Prediction Of Transmission Loss Research Articles

Prediction of the sound transmission loss of shape-varied sonic crystals: A transfer matrix approach.

This paper proposes a transfer matrix method (TMM) for modeling sonic crystals to predict the transmission loss of noise exiting an air extraction system. Because the crystals may be of different shapes (e.g., square, circular, or standardized airfoil profile to minimize airflow resistance) and must account for thermo-viscous losses, a discrete version of the TMM is used. Similar to the finite element method, a discretization of the geometry is first performed. Each element is modeled with a transfer matrix (TM) that includes the local thermo-viscous losses which attenuate the sound wave. For each element in parallel, the parallel TMM is employed. For the subsequently created elements in series, the classic TMM is used. This generates a global TM from which the sound transmission loss of the crystal network is deduced. The predictions obtained by the proposed method are compared to measurements in an acoustic tube for three different shapes of sonic crystals. The results show that a geometric tortuosity correction is necessary for the predicted bandgap center frequency to match the measurement. A correction is proposed, but this requires a possible refinement for more complicated profiles.

Multi-objective Approach for Dynamic Economic Emission Dispatch Problem Considering Power System Reliability and Transmission Loss Prediction Using Cascaded Forward Neural Network

This study addresses the significant problem of Dynamic Economic Emission Dispatch (DEED), a critical consideration in power systems from both economic and environmental protection viewpoints. Reliability stands as another vital facet, impacting maintenance and operation perspectives. The integration of Artificial Neural Network (ANN)-based transmission loss prediction into the DEED model is also essential to address specific limitations and enhance the overall performance of the dispatch process. Traditionally, the DEED model relies on a single B-loss coefficient to estimate transmission losses. While this approach simplifies calculations, it fails to account for the significant variations in demand that occur throughout the dispatch period and it leads to inaccuracies in loss prediction, especially in dynamic environments. Using a single coefficient, the model cannot adequately capture the complex, non-linear relationships between power generation, load, and transmission losses under different operating conditions. To overcome this limitation, this study introduces an ANN-based loss prediction method integrated into the DEED model and uses trained ANN to replace the process of finding B-loss coefficients during each dispatch period. This paper also introduces a strategy leveraging the multi-objective northern goshawk optimizer algorithm, characterized by a non-dominated sorting and crowding distance mechanism, to enhance DEED considerations incorporating reliability (DEEDR). This novel algorithm improves the solution space effectively, maintains high population diversity and enables an even distribution of individuals sharing the same rank in the objective space. The fundamental objective of this study is to balance fuel cost, emission, and system reliability in power system operations. Compared with a few existing multi-objective optimization algorithms, this study demonstrates superior performance in generating a series of non-dominated solutions. The experimental results highlight its competitive and potential as an efficient tool in the DEED and DEEDR problems, promising a synergistic coordination of economy, environmental protection, and system reliability benefits in power system management.

Predicting underwater acoustic transmission loss in the SOFAR channel from ray trajectories via deep learning.

Predicting acoustic transmission loss in the SOFAR channel faces challenges, such as excessively complex algorithms and computationally intensive calculations in classical methods. To address these challenges, a deep learning-based underwater acoustic transmission loss prediction method is proposed. By properly training a U-net-type convolutional neural network, the method can provide an accurate mapping between ray trajectories and the transmission loss over the problem domain. Verifications are performed in a SOFAR channel with Munk's sound speed profile. The results suggest that the method has potential to be used as a fast predicting model without sacrificing accuracy.

Multi-Objective Stochastic Paint Optimizer for Solving Dynamic Economic Emission Dispatch with Transmission Loss Prediction Using Random Forest Machine Learning Model

Dynamic economic emission dispatch problems are complex optimization tasks in power systems that aim to simultaneously minimize both fuel costs and pollutant emissions while satisfying various system constraints. Traditional methods often involve solving intricate nonlinear load flow equations or employing approximate loss formulas to account for transmission losses. These methods can be computationally expensive and may not accurately represent the actual transmission losses, affecting the overall optimization results. To address these limitations, this study proposes a novel approach that integrates transmission loss prediction into the dynamic economic emission dispatch (DEED) problem. A Random Forest machine learning model was offline-trained to predict transmission losses accurately, eliminating the need for repeated calculations during each iteration of the optimization process. This significantly reduced the computational burden of the algorithm and improved its efficiency. The proposed method utilizes a powerful multi-objective stochastic paint optimizer to solve the highly constrained and complex dynamic economic emission dispatch problem integrated with random forest-based loss prediction. A fuzzy membership-based approach was employed to determine the best compromise Pareto-optimal solution. The proposed algorithm integrated with loss prediction was validated on widely used five and ten-unit power systems with B-loss coefficients. The results obtained using the proposed algorithm were compared with seventeen algorithms available in the literature, demonstrating that the multi-objective stochastic paint optimizer (MOSPO) outperforms most existing algorithms. Notably, for the Institute of Electrical and Electronics Engineers (IEEE) thirty bus system, the proposed algorithm achieves yearly fuel cost savings of USD 37,339.5 and USD 3423.7 compared to the existing group search optimizer algorithm with multiple producers (GSOMP) and multi-objective multi-verse optimization (MOMVO) algorithms.

