Acoustic emission frequency discrimination

In acoustic emission nondestructive testing, broadband frequency noise is distinguished from narrow banded acoustic emission signals, since the latter are valid events indicative of structural flaws in the material being examined. This is accomplished by separating out those signals which contain frequency components both within and beyond (either above or below) the range of valid acoustic emission events. Application to acoustic emission monitoring during nondestructive bond verification and proof loading of undensified tiles on the Space Shuttle Orbiter is considered

Comparative studies on Mopeia viruses and other Arenaviridae, particularly Lassa virus

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Serologically related arenaviruses have been isolated from West Africa, Mozambique, Zimbabwe and the Central African Republic. Human disease is only associated with the West African isolates. The virulence of Mozambique, Zimbabwe and Central African Republic isolates in humans is not known. This Thesis is an account of work carried out by the author to compare the biological characteristics of isolates from West Africa, Mozambique and Zimbabwe. It describes the successful isolation and identification of the aetiological agents, their physicochemical and antigenic characteristics and describes in vivo studies using mice, guinea pigs and Rhesus monkeys. A direct comparison was made with a patient diagnosed as having Lassa fever. The disease in man and monkeys following infection with Lassa virus was similar. The Rhesus monkey and guinea pig proved suitable experimental models in which to study and compare the pathogenic responses and also to evaluate various aspects of protection. These animal models when immunised with the viruses from Mozambique and Zimbabwe were protected when subsequently challenged with Lassa virus. The Mozambique and Zimbabwe isolates proved to have morphological and physicochemical characteristics not dissimilar from West African Lassa viruses and those members of the arenavirus family from South America. Serological and immunochemical investigations suggest the existence of both common and unique antigenic determinants on the viruses from Mozambique, -Zimbabwe and West Africa. This grouping also coincides with the geographic separation of the viruses, i.e. Lassa - West Africa and Mopeia -southeast Africa. Similar differences in host susceptibility have also been demonstrated. Lassa virus produces a fatal haemorrhagic disease while Mopeia isolates produce only an asymptomatic infection. The combined data suggests the possibility of two virus groups within the 'Old World' arenavirus classification. The proposed name, 'Mopeia', forms one group and includes the viruses from Mozambique and Zimbabwe. The Lassa strains from West Africa form the second group. It is suggested that the Mopeia viruses are minor antigenic variants of Lassa and should be included within the arenavirus family

Acoustic Emission in Composites Using MPA

The purpose of this study is to try to determine the current mechanical state of a composite specimen (or structure) and predict its remaining lifetime from the characteristics of the acoustic emission (AE) signals it emits under load. In previous studies of the characteristics of AE generated in graphite-epoxy composites, empirical observations were made relating the frequency content and the amplitude distributions of the AE signals to singular points on the loading curve of a specimen and to the composite\u27s, moisture content. Up to now, these relationships have been difficult to study systematically because of limitations in efficiently handling the large amount of data contained in the emission signals. With the Acoustic Emission Multi-Parameter Analyzer (AEMPA) (developed under a Science Center IR&D program), pertinent information is abstracted from each emission signal as it occurs during a test and is stored in compact digital form for subsequent data processing. Multi-parameter correlation and pattern recognition techniques among the 23 abstracted parameters are then used to identify distinct types of AE events, and various observations used ,to \u27identify the microscopic mechanisms of flaw growth in the material which generate these different\u27types. For those geometries and load conditions which product failure by a well defined series of mechanistic steps (e.g., matrix crazing, fiber-matrix interface debonding, fiber fracture, interlaminar fracture), it may be possible to predict specimen·failure by determining the relative amounts of the various mechanisms occurring at a given time in the life of the specimen from the AE signals. Progress along these lines using MPA is described

Sources of Acoustic Emission in Aluminum Alloys

I\u27d like to start the talk with a brief description of this task on acoustic emission (AE) source identification in terms of its immediate aims and ultimate goals. The _immediate aims were to, first, identify the sources of AE in a variety of materials, making a survey of just where they originate, and their dependence on changes in microstructure. The second aim was to identify characteristics of the AE signals which might be related to these sources and, therefore, indirectly to the microstructural effects. These aims were realized for the materials studied. The ultimate goals of such a study would be the extrapolation of AE data from one test situation or from one material to another, and, ideally, to relate the emissions to a determination of flaw criticality

Statistical Evaluation of Sources of Acoustic Emission in Composites

Acoustic emission (AE) signals that are generated by different microscopic processes during flaw growth in graphite-epoxy specimens have measurably different characteristics. In particular, the amplitudes of the emissions and a parameter that describes their frequency spectral content seem to give the most information about the processes. These parameters have a range of values for a given process which can be described by certain types of analytical distribution functions. When several processes occur simultaneously during flaw growth, such as epoxy crazing, fiber fracture, fiber-matrix disband and interlaminar cleavage, the distributions in the values of the AE parameters generally overlap so that identification of an individual AE signal as being caused by a particular process is not possible. However, statistical evaluation of the data for a few hundred events in terr.ls of the analytical distributions, once the shape and modal value of these distributions are defined for each process, should provide a quantitative measure of the relative amounts of the various processes which occurred. Analyses of many data sets are required to develop confidence in the decomposed distributions as being descriptive of the individual processes. The ultimate purpose for this determination is to provide a description of the stage of flaw growth from the quantitative knowledge of the types and the amounts of the microscopic processes which occurred

Characterization of Acoustic Emission Signals and Application to Composite Structures Monitoring

The objectives of this study were first, to identify characteristics of the acoustic emission signals from graphite-epoxy composites which could be related to the various fracture mechanisms, and second, to determine how these are related to the history of the flaw growth and to the degree of degradation of the strength of the composites due to moisture

Ultrasonic Wave Interactions with Interfaces

The objective of the work that I will describe was to determine experimentally how a sound wave interacts with a layer whose thickness is measured in atomic sized units. That is, the interface layers are measured in hundreds of angstroms and are thus much, much thinner than any of the millimeter or tenths of millimeter kinds of wave lengths that we ordinarily have, or can hope to have to interrogate the bond line. The question we must address is; How does a very long wave length ultrasonic wave interact with a very thin layer or collection of layers? Or; When we get a signal back from an interface, how should we unfold it to learn something about the nature of the interface