proposed technique, and the relative errors are controlled within 0.5% by comparing\nwith the 3D finite element method (FEF) calculations. Additionally, the effects of axial modes on the acoustic characteristics are\ninvestigated, and the pass through frequencies can be eliminated to enhance the acoustic attenuation performance by locating the\nside branch outlet on the nodal lines of axial modes....
3D temperature distribution measurement in a furnace based on acoustic tomography (AT) calculates temperature field through\nmultipath acoustic time-of-flight (TOF) data. In this paper, a new 3D temperature field reconstruction model based on radial basis\nfunction approximation with polynomial reproduction (RBF-PR) is proposed for solving the ATinverse problem. In addition, the\nmodified reconstruction method that integrates the advantages of the TSVD and Tikhonov regularization methods is presented to\nreduce the sensitivity of noise on perturbations with the ill-posed problems and improve the reconstruction quality (RQ).\nNumerical simulations are implemented to evaluate the effectiveness of the proposed reconstruction method using different 3D\ntemperature distribution models, which include the one-peak symmetry distribution, one-peak asymmetry distribution, and twopeak\nsymmetry distribution. To study the antinoise ability of our method, noises are added to the value of TOF. 3D display of\nreconstructed temperature fields and reconstruction errors is given. The results indicate that our model can reconstruct the\ntemperature distribution with higher accuracy and better antinoise ability compared with the truncated generalized singular value\ndecomposition (TGSVD). Besides that, the proposed method can determine the hot spot position with higher precision, and the\ntemperature error of the hot spot is lower than the other compared methods....
A 4 Ã?â?? 4 matrix model with three degrees of freedom is proposed as a means for controlling microvibrations and applied to\r\nan electromagnetic isolator. The model was derived from an assumption based on small- and low-frequency vibrations. The\r\ncoordinates of the 3 DOF was composed of the 4 variables, representing a vertical position, pitch, roll, and a proof term. The\r\ncoordinates were calculated from the 4 position sensors in the isolator and formulated into a 4 Ã?â?? 4 matrix, which possesses\r\ninversive full rank. The electro-magnetic isolator was built for a simulated machine in semiconductor manufacturing and consisted\r\nof a heavy surface plate, sensors, amps, a controller, and air springs with electromagnets. The electromagnets are installed in a\r\npneumatic chamber of the individual air spring. The performance of the 3 DOF model was experimented and compared with that\r\nof a 1 DOF model in an impact test. The settling time in the result was reduced to 25%....
In this paper, the Chebyshev spectral method is used to solve the normal mode and parabolic equation models of underwater\nacoustic propagation, and the results of the Chebyshev spectral method and the traditional finite difference method are compared\nfor an ideal fluid waveguide with a constant sound velocity and an ideal fluid waveguide with a deep-sea Munk speed profile. The\nresearch shows that, compared with the finite difference method, the Chebyshev spectral method has the advantages of a high\ncomputational accuracy and short computational time in underwater acoustic propagation....
In modern applications such as robotics, autonomous vehicles, and speaker localization, the computational power for sound source localization applications can be limited when other functionalities get more complex. In such application fields, there is a need to maintain high localization accuracy for several sound sources while reducing computational complexity. The array manifold interpolation (AMI) method applied with the Multiple Signal Classification (MUSIC) algorithm enables sound source localization of multiple sources with high accuracy. However, the computational complexity has so far been relatively high. This paper presents a modified AMI for uniform circular array (UCA) that offers reduced computational complexity compared to the original AMI. The complexity reduction is based on the proposed UCA-specific focusing matrix which eliminates the calculation of the Bessel function. The simulation comparison is done with the existing methods of iMUSIC, the Weighted Squared Test of Orthogonality of Projected Subspaces (WS-TOPS), and the original AMI. The experiment result under different scenarios shows that the proposed algorithm outperforms the original AMI method in terms of estimation accuracy and up to a 30% reduction in computation time. An advantage offered by this proposed method is the ability to implement wideband array processing on low-end microprocessors....