Prediction of Sound Transmission Loss of Vehicle Floor System Based on 1D-Convolutional Neural Networks

Prediction of Sound Transmission Loss of Vehicle Floor System Based on 1D-Convolutional Neural Networks

Integrated approach for dynamic economic/emission dispatch problem: multi-objective moth flame optimizer with transmission loss prediction using cascaded forward neural network

Integrated approach for dynamic economic/emission dispatch problem: multi-objective moth flame optimizer with transmission loss prediction using cascaded forward neural network

Numerical prediction of the sound transmission loss of double panel configurations with acoustic structured and poroelastic materials

The sound transmission loss of multilayer system assemblies made of Noise Control Treatments comprising metamaterials and poroelastic materials, sandwiched between two structural panels is of utmost importance in aerospace, automotive, building and several other engineering applications. The integration of acoustic metamaterials in these systems introduces a number of challenges at the level of numerical simulation in order to predict their acoustic and structural behavior. This paper presents a methodology for the numerical simulation of the sound transmission loss of structural configurations comprising acoustic metamaterials embedded in a glass wools layer, sandwiched between two elastic panels. The metamaterial is designed as a parallel assembly of four sub-metamaterials. Each of these sub-metamaterials is also a series assembly of a periodic unit cell having a neck + cavity + neck type configuration. The four sub-metamaterials are designed so that their first resonance frequencies are grouped together in order to improve the sound transmission loss at the first two natural frequencies of the double panel. The COMSOL Multiphysics finite element method solver is used. The normal incidence and diffuse field sound transmission loss numerical results are presented and discussed in comparison with results from literature for validation.

Reduced-Order Machine-Learning Model for Transmission Loss Prediction in Underwater Acoustics

Reduced-Order Machine-Learning Model for Transmission Loss Prediction in Underwater Acoustics

Learning a surrogate Pekeris waveguide model

Underwater sound propagation models predict transmission loss (TL) given environmental parameters, such as seafloor sediment parameters. Also of interest is the reverse: Can seafloor parameters, such as sediment density, sound speed, and attenuation, be inferred from ocean acoustic data? This inverse problem can be addressed using information geometry tools for parameter identifiability analysis and model reduction, where we identify parameters that can be removed from a model without sacrificing accuracy. Critical to these methods for model reduction is the ability to evaluate derivatives of the model’s TL predictions with respect to model parameters. Automatic differentiation (AD) allows for rapid evaluation of a model’s derivatives but may face some challenges for implementation with sound propagation models, including models being written in “legacy code” and having non-differentiable points in the function space. Our solution is to train a surrogate machine learning (ML) model which can circumvent these challenges, and which we can apply AD to. We demonstrate this method using the Pekeris waveguide model of the ocean. We compare the surrogate and original model outputs and demonstrate the process of model reduction using a ML surrogate model. [Work supported by Office of Naval Research.]

Machine learning transmission loss simulations in complex undersea environments with range-dependent bathymetry

Range-dependent bathymetric variability introduces diverse scattering and multipath that can impact optimal acoustic communication ranges, and will impact autonomous underwater vehicle (AUV) operations. We are working towards building a sound-aware framework in which the AUV has knowledge of the transmission environment and can exploit or react with intended outcomes. Prior knowledge of the environment coupled with acoustic propagation modeling on-board AUVs can improve sound-awareness of a vehicle but is computationally intensive, and costly from a power-budget perspective. This work addresses the computational burden by training a machine learning (ML) model to interpret range-dependent acoustic propagation through environmental inputs to predict transmission loss (TL) from the ray tracing model, BELLHOP. A decision tree model is trained to predict locations in range and depth of acceptable TL using feature representations to reduce bathymetric information within a region. Models are trained and tested with TL field realizations for acoustic communications at 10 and 25 kHz for environments with varying bathymetry at ranges up to 3 km through BELLHOP. We present simulations and results from field experiments performed off the coast of Southern California. The communication paths are between a surface vehicle (Liquid Robotics Waveglider, Herdon, VA) and an AUV (REMUS UUV, Huntington Ingalls, Falmouth, MA). Results for model predictions and collected field data will be discussed.

