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The public’s awareness of noise and vibration forms a significant barrier to further development of railways. This chapter begins with a short introduction to the main fundamental aspects of acoustics, including decibels, frequency analysis, the propagation of sound with distance and common measurement quantities. The main sources of railway noise are discussed, including rolling noise, impact noise, curve squeal and aerodynamic noise. Simple calculation procedures are described that can be used to assess the impact of railway noise and to compare it with legal limits. The final section is devoted to ground vibration, which is a related form of environmental disturbance.

ALTHOUGH the University of Southampton has had an independent existence for ten years it has roots going back almost a century in the Hartley Institute. An early pioneer in aeronautics, F. W. Lanchester received his technical training in Southampton and the first of the new engineering buildings at the university has been called after him.

Acoustic sensors based on the well‐known relation between sound velocity and mean molecular mass are suggested for the determination of small concentrations of “pollutants”, such as CO₂, in air. The theoretical basis for high resolution is outlined, and a basic design is presented, together with experimental results. Sound velocity is measured continuously at high resolution in an oscillator controlled by the acoustic transit time between a transmitter/receiver pair operating in the 40kHz range. The static error band is better than +/‐3 per cent of full scale, response time less than two minutes, and short‐term resolutions of 0.3 ppm rms, and 3 ppm rms have been obtained in terms of frequency and CO₂ concentration, respectively.

The purpose of this paper is to develop a novel electromagnetic-based acoustic energy harvester (EH) for the application of wireless autonomous sensors.

The developed acoustic EH comprises a Helmholtz resonator (HR), a suspension system that consists of a flexible membrane and a permanent magnet, a couple of coils and a coil holder. Furthermore, the HR, used in the harvester, is designed for a specific resonant frequency based on simulation carried out in COMSOL Multiphysics®.

The developed harvester is tested both in lab under harmonic sound pressure levels (SPLs) and in real environment under random SPLs. In lab, when exposed to 100 dB SPL, the harvester generated a peak power of 212 µW. Furthermore, in real environment in vicinity of electric generator, the harvester produced an output voltage of about 110 mV collectively from its both coils.

In this paper, a novel geometric configuration for electromagnetic-based acoustic EH is proposed. In the developed harvester, two coils are placed in it to achieve enhanced electrical output from it for the first time.

The objective of this study is to model the influence of free gas, in the form of size‐distributed fine bubbles, on sound attenuation and dispersion in a thin‐walled elastic cylindrical tube filled with viscoelastic polymeric liquid.

Sound wave propagation in the system is described within a three‐phase interaction scheme, based on a quasi‐homogeneous approach to liquid‐gas mixture dynamics in the wave. Coupled equations of tube wall deformations and viscoelastic liquid dynamics in the tube are solved using a long‐wave approximation. The dissipative losses, stemming from flow gradients in the wave, as well as from non‐equilibrium bubble‐liquid interaction, are accounted for. The dispersion equation for the waveguide is obtained and studied numerically.

The results of the study indicate that bubble‐size distribution in viscoelastic liquid has an essential impact on sound propagation in the tube at sufficiently high frequencies. The frequency range in which the mixture heterogeneity influences the acoustic properties of the system is sensitive to both the distribution parameters and the rheological properties of the liquid. As distinct to polydispersity features, the viscoelastic properties of liquid are also relevant in the low‐frequency range, where they lead to an increase of the wave speed and a decrease of its attenuation.

A model of sound wave propagation in a tube filled with a heterogeneous viscoelastic liquid‐bubble mixture is formulated. The study provides a basis for modeling transient processes in tubes filled with polymeric liquids containing free gas, and for acoustic control of certain processes in polymer technologies.

It is currently difficult to measure temperature and pressure in harsh environments. Such measurements are limited by either the ability of the sensing element or the associated electrical wiring to withstand the operating environment. This is unfortunate as temperature and pressure are important measurands in various engineering structures as they provide critical information on the operating condition of the structure. Hence, there is a need to address this shortcoming. Such a sensor in place would enhance the operating efficiency thereby reducing the pollution burden and its impact on the environment. The purpose of this paper is to present theoretical and preliminary experimental results for a co‐integrated pressure and temperature sensor for harsh environments.

This work describes a co‐integrated pressure‐temperature wireless sensing scheme. The approach presented herein provides the possibility of measuring dynamic pressure and temperature within an enclosed volume using acoustic signals. Resonance tube physics is exploited for the temperature sensing. A microphone is used to obtain the acoustic signal whose frequency is a function of the temperature and the tube geometry.

The dynamic pressure is measured from the calibrated amplitude of the pressure wave signal measured by the microphone. The temperature can be measured through the shift of the standing wave frequency with a resolution of <1°C. The resonance tube can be fabricated using any material that resists harsh environments. The geometry of the tube can be tailored for any specific frequency range, as the application warrants. Also, this provides a means for accurate temperature compensation of pressure sensor data from high temperature environments. A Matlab/Simulink model is developed and presented for the acquisition of acoustic signals through the wall of an enclosed volume. For these applications the standing wave signal transmitted through the enclosure wall becomes a function of the wall material and wall thickness. Preliminary experimental results are presented in which a DC fan is used for generating the dynamic pressure in a varying temperature environment.

The major issue is the separation of the noise from the signal. As various applications yield specific signal noise, the problem needs detailed data to be addressed.

Temperature and dynamic pressure could be recorded/monitored in very harsh environment conditions such as chemical reactors.

This work demonstrates the possibility of employing a co‐integrated acoustic sensing scheme in which both pressure and temperature are measured simultaneously with a sole sensor. The major advantage with acoustic sensing is the wireless transmission of data. This allows for non‐invasive measurement from within enclosed systems. Direct real‐time temperature compensation is possible that does not require any compensation circuitry. Hence, pressure and temperature data may be obtained from caustic operating environments whose access is otherwise not feasible.

