Engineering Acoustics. Malcolm J. Crocker

measurements can also be used to determine the transmission loss of walls without the need for a reception room. In addition, such measurements allow the transmission loss of different parts of a wall partition to be determined, which the conventional two‐room method does not.

      Chapter 10 deals with reactive and passive mufflers and silencers. Reactive mufflers are used on all automobiles and trucks, and proper acoustical design is of course most important. Although useful theory and measurements date back to the 1950s, it was not until finite element modeling was first used in 1971 on non‐concentric reactive mufflers that the attenuation of such mufflers could be predicted accurately. This chapter also reviews and compares different attempts to predict the attenuation of passive mufflers used in industrial systems.

      Chapter 12 discusses various aspects of sound and vibration in buildings. An important aspect of building acoustics is the unwanted transmission of sound from one room to an adjoining one. One measure of the effectiveness of a party wall to reduce this transmission is the sound reduction index, commonly called the transmission loss (TL). This chapter presents different theoretical models for TL and also measurements of TL for a wide variety of wall structures. A useful theoretical approach to determine the TL of single and multiple layer walls is statistical energy analysis (SEA), as described in Section 12.5 of Chapter 12.

      Chapter 13 deals with the noise and vibration generated by air‐conditioning (HVAC) systems in buildings. Although such systems are widely used they are often inadequately or incorrectly designed acoustically. Correct design at the beginning often costs a little more, but corrections made to such systems later, after they are installed, can be very expensive.

       Malcolm J. CrockerJorge P. Arenas

      30 September 2020

      Acknowledgements

The authors are greatly indebted to many colleagues for their patience and considerable support with this project. The staff at John Wiley including Paul Petralia, Eric Willner, Anne Hunt, Lauren Poplawski, Becky Cowan and Karthika Sridharan have been most helpful. We were also supported by Margarita Maksotskaya at Auburn University, who provided splendid assistance in bringing this book project to a successful conclusion. Our colleagues are thanked for reading the book in manuscript. Pouria Oliazadeh read every chapter and provided helpful comments throughout. In addition, Gene Chung, Steven Hambric, Colin Hansen, David Herrin, Reginald Keith, Robin Langley, Florent Masson and Tomas Ulrich, kindly acted as reviewers of selected chapters with which they had expertise. The first author is grateful for the support of Richard H. Lyon, a brilliant acoustician and one of the originators and developers of SEA. The first author learned so much about the subject from him. Richard Lyon almost was an unofficial advisor for the first author's PhD dissertation and section 12.5 of Chapter 12 is dedicated to Dr. Lyon's memory. Last and not least, the authors thank their wives, Ruth Crocker and Ester Arteaga for their support, patience and understanding during the lengthy period of preparation of this book.

1 Introduction

      1.1 Introduction

      Real‐world problems in the control of noise and vibration in aircraft, appliances, buildings, industry, and vehicles require the measurement of particular environmental parameters such as sound pressure, force, acceleration, velocity, displacement, etc. This process is often performed by using acoustical and vibration transducers. Vibration and acoustical sensors are transducers which convert a measured physical property (e.g. the vibration of a body or the propagation of a sound wave) into an electrical signal (voltage or charge). These electrical signals are often conditioned to provide signals suitable for the measurement devices. The signals are then amplified, attenuated, or transformed so that they can subsequently be analyzed and/or processed to provide the data of particular interest in the time domain and frequency domain. The information provided by these analyses is widely used to assess sources of noise and vibration, and design proper engineering control measures. For some cases, such as simple measurements of the A‐weighted sound pressure level, only limited amounts of processing are needed. In other cases with more sophisticated measurements, special analysis and processing is required. Such examples include modal analysis, sound intensity, wavelet analysis, machinery condition monitoring, beamforming, and acoustical holography, with which quite complicated signal analysis and processing may be needed. Some years ago, almost all measurements were made with analog equipment. Many analog instruments are still in use around the world. However, by using analog‐to‐digital conversion, increasing use is now made of digital signal processing to extract the required data. This is done either in dedicated instruments or by transferring measurement results onto computers for later processing by software.

      Real‐time analysis in the frequency domain has many applications, including noise and vibration studies where the signal is nonstationary with time. Such applications include machinery vibration analysis, bearing noise, transient analysis, acoustic emission, speech analysis, music, and others. The goal of this chapter is to define the main types of signals used in noise, shock, and vibration control and also to serve as an introduction to signal analysis. The discussion in this chapter is kept mainly descriptive and those readers requiring a further mathematical discussion of signal analysis are referred to more detailed treatments available in several books [1–9].

1.2 Types of Noise and Vibration Signals

Depending on their time histories, noise and vibration signals can basically be divided into stationary and nonstationary. Examples of the various types of signals in the time and frequency domains are shown in Figure 1.1 [10].

Figure 1.1 Examples of different types of signals and their spectral content [10].

      1.2.1 Stationary Signals

      Stationary signals can be divided into deterministic and random signals. Stationary deterministic signals can be described by a mathematical function. They are made of a combination of sinusoidal signals (pure tones) with different amplitudes and frequencies. The spectrum of a stationary deterministic signal is characterized by content (power) at discrete frequencies (a line spectrum). The measured displacement signal of a simple mass‐spring system and the noise and vibration signals from machinery rotating at constant speeds are both examples of stationary deterministic signals.

      Stationary random signals cannot be described by explicit mathematical functions but instead they must be described by their statistical properties (mean value, variance, standard deviation, crest factor, kurtosis, amplitude probability, etc.). In contrast to stationary deterministic signals, they have a continuous distribution of spectral content. If the random signal has constant statistical values which do not change with time, we refer to the random signal as stationary. The sound produced by rain or a waterfall, the noise produced by turbulent air coming out of a ventilation system, the noise produced by a passing vehicle, and the airborne noise of a circular saw during idle are examples of random signals. A random signal which has a flat (constant) spectral content over a wide frequency range is called white noise.

      1.2.2 Nonstationary Signals

      Nonstationary signals are divided into transient and continuous