A coupled approach for predicting transmission loss of silencers with consideration of three-dimensional wave effects in inlet and outlet ducts

A coupled approach combining finite element method and duct mode expansion is proposed to determine the transmission loss of large silencers with consideration of three-dimensional wave effects in inlet and outlet ducts in the presence of flow. The finite element formula is established for the linearized potential equation (LPE), and the acoustic variables in the boundary integrals on the inlet and outlet surfaces are expressed analytically in terms of mode expansion. Since the introduced modal amplitude coefficients add unknown variables, the intrinsic functions are used as weighted functions to construct integral equations on the inlet and outlet surfaces to form a closed set of equations. Expressions of incident and transmitted sound powers are derived, and the transmission loss of silencer may be determined for any-giving mode incidence. For the symmetric silencers, specific combinations of acoustic modes are presented to improve the stability and efficiency of the approach. The dissipative and reactive silencers with rectangular and circular cross-sectional inlet and outlet ducts are examined, good agreements between predictions and benchmark data verified the present approach. Effects of Mach numbers and geometric parameters, flow fields and acoustic excitations on the acoustic attenuation behavior of the silencers as well as transmission loss predictions from different strategies are discussed.

Acoustic behaviour of micro-perforated panel backed by shallow cavity under fully developed grazing flow

This paper presents numerical and experimental investigations into the acoustic behaviour of a micro-perforated panel (MPP) backed by a shallow cavity under a fully developed grazing flow. A three-dimensional time-domain computational fluid dynamics approach is applied alongside hybrid evaluation methods to accurately capture the acoustic behaviour variations of an MPP orifice, considering the interaction of flow dynamics and the acoustics in the orifice and cavity. Meanwhile, a hybrid Rayleigh's end correction model for T-shaped resonators is utilized to calculate the acoustic reactance of a very shallow cavity. The numerical results show that the vertical shear layer is observed alongside the hole and cavity and impinges the bottom of shallow cavity. Subsequently it alters acoustic and flow interaction significantly that leads to variation of acoustic behaviour of orifice. In this regard, an acoustic impedance model of an orifice backing with shallow cavity at grazing flow is established. The proposed acoustic impedance model of orifice has been validated by experimental study. The transmission loss predictions for a compact MPP absorber with single or double cavities agree well with the experimental results for the grazing flow condition. The practical significance of this study is that it elucidates the influence of cavities upon the acoustic behaviour of MPPs in the presence of grazing flow; furthermore, it proposes an impedance model that is more suitable for predicting the acoustic performance of booming compact meta-structures.

Study of the Deterministic Transmission Loss Prediction Model for Troposcatter

Troposcatter transmission loss prediction models are based mainly on ITU-R’s empirical models. However, the available troposcatter propagation models do not enable the real-time calculation of the transmission loss for a specific propagation link. Therefore, the primary purpose of this study is to develop a deterministic troposcatter transmission loss prediction model to realize the real-time calculation or prediction of the transmission loss. In this study, the turbulent scattering cross-section, the refractive turbulence structure constant and the radar equation were introduced to our approach, and the troposcatter transmission loss prediction model was established based on the geometry of the troposcatter circuit. In addition, a weighted method for taking the value of the refractive turbulence structure constant, generated from the WRF schema, was proposed, based on the ray tracing method. Furthermore, the proposed model was validated with the experimental measurement of troposcatter, which verifies the proposed model’s availability and reliability.

Characterizing elastic parameters of isotropic thin plates using impedance tube and transmission loss measurements: a numerical inverse method

Thin plates are widely used in various engineering applications, including aerospace, automotive, and construction industries. The mechanical properties of these plates, such as Young's modulus, Poisson's ratio, and structural damping, are essential in determining their acoustic performance. While there are various methods for measuring these properties individually or simultaneously, accurately obtaining them can be difficult due to the thin plate's delicate structure and the need for high precision and sensitive equipment. In this paper, we present a numerical inverse method for characterizing the elastic parameters of isotropic thin plates using transmission loss measurements obtained from an impedance tube and clamped circular specimen. The proposed method is compared to the Oberst beam technique (ASTM E756-05), and demonstrated through several examples ultimately allowing for the prediction of diffused field transmission loss obtained through intensity measurements in a small reverberant room.

Atmospheric acoustic propagation modeling using heterogeneous wind profiles along the acoustic path

This work presents atmospheric sound propagation predictions using a parabolic equation solver that accounts for heterogeneous wind profile distribution along the acoustic path. Transmission loss predictions using both homogeneous and heterogeneous wind speeds information are compared with data. A three-dimensional scanning Doppler LIDAR wind profiler captures real-time wind speed gradients at many locations along the acoustic propagation path providing the heterogeneous wind speed profiles. The wind measurements are concurrent with a pitch catch transmission loss measurements. A long-range acoustic device on an anchored pontoon sends known chirp sequences to a seven-channel receiver array at the water’s edge at ranges up to approximately one kilometer. Additional synchronized meteorological observations include temperature, humidity, and wind measured with anemometers. Key differences in model results are highlighted and an assessment of the value of the computational cost is presented.

