Fatigue in rubber vulcanisates

A study has been made of the effect of single extensions and continuous fatigue on the structures of various natural rubber networks. The change in network structure of a conventional vulcanisate on a single extension manifests itself as permanent set. The change in network structure has been assessed by the use of the chemical probes, propan-2-thiol/piperidine, hexane-thiol/piperidine and triphenyl phosphine, which determine the polysulphide and disulphide crosslink densities and main chain modification respectively. The permanent set induced on a single extension of a conventional sulphur vulcanisate has been shown to result from the destruction and reformation of polysulphide crosslinks. The magnitude of the effect was dependent upon the degree of extension and showed a maximum at extensions corresponding to the onset of stress-induced crystallisation. The incorporation of a reinforcing filler, HAF-carbon black, magnified the effect. Vulcanisates that possessed only mono and disulphide crosslinks did not show any significant permanent set. The continuous changes in network structure during fatigue have also been determined, and the effects of carbon black and antioxidants on these changes and the fatigue life have been assessed. During fatigue the overall crosslink density increased slightly, which resulted from the destruction of polysulphide crosslinks. and their replacement by principally disulphide crosslinks. Antioxidants reduced the rate of destruction of polysulphide crosslinks and increased the fatigue life of the rubber network. The fatigue life of the network also depended upon the concentration of free chain ends. These chain ends were incorporated into the network by masticating rubber under nitrogen in the presence of bis (diisopropyl)thiophosphoryl disulphide, which improved the fatigue resistance by up to 9%.

A network coding based interference cancelation scheme for wireless ad hoc networks

The performance of wireless networks is limited by multiple access interference (MAI) in the traditional communication approach where the interfered signals of the concurrent transmissions are treated as noise. In this paper, we treat the interfered signals from a new perspective on the basis of additive electromagnetic (EM) waves and propose a network coding based interference cancelation (NCIC) scheme. In the proposed scheme, adjacent nodes can transmit simultaneously with careful scheduling; therefore, network performance will not be limited by the MAI. Additionally we design a space segmentation method for general wireless ad hoc networks, which organizes network into clusters with regular shapes (e.g., square and hexagon) to reduce the number of relay nodes. The segmentation methodworks with the scheduling scheme and can help achieve better scalability and reduced complexity. We derive accurate analytic models for the probability of connectivity between two adjacent cluster heads which is important for successful information relay. We proved that with the proposed NCIC scheme, the transmission efficiency can be improved by at least 50% for general wireless networks as compared to the traditional interference avoidance schemes. Numeric results also show the space segmentation is feasible and effective. Finally we propose and discuss a method to implement the NCIC scheme in a practical orthogonal frequency division multiplexing (OFDM) communications networks. Copyright © 2009 John Wiley & Sons, Ltd.

Dynamic causal modeling of load-dependent modulation of effective connectivity within the verbal working memory network

Neuroimaging studies have consistently shown that working memory (WM) tasks engage a distributed neural network that primarily includes the dorsolateral prefrontal cortex, the parietal cortex, and the anterior cingulate cortex. The current challenge is to provide a mechanistic account of the changes observed in regional activity. To achieve this, we characterized neuroplastic responses in effective connectivity between these regions at increasing WM loads using dynamic causal modeling of functional magnetic resonance imaging data obtained from healthy individuals during a verbal n-back task. Our data demonstrate that increasing memory load was associated with (a) right-hemisphere dominance, (b) increasing forward (i.e., posterior to anterior) effective connectivity within the WM network, and (c) reduction in individual variability in WM network architecture resulting in the right-hemisphere forward model reaching an exceedance probability of 99% in the most demanding condition. Our results provide direct empirical support that task difficulty, in our case WM load, is a significant moderator of short-term plasticity, complementing existing theories of task-related reduction in variability in neural networks. Hum Brain Mapp, 2013. © 2013 Wiley Periodicals, Inc.

A simulation study of service differentiation for the bandwidth request scheme in fixed IEEE 802.16 network

Various flexible mechanisms related to quality of service (QoS) provisioning have been specified for uplink traffic at the medium access control (MAC) layer in the IEEE 802.16 standards. Among the mechanisms, contention based bandwidth request scheme can be used to indicate bandwidth demands to the base station for the non-real-time polling and best-effort services. These two services are used for most applications with unknown traffic characteristics. Due to the diverse QoS requirements of those applications, service differentiation (SD) is anticipated over the contention based bandwidth request scheme. In this paper we investigate the SD with the bandwidth request scheme by means of assigning different channel access parameters and bandwidth allocation priorities at different packets arrival probability. The effectiveness of the differentiation schemes is evaluated by simulations. It is observed that the initial backoff window can be efficient in SD, and if combined with the bandwidth allocation priority, the SD performances will be better.

