Pre-built pre-assured components are the key to building most types of high reliability engineering systems at reasonable cost, but software engineering has not found a way to use this approach. Technologies exist for building software by connecting together pre-built components but this only addresses one part of the problem. The key additional requirement is to control the reliability of the resulting software programs. New statistical reliability models appear to offer the breakthrough required to achieve this. The proposal is to assess the theoretical and practical feasibility of using these models to provide a powerful new reliability assurance method for critical software.

This project aims to develop a powerful and novel computational simulation tool for predicting the large-scale (macroscopic) response of complex materials. In particular this project is concerned with the simulation of materials that are diverse or non-uniform in nature (heterogeneous). In addition to the solid, these materials may also contain regions of liquid and/or gas (multi-phase).In order to understand and simulate the large scale response of such materials that are exposed to external and internal forces, heating and/or drying, it is necessary to identify and simulate the underlying physical processes that are taking place inside the material at a small scale (micro-scale) and to take account of the complex nature of the structure of the material at this small scale.Traditionally, engineers and scientists have described the large-scale behaviour of materials by simulating their observed response without reference to the underlying processes or material composition. The proposed analysis tool aims to describe the large-scale response indirectly by simulating the processes that are taking place at the small scale. However, any attempt to model every material detail of a large scale problem is unrealistic and therefore each region of the material will be represented by a realistic small-scale description. The response of this representative part to loading will then be scaled up to the large scale. In this way the large-scale response of the material is simulated by processes that are taking place at the smallscale.This project will extend existing upscaling techniques that are applicable to purely mechanical behaviour to include coupling with heat and mass (liquid and gas) transport processes. Such a technique will permit the simulation of solids subject to extreme environmental conditions, such as heating (e.g. fire), and the effect of liquid and/or gas that occupy voids in the material. Furthermore, the research will consider how these processes change as the material composition changes. New techniques for modelling material interfaces and fractures will also be adopted.The modelling framework to be developed will be applicable to a large class of heterogeneous materials (e.g. cementitious composites, biological tissues, rocks, soils, metal composites and vegetative materials) whose large-scale behaviour cannot be interpreted without consideration of the complex processes occurring at smaller scales.

Before high voltage plant fails there is generally a period when degradation of the insulation system occurs, this may be a number of years. The key to improving the assessment of the equipment condition and life expectancy lies in identifying and characterising the stages of degradation. It is widely recognised that the degradation phase, irrespective of the cause, results in small sparks being generated at the site(s) of degradation. These electric sparks are generally referred to as partial discharges(PD). The characteristics of the sparks are influenced by the materials and stresses at the fault site. Improvement in their detection and characterisation will provide information on the location, nature, form and extent of degradation.The current detection process is severely compromised in practical on-site testing. These PD pulses are extremely small and hence, irrespective of the particular strategy being applied to detect them(electrical or acoustic), detection equipment must be very sensitive. In the field, this makes it prone to the influence or external interference or 'noise' from the surrounding environment and electrical/mechanical infrastructure. At best, this results in data corruption and compromises the efficiency of the condition assessment. At worst, it stops the technique from being of any use as the 'noise' signal exceeds the level of partial discharge activity.To solve the problems associated with noise a number of methods have been tried such as: screening and filtering, the application of analogue band-pass filtering, matched filters, polarity discrimination circuitry, time-windowed methods and digital filters. Each of these is, however, applicable to only certain types of noiseIn a recent study the author compared the matched filter, the traditional filter and the Discrete Wavelet Transform (DWT) in PD measurement denoising and has proven DWT provides the best solution in practical measurement when strong noise is in presence. Furthermore, DWT is the only method which allows reconstruction of the PD pulse.Having evolved from the Fourier Transform(FT), WT is particularly designed to analyse transient, irregular and non-periodic signals. Ideally, if a wavelet can be selected to match the PD pulse shape, the PD pulse could be extracted from any strong noise signals. Though the WT generates more information than the FT, it is inherently more complex than the FT and involves procedures dependent on the shape of the signals to be extracted from noisy data, the record length and the sampling rate. Dr. Zhou in the Insulation Diagnostics Group at the GCU was the first to study the optimal selection of the most appropriate wavelets, the optimal number of levels and level-dependent thresholding algorithm for automatic PD pulse extraction from electrically noisy environments using DWT. This innovative work has been proved to be effective in a number of measurement platforms. However, the application of DWT still requires significant experience at the moment when pulses of different shapes exist. The proposed research is to build on the experience and success already gained at GCU and to develop a methodology which allows the DWT to be applied to various PD measurement systems irrespective of their mechanism and bandwidth for PD data denoising and PD pulse reconstruction and classification.The outcome of the proposed research will be algorithms which can identify all types of transient pulses contained in data under analysis and present them separately in time domain. This would allow the identification and classification of various PD activities from PD measurements and production of phi-q-n diagrams which, in conjunction with pulse shapes, provides significantly improved means for plant diagnosis.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Computers increasingly play vital roles in organisations - e.g., hospitals or factories - which thus become computer-based systems . The dependability of these systems is a major societal concern. In response, EPSRC funded the Dependability Interdisciplinary Research Collaboration (DIRC) between City, Edinburgh, Lancaster, Newcastle and York universities. DIRC was based on the premise that dependability must be studied not as a purely technical issue, but as a socio-technical property of the combination of a computing system with the environments in which it is procured, developed and used. DIRC thus assembled a world class interdisciplinary team of computer scientists, psychologists, sociologists and statisticians, which has achieved substantial results through a rare degree of collaboration between engineering and social sciences.INDEED will build on DIRC's results to address important challenges in extending these results and combining them with current practices, to ensure a real, long-term impact on the design and evaluation of dependable systems. It will apply a multidisciplinary approach in four major research activities:Timing and Structure. This work will further develop DIRC's time band concept for reasoning about processes that unfold on different time scales, from microseconds to days, within a system. We will define an appropriate descriptive language, and extend it to deal with probabilistic relationships between events in different time bands. We will then build a software tool to use in case studies, to validate the use of time bands in structuring dependable systems. Adaptation and diversity. This activity will help designers and assessors of socio-technical systems to address some of the hard problems caused by the difficulty of predicting how people adapt to computers. We will give designers greatly enhanced abilities to analyse quantitatively, control and exploit the phenomena of adaptation and diversity, which although often recognised in informal terms need more thorough and formal treatment. Our focus will be data-rich, knowledge intensive activities that are increasingly supported by automation.Responsibility and trust. Inappropriate allocation or perception of responsibilities, and inappropriate levels of trust in the various system components, are important causes of failure in computer-based systems. This work will support the modelling, management and analysis of responsibility and trust during the design and deployment of such systems, by developing the necessary notations, techniques and software tools.Confidence and Uncertainty in dependability cases. A case is the web of evidence and reasoning through which system dependability is assessed. DIRC defined confidence-based cases, which describe dependability claims together with the degree of confidence that can be had in them. We will produce methods for detailing and structuring cases, using the results of work on time bands; guidance for using more diverse evidence and arguments towards increasing confidence; new interdisciplinary understanding of the factors causing people to trust a case less (or more) than its contents warrant.These activities are integrated into a coherent programme of work. An integration mechanism is the use of real-world case studies where we work with our partners in the project (Voca, British Energy, CAA and Qinetiq) to challenge and validate our research.