Air temperature profiling over different littoral surfaces

There are many factors that must be considered in the study of atmospheric sound propagation over long distances; these include surface characteristics as well as wind speed and air temperature profiles. This work presents measurements of air temperature profiles over various surfaces. Building a catalogue of this type can enable more realistic case assumptions to be made in an atmospheric acoustic transmission loss model. Accurate air temperature profiles are critical because of the potentially significant impact on the transmission loss predictions. Air temperature profile measurements have been collected by solar radiation shielded temperature loggers mounted 1 m apart on a 7 m mast. Long duration measurements were taken to capture the variability in day and nighttime temperature differentials present over surfaces such as: gravel, vegetated shoreline, marsh grass, lawn grass, water, and asphalt. In particular, measurements performed off shore were done by placing the sensor array on the deck of a pontoon boat. The initial results revealed that this configuration captures a very low elevation warming due to re-radiation from the deck of a boat instead of the true over water temperature. This study highlights the influence of wind speed on the development of the near surface temperature inversions.

Radiation damping effect on the sound pressure response in a structural-acoustic cavity

A structural-acoustic system consisting of an enclosed cavity with flexible wall panels is considered to evaluate the effect of sound radiation-damping resulting from the panel vibration on the sound pressure level in the cavity. The interior cavity pressure can excite the flexible wall panels that will radiate noise to the exterior that in turn can reduce the interior noise due to the energy loss to the exterior surroundings. This energy loss is described as radiation damping of the interior cavity system. An enclosed rectangular cavity-plate system subjected to external random pressure excitation is used to evaluate the effect. The radiation damping is evaluated using a modal approach (FEM, BEM) to determine the radiation efficiency of the vibrating panels. The effect of the radiation damping for the example of a box-plate structure is evaluated using the CMA (classical modal analysis) method for the low-frequency range and the AMA (asymptotic modal analysis) method for the mid- and high-frequency ranges. The inclusion of radiation damping due to rigid body cavity mode excitation is shown to provide an improved prediction of the sound transmission loss especially in the low-frequency range.

Machine learning of acoustic propagation models for sound aware autonomous systems

Opportunities exist for autonomous underwater vehicles to optimize acoustic communications with knowledge of the sound channel, but implementation has traditionally been hindered by computationally expensive propagation models. This work addresses computational complexity by enabling machine learning to interpret environmental inputs to predict transmission loss (TL) outputs from a physics-based ray tracing model (BELLHOP). Feature representations to reduce bathymetric and sound speed profile (SSP) information through wavelet decomposition and empirical orthogonal analysis are explored, respectively. A decision tree machine learning algorithm is trained to learn the paths (depths and ranges) of acceptable transmission loss for a pair of acoustic modems within a given environment. Our test case is off the coast of Southern California, and SSPs are collected from a 3 year record sampled from an ocean mooring. Bathymetry data is based on high resolution multibeam data. TL field realizations are calculated as training and testing data for environments of varying bathymetry with frequency of 25kHz and range of 1km through the BELLHOP model. Results are shown with stationary and non-stationary emitters to demonstrate the effectiveness of this approach.

An Infinity Tube with an Expansion Chamber for Noise Control in the Ductwork System

This paper proposes a muffler with simple geometry to effectively reduce low-frequency noise in ductwork systems. A muffler named infinity tube with an expansion chamber (ITEC) is developed from the infinity tube (IT). Theoretical and numerical analyses of wave propagation in the ITEC have been conducted in this paper. The transfer matrix method is adopted to predict transmission loss theoretically. The theoretical results are validated by the finite element method simulation. The comparison of the transmission loss between the IT and ITEC illustrates that the ITEC has an advantage in low-frequency noise reduction. The transmission loss results of the ITEC are compared with the Helmholtz resonator system to assess the potential for industrial application. Finally, the geometric parameters of the proposed ITEC on its noise attenuation performance have been analyzed. The proposed ITEC can effectively reduce low-frequency noise, and it is suitable for ductwork systems in constrained spaces.

Prediction of sound transmission loss of hemispherical shell using statistical energy analysis and its experimental validation

The hemispherical shells are broadly used in many industries and applications such as aerospace, submarines, tanks, and architectural structures. Sound transmission loss prediction of such structures is essentially required during the design and assessment phases from the acoustic point of view. In the present paper, sound transmission loss of the hemispherical shell is predicted analytically and validated experimentally. The Statistical energy analysis (SEA) technique is employed to formulate an analytical model for computing the sound transmission loss of a hemispherical shell across a wide frequency range. The sound intensity experimental method was used to verify the proposed SEA formulation. The analytical predictions and the measured transmission loss were found to be in good agreement. Based on the proposed SEA model, the influence of design parameters such as the absorption coefficient, thickness, radius, and different materials on the transmission loss was investigated.