Role of reinforcement/matrix interfacial strength in fatigue crack propagation in particulate SiC reinforced aluminum alloy 8090

A study has been made of the influence of the reinforcement/matrix interfacial strength on fatigue crack propagation in a powder metallurgy aluminum alloy 8090-SiC particulate composite. The interfacial region has been altered by two separate routes, the first involving aging of the 8090 matrix, with the subsequent formation of precipitate free zones at the boundaries, and the second consisting of oxidizing the surface of the SiC particles before their incorporation into the composite. In the naturally aged condition, oxidation of the SiC leads to a reduction in fatigue crack growth resistance at higher values of stress intensity range ΔK. This is due to a proportion of the crack growth occurring through voids formed in association with many of the weak SiC interfaces which have retained a layer of thick surface oxide after processing. On overaging no difference in crack growth rate is discernible between the oxidized and unoxidized SiC composites. It is proposed that this is due to similar levels of interfacial weakening having occurred in both composites, indicating that this is an important factor in the reduction of the high ΔK crack growth resistance of the unoxidized SiC composite on aging.

Fatigue crack propagation in nickel-base superalloys:effects of microstructure, load ratio, and temperature

The current state of knowledge and understanding of the long fatigue crack propagation behavior of nickel-base superalloys are reviewed, with particular emphasis on turbine disk materials. The data are presented in the form of crack growth rate versus stress intensity factor range curves, and the effects of such variables as microstructure, load ratio, and temperature in the near-threshold and Paris regimes of the curves, are discussed.

Fatigue crack growth behaviour in molten-metal processed SiC particle-reinforced aluminium alloys

Fatigue crack initiation and subsequent short crack growth behaviour of 2014-5wt%SiC aluminium alloy composites has been examined in 4-point bend loading using smooth bar specimens. The growth rates of long fatigue cracks have also been measured at different stress ratios using pre-cracked specimens. The distributions of SiC particles and of coarse constituent particles in the matrix (which arise as a result of the molten-metal processing and relatively slow cooling rate) have been investigated. Preferential crack initiation sites were found to be SiC-matrix interfaces, SiC particles associated with constituent particles and the coarse constituent particles themselves. For microstructurally short cracks the dispersed SiC particles also act as temporary crack arresters. In the long crack growth tests, higher fatigue crack growth rates were obtained than for monolithic alloys. This effect is attributed to the contribution of void formation, due to the decohesion of SiC particles, to the fatigue crack growth process in the composite. Above crack depths of about 200 μm 'short' crack growth rates were in good agreement with the long crack data, showing a Pris exponent, m = 4 in both cases. For the long crack and short crack growth tests little effect of specimen orientation and grain size was observed on fatigue crack growth rates, but, specimen orientation affected the toughness. No effect of stress ratio in the range R = 0.2-0.5 was seen for long crack data in the Paris region.

Effects of dynamic strain ageing on fatigue crack propagation and threshold behaviour in a Ni-base superalloy

In the temperature range 200-400 degree C the Ni-base superalloy, N901, develops marked dynamic strain ageing effects in its tensile behavior. These include inverse strain rate sensitivity, especially in UTS values, strongly serrated stress-strain curves and a heavily sheared failure mode at the higher test-temperatures. As for steels these effects seem to be due to interactions between the dislocations and the interstitial carbon atoms present. The results of tensile and fatigue threshold tests carried out between 20 degree C and 420 degree C are reported and the fatigue behavior is discussed in terms of the effects of surface roughness induced closure, temperature and strain aging interactions.

Study on fatigue and energy-dissipation properties of nanolayered Cu/Nb thin films

Energy dissipation and fatigue properties of nano-layered thin films are less well studied than bulk properties. Existing experimental methods for studying energy dissipation properties, typically using magnetic interaction as a driving force at different frequencies and a laser-based deformation measurement system, are difficult to apply to two-dimensional materials. We propose a novel experimental method to perform dynamic testing on thin-film materials by driving a cantilever specimen at its fixed end with a bimorph piezoelectric actuator and monitoring the displacements of the specimen and the actuator with a fibre-optic system. Upon vibration, the specimen is greatly affected by its inertia, and behaves as a cantilever beam under base excitation in translation. At resonance, this method resembles the vibrating reed method conventionally used in the viscoelasticity community. The loss tangent is obtained from both the width of a resonance peak and a free-decay process. As for fatigue measurement, we implement a control algorithm into LabView to maintain maximum displacement of the specimen during the course of the experiment. The fatigue S-N curves are obtained.

