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A turbulent combustion noise model based on first principles is developed in this thesis. The model predicts (1) the pressure time series, (2) Sound Pressure Level (SPL) spectrum, (3) Over-All Sound Pressure Level (OASPL), (4) the thermoacoustic efficiency, (5) the peak frequency, and (6) the sound power of combustion generated noise. In addition, a correlation for sound power is developed based on fundamental burner and fuel variables known to affect the acoustic characteristics of turbulent combustion. The predicted pressure time series exhibits consistency with reality in that it has no steady component. It also confirms speculation in the literature that the predominant noise mechanism in open turbulent flames results from a "transition burning" phenomenon at the flame front. The predicted Sound Pressure Level spectrum, Over-All Sound Pressure Level, and the thermoacoustic efficiency are in excellent agreement with the results available in the literature. The shifts in the peak frequency with basic burner and fuel parameters are consistent with experimental observations from the literature. The disagreements between the predicted and the observed exponents of fuel and burner parameters for sound power are shown to be well within the standard deviation of the experimental observations. Certain areas for further analytical research on the combustion noise mechanism are identified. / Master of Science

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1977. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by Ulick Oliver Malone. / M.S.

M.Cur. / Advances in medical technology have led to major technological developments in the field of neonatal care. Over the past three decades there has been increasing concern about noise levels in neonatal intensive care units. The experience of working in a neonatal intensive care unit and exposure to high noise levels of such a unit prompted the researcher to investigate the sources of noise further and to explore ways of reducing or preventing the occurrence of noise. After conducting an extensive literature review, a systematic measurement of noise levels in a neonatal intensive care unit was undertaken. The following findings emerged:- There is a considerable level of noise in the neonatal intensive care unit and this noise persists throughout day and night. The sources of noise are mainly environmental, equipment, personnel and patientrelated. It was significant to observe that neonatal personnel can contribute to the reduction or prevention of high noise levels in the unit. Guidelines for neonatal personnel have been developed to this end. It is hoped that this study will make a contribution to the creation of a more conducive neonatal environment.

An acoustic pattern recognition system has been developed to identify aerospace acoustic sources. The system is capable of classifying five different types of air and ground sources: jets, propeller planes, helicopters, trains, and wind turbines. The system consists of one microphone for data acquisition, a preprocessor, a feature selector, and a classifier. This thesis presents two new classifiers, one based on an associative memory and one on artificial neural networks, and compares their performance to that of the original classifier developed at VPI&SU (1,2). The acoustic patterns are classified using features that have been calculated from the time and frequency domains. Each of the classifiers undergoes a training period during which a set of known patterns is used to teach the classifier to classify unknown patterns correctly. Once training was completed each classifier is tested using a new set of unknown data. Two different classifier structures were tested, a single level structure and a tree structure. Results show that the single level associative memory and artificial neural network classifiers each identified 90.6 percent of the acoustic sources correctly. The original linear discriminant function single level classifier (1,2) identified 86.7 percent of the sources. The tree structure classifiers classified respectively 90.6 percent, 91.8 percent, and 90.1 percent of the sources correctly. / Master of Science