This paper concerns themechanism for harvesting energy fromhuman bodymotion.Thevibration signal fromhuman bodymotion\nduring walking and jogging was first measured using 3-axes vibration recorder placed at various places on the human body. The\nmeasured signal was then processed using Fourier series to investigate its frequency content. A mechanism was proposed to harvest\nthe energy from the low frequency-low amplitude human motion. This mechanism consists of the combined nonlinear hardening\nand softening mechanism which was aimed at widening the bandwidth as well as amplifying the low human motion frequency.\nThis was realized by using a translation-to-rotary mechanism which converts the translation motion of the human motion into the\nrotational motion.The nonlinearity in the system was realized by introducing a winding spring stiffness and the magnetic stiffness.\nQuasi-static and dynamic measurement were conducted to investigate the performance of the mechanism. The results show that,\nwith the right degree of nonlinearity, the two modes can be combined together to produce a wide flat response. For the frequency\namplification, the mechanism manages to increase the frequency by around 8 times in terms of rotational speed....
In this work a crack identification method is proposed for bridge type structures carryingmoving vehicle.The bridge ismodeled as\nan Euler-Bernoulli beam, and open cracks exist on several points of the beam. Half-car model is adopted for the vehicle. Coupled\nequations of the beam-vehicle systemare solved usingNewmark-Betamethod, and the dynamic responses of the beamare obtained.\nUsing these and the reference displacements, an objective function is derived. Crack locations and depths are determined by solving\nthe optimization problem. To this end, a robust evolutionary algorithm, that is, the particle swarmoptimization (PSO), is employed.\nTo enhance the performance of the method, themeasured displacements are denoised usingmultiresolution property of the discrete\nwavelet transform (DWT). It is observed that by the proposed method it is possible to determine small cracks with depth ratio 0.1\nin spite of 5% noise interference....
Uplink communication across the water–air interface holds great potential for offshore oil surveys and military applications. Among the various methods available for implementing uplink communication, translational acoustic-RF (TARF) communication stands out due to its ability to withstand wave interference and exhibit low absorption losses. However, the physical processes underlying such systems are currently under-researched, and channel models for evaluating its communication performance indicators are lacking. Herein, we propose a signal-to-noise ratio (SNR) channel model for evaluating the performance metrics of an uplink communication system combining acoustic and millimeter waves for the first time and validate the accuracy of the proposed model through experiments. Specifically, in the process of model construction, the physical process of the communication system was deeply studied, and the corrections of multipath effects, box vibrations, and second-order nonlinear coefficients of the amplitude of the water surface were realized. The water-to-air cross-medium communication system was built, and communication experiments were conducted to validate the feasibility of combining acoustic and millimeter wave communication. This research provides a theoretical and experimental foundation for the design and evaluation of TARF communication systems, providing valuable guidance for enhancing the system’s performance metrics and promising an innovative approach for modern seaborne communication....
The fluvial acoustic tomography (FAT) system relies on the arrival time of the system signal to calculate the parameters of the region. The traditional method uses the matching filter method to calculate the peak position of the received acoustic signal after cross-correlation calculation within a certain time as the signal arrival time point, but this method is difficult to be effectively applied to the complex underwater environment, especially in the case of extremely low SNR. To solve this problem, a two-channel deep learning model (DCA-Net) is proposed to detect the arrival time of acoustic chromatographic signals. Firstly, an interactive module is designed to transmit the auxiliary information from the cross-correlation subnetwork to the original signal subnet to improve the feature information extraction capability of the network. In addition, an attention module is designed to enable the network to selectively focus on the important features of the received acoustic signals. Under the background of white Gaussian noise and real river environment noise, we use the received signals of the acoustic tomography system collected in the field to synthesize low SNR data of −10, −15, and −20 different decibels as datasets. The experimental results show that the proposed network model is superior to the traditional matching filtering method and some other deep neural networks in three low SNR datasets....
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