Modular ESS with second life batteries operating in grid independent mode

This work is part of a bigger project which aims to research the potential development of commercial opportunities for the re-use of batteries after their use in low carbon vehicles on an electricity grid or microgrid system. There are three main revenue streams (peak load lopping on the distribution Network to allow for network re-enforcement deferral, National Grid primary/ secondary/ high frequency response, customer energy management optimization). These incomes streams are dependent on the grid system being present. However, there is additional opportunity to be gained from also using these batteries to provide UPS backup when the grid is no longer present. Most UPS or ESS on the market use new batteries in conjunction with a two level converter interface. This produces a reliable backup solution in the case of loss of mains power, but may be expensive to implement. This paper introduces a modular multilevel cascade converter (MMCC) based ESS using second-life batteries for use on a grid independent industrial plant without any additional onsite generator as a potentially cheaper alternative. The number of modules has been designed for a given reliability target and these modules could be used to minimize/eliminate the output filter. An appropriate strategy to provide voltage and frequency control in a grid independent system is described and simulated under different disturbance conditions such as load switching, fault conditions or a large motor starting. A comparison of the results from the modular topology against a traditional two level converter is provided to prove similar performance criteria. The proposed ESS and control strategy is an acceptable way of providing backup power in the event of loss of grid. Additional financial benefit to the customer may be obtained by using a second life battery in this way.

Sensitivity and Bias within the Binary Signal Detection Theory, BSDT

Similar to classic Signal Detection Theory (SDT), recent optimal Binary Signal Detection Theory (BSDT) and based on it Neural Network Assembly Memory Model (NNAMM) can successfully reproduce Receiver Operating Characteristic (ROC) curves although BSDT/NNAMM parameters (intensity of cue and neuron threshold) and classic SDT parameters (perception distance and response bias) are essentially different. In present work BSDT/NNAMM optimal likelihood and posterior probabilities are analytically analyzed and used to generate ROCs and modified (posterior) mROCs, optimal overall likelihood and posterior. It is shown that for the description of basic discrimination experiments in psychophysics within the BSDT a ‘neural space’ can be introduced where sensory stimuli as neural codes are represented and decision processes are defined, the BSDT’s isobias curves can simultaneously be interpreted as universal psychometric functions satisfying the Neyman-Pearson objective, the just noticeable difference (jnd) can be defined and interpreted as an atom of experience, and near-neutral values of biases are observers’ natural choice. The uniformity or no-priming hypotheses, concerning the ‘in-mind’ distribution of false-alarm probabilities during ROC or overall probability estimations, is introduced. The BSDT’s and classic SDT’s sensitivity, bias, their ROC and decision spaces are compared.

The B-Terminal Busy Probability Prediction

In the teletraffic engineering of all the telecommunication networks, parameters characterizing the terminal traffic are used. One of the most important of them is the probability of finding the called (B-terminal) busy. This parameter is studied in some of the first and last papers in Teletraffic Theory. We propose a solution in this topic in the case of (virtual) channel systems, such as PSTN and GSM. We propose a detailed conceptual traffic model and, based on it, an analytical macro-state model of the system in stationary state, with: Bernoulli– Poisson–Pascal input flow; repeated calls; limited number of homogeneous terminals; losses due to abandoned and interrupted dialling, blocked and interrupted switching, not available intent terminal, blocked and abandoned ringing and abandoned conversation. Proposed in this paper approach may help in determination of many network traffic characteristics at session level, in performance evaluation of the next generation mobile networks.

Bayesian neural network approach for determining the risk of re-intervention after endovascular aortic aneurysm repair

This article proposes a Bayesian neural network approach to determine the risk of re-intervention after endovascular aortic aneurysm repair surgery. The target of proposed technique is to determine which patients have high chance to re-intervention (high-risk patients) and which are not (low-risk patients) after 5 years of the surgery. Two censored datasets relating to the clinical conditions of aortic aneurysms have been collected from two different vascular centers in the United Kingdom. A Bayesian network was first employed to solve the censoring issue in the datasets. Then, a back propagation neural network model was built using the uncensored data of the first center to predict re-intervention on the second center and classify the patients into high-risk and low-risk groups. Kaplan-Meier curves were plotted for each group of patients separately to show whether there is a significant difference between the two risk groups. Finally, the logrank test was applied to determine whether the neural network model was capable of predicting and distinguishing between the two risk groups. The results show that the Bayesian network used for uncensoring the data has improved the performance of the neural networks that were built for the two centers separately. More importantly, the neural network that was trained with uncensored data of the first center was able to predict and discriminate between groups of low risk and high risk of re-intervention after 5 years of endovascular aortic aneurysm surgery at center 2 (p = 0.0037 in the logrank test).

A multi-modular second life hybrid battery energy storage system for utility grid applications

The modern grid system or the smart grid is likely to be populated with multiple distributed energy sources, e.g. wind power, PV power, Plug-in Electric Vehicle (PEV). It will also include a variety of linear and nonlinear loads. The intermittent nature of renewable energies like PV, wind turbine and increased penetration of Electric Vehicle (EV) makes the stable operation of utility grid system challenging. In order to ensure a stable operation of the utility grid system and to support smart grid functionalities such as, fault ride-through, frequency response, reactive power support, and mitigation of power quality issues, an energy storage system (ESS) could play an important role. A fast acting bidirectional energy storage system which can rapidly provide and absorb power and/or VARs for a sufficient time is a potentially valuable tool to support this functionality. Battery energy storage systems (BESS) are one of a range suitable energy storage system because it can provide and absorb power for sufficient time as well as able to respond reasonably fast. Conventional BESS already exist on the grid system are made up primarily of new batteries. The cost of these batteries can be high which makes most BESS an expensive solution. In order to assist moving towards a low carbon economy and to reduce battery cost this work aims to research the opportunities for the re-use of batteries after their primary use in low and ultra-low carbon vehicles (EV/HEV) on the electricity grid system. This research aims to develop a new generation of second life battery energy storage systems (SLBESS) which could interface to the low/medium voltage network to provide necessary grid support in a reliable and in cost-effective manner. The reliability/performance of these batteries is not clear, but is almost certainly worse than a new battery. Manufacturers indicate that a mixture of gradual degradation and sudden failure are both possible and failure mechanisms are likely to be related to how hard the batteries were driven inside the vehicle. There are several figures from a number of sources including the DECC (Department of Energy and Climate Control) and Arup and Cenex reports indicate anything from 70,000 to 2.6 million electric and hybrid vehicles on the road by 2020. Once the vehicle battery has degraded to around 70-80% of its capacity it is considered to be at the end of its first life application. This leaves capacity available for a second life at a much cheaper cost than a new BESS Assuming a battery capability of around 5-18kWhr (MHEV 5kWh - BEV 18kWh battery) and approximate 10 year life span, this equates to a projection of battery storage capability available for second life of >1GWhrs by 2025. Moreover, each vehicle manufacturer has different specifications for battery chemistry, number and arrangement of battery cells, capacity, voltage, size etc. To enable research and investment in this area and to maximize the remaining life of these batteries, one of the design challenges is to combine these hybrid batteries into a grid-tie converter where their different performance characteristics, and parameter variation can be catered for and a hot swapping mechanism is available so that as a battery ends it second life, it can be replaced without affecting the overall system operation. This integration of either single types of batteries with vastly different performance capability or a hybrid battery system to a grid-tie 3 energy storage system is different to currently existing work on battery energy storage systems (BESS) which deals with a single type of battery with common characteristics. This thesis addresses and solves the power electronic design challenges in integrating second life hybrid batteries into a grid-tie energy storage unit for the first time. This study details a suitable multi-modular power electronic converter and its various switching strategies which can integrate widely different batteries to a grid-tie inverter irrespective of their characteristics, voltage levels and reliability. The proposed converter provides a high efficiency, enhanced control flexibility and has the capability to operate in different operational modes from the input to output. Designing an appropriate control system for this kind of hybrid battery storage system is also important because of the variation of battery types, differences in characteristics and different levels of degradations. This thesis proposes a generalised distributed power sharing strategy based on weighting function aims to optimally use a set of hybrid batteries according to their relative characteristics while providing the necessary grid support by distributing the power between the batteries. The strategy is adaptive in nature and varies as the individual battery characteristics change in real time as a result of degradation for example. A suitable bidirectional distributed control strategy or a module independent control technique has been developed corresponding to each mode of operation of the proposed modular converter. Stability is an important consideration in control of all power converters and as such this thesis investigates the control stability of the multi-modular converter in detailed. Many controllers use PI/PID based techniques with fixed control parameters. However, this is not found to be suitable from a stability point-of-view. Issues of control stability using this controller type under one of the operating modes has led to the development of an alternative adaptive and nonlinear Lyapunov based control for the modular power converter. Finally, a detailed simulation and experimental validation of the proposed power converter operation, power sharing strategy, proposed control structures and control stability issue have been undertaken using a grid connected laboratory based multi-modular hybrid battery energy storage system prototype. The experimental validation has demonstrated the feasibility of this new energy storage system operation for use in future grid applications.

A Bayesian neural network algorithm identifