This paper studies the problem of optimal sensor placement for impact detection and location in composite materials. The study involves a simple impact experiment on a composite box panel. The time-varying strain data are measured using piezoceramic sensors. An effective impact detection procedure is established using a neural network approach. The procedure determines the location and amplitude of impacts. A genetic algorithm is used to determine the optimum sensor positions for a diagnostic system. The main object of the paper is to study fail-safe distributions, i.e. those whose sub-distributions also have a low probability of detection error. The results are validated against an exhaustive search. The study shows that genetic algorithms combined with neural networks can be effectively used to find near-optimal sensor distributions for damage detection. The methods presented are generic and can be used in similar sensor position problems.

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(2016) 'Fostering active network management through SMEs'practises.', Energy eciency., 9 (3). pp. 591-604.Further information on publisher's website:http://dx.doi.org/10.1007/s12053-015-9382-y Publisher's copyright statement:The nal publication is available at Springer via http://dx.doi.org/10.1007/s12053-015-9382-y Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractManaging the electricity network through 'smart grid' systems is a key strategy to address challenges of energy security, low carbon transitions and the replacement of aging infrastructure networks in the UK.Small and Medium Enterprises (SMEs) have a significant role in shaping patterns of energy consumption.Understanding how their activities interrelate with changes in electricity systems is critical for active network management. A significant challenge for the transformation of electricity systems involves comprehending the complexity that stems from the variety of commercial activities and diversity of social and organisational practices amongst SMEs that interact with material infrastructures. We engage with SMEs to consider how smart grid interventions 'fit' into everyday operational activities. Drawing on analysis of empirical data on electricity use, smart meter data, surveys, interviews and 'energy tours' with SMEs to understand lighting, space heating and cooling, refrigeration and IT use, this paper argues for experimenting with the use of practice theory as a framework for bringing together technical and social aspects of energy use in SMEs. This approach reveals that material circumstances and temporal factors shape current energy demand amongst SMEs, with 'connectedness' an emergent factor.

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A common method of reducing vibration levels in structures is to coat the surfaces of the structure with a layer of viscoelastic material which dissipates the elastic strain energy induced by the dynamic loading. The passive damping material is placed in areas of high strain energy to extract as much energy as possible from the structure, but the exact location of the material can tend to be arbitrary. A more systematic approach is demonstrated which uses an evolutionary method which allows viscoelastic surface coatings to be gradually built up until the required damping is achieved. In this way the material can be placed in a more efficient manner, and it is proposed that this leads to a better damping performance.

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