The purpose of this paper is to employ the acoustic wave propagation method for leakage detection in pipes. The first objective is to use acoustic finite element analysis (AFEA) method to simulate acoustic wave propagation and acoustic wave reflectometry in an intact pipe and in pipes with leaks of various sizes. This is followed by the second objective which is to validate the effectiveness and the practicability of the acoustic wave method via experimental testing. The third objective involves the decomposition and de-noising of the measured acoustic waves using stationary wavelet transform (SWT). It is shown that this approach, which is used for the first time on leakage detection in pipes, can be used to identify, locate and estimate the size of a leakage defect in a pipe.

The research work was designed inline with best practices and acceptable standards. The research methodology focusses on five basic areas: literature review; experimental measurements; simulations; data analysis and writing-up of the study with clear-cut communication of the findings. The approach used was acoustic wave propagation-based method in conjunction with SWT for leakage detection in fluid-filled pipe.

First, the simulation of acoustic wave propagation and acoustic wave reflectometry in fluid-filled pipes with and without leakage have great potential in leakage detection in pipeline systems and can detect very small leaks of 1 mm diameter. Second, the measured noise-contaminated acoustic wave propagation in a fluid-filled pipe can be successfully de-noised using the SWT method in order to clearly identify and locate leakage as little as 5 mm diameter in a pipe. Third, AFEA of a fluid-filled pipe can be achieved with the simulation of only the fluid content of the pipe and without the inclusion of the pipe in the model. This eliminates contact interaction of the solid pipe walls and the fluid, and as a consequence reduces computational time and resources. Fourth, the relationship of the ratio of the leakage diameter to the ratio of the first and second secondary wave amplitudes caused by the leakage can be represented by a second-order polynomial function. Fifth, the identification of leakage in a pipe is intuitive from mere comparison of the acoustic waveforms of an intact pipe with that of a pipe with a leakage.

The research work is a novelty and was developed from the scratch. The AFEA of acoustic wave propagation and acoustic wave reflectometry in a static fluid-filled pipe, and the SWT method have been used for the first time to detect, locate and estimate the size of a leakage in a fluid-filled pipe.

The purpose of this paper is to analyze and optimize synthetic jet actuators (SJAs) by means of a literature-known one-dimensional analytical model.

The model was fit to a wide range of experimental data from in-house built SJAs with different dimensions. A comprehensive parameter study was performed to identify coupling between parameters of the model and to find optimal dimensions of SJAs.

The coupling of two important parameters, the diaphragm resonance frequency and the cavity volume, can be described by a power law. Optimal orifice length and diameter can be calculated from cavity height in good agreement with literature. A transient oscillation correction is required to get correct simulation outcomes.

Based on these findings, SJA devices can be optimized for maximum jet velocity and, therefore, high performance.

Nowadays, the determination of the acoustic radiation of electric machines is of particular interest, because legal regulations restrict the maximum audible noise radiated by technical devices such as electrical machinery. The purpose of this paper is to analyze the electromagnetic excited structure‐borne sound and air‐borne noise of an AC servo drive.

This paper presents the required steps for the multiphysics acoustic simulation of electrical machines to evaluate its noise behaviour. This numerical approach starts with the electromagnetic force‐wave simulation. The computation by a structure dynamic model determines the deformation of the mechanical structure due to the force‐waves. The final step of the simulation approach consists of the computation of the acoustic radiation.

For the electromagnetic simulation analytical and numerical methods are combined to gain some acceleration of the entire multiphysics simulation approach. This combination offers additionally a detailed understanding of the noise generation mechanism in electrical machines.

Particular attention is paid to the structural‐dynamic model. Modelling of microstructures, such as the laminated iron core or insulated coils, is memory and computational expensive. A systematic material homogenisation technique, based on experimental‐ and numerical modal analyses, yields a higher accuracy at lower computational costs when compared to standard numerical approaches. The presented multiphysics simulation is validated by measurements. The methods are presented by means of a case study.

The purpose of this paper is to improve active sonar detection performance in shallow water. A stochastic‐like model multivariate elliptically contoured (MEC) distributions is defined to model reverberation, which helps to reveal structure information of target signatures.

Active sonar systems have been developed with wider transmission bandwidths and larger aperture receiving array, which improve the signal‐to‐noise ratio and reverberation power ratio after matched filtering and beamforming. But, it has changed the statistical distribution of the reverberation‐induced envelope from the traditionally assumed Rayleigh distribution. The MEC is a kind of generalized non‐Gaussian distribution model. The authors theoretically derive the compound Gaussian, Rayleigh‐mixture, Weibull, K distributions are all special cases of MEC. It is known that Weibull and K distributions have obvious heavy‐tail than Rayleigh distribution. MEC is a suitable model to characterize non‐Rayleigh heavy‐tailed distribution of reverberation.

The analysis of test data shows reverberation envelopes obviously deviate Rayleigh distribution. In a broad non‐Gaussian framework, reverberation is modelled as MEC distribution, which is suitable to characterize non‐Rayleigh reverberation. The received data in trials validate the effectiveness of MEC model. The real data envelops follows K distribution, which is a special case of MEC. So, the MEC can be applied to develop novel signal‐processing algorithms that mitigate or account for the effects of the heavy‐tailed reverberation distributions on the target detection.

The limited sea test data are the main limitation to prove model validation in further.

A very useful model for representing reverberation in shallow‐water.

The MECs in fact represent an attractive set data model for adaptive array, and it provides a theoretic framework to design an optimal or sub‐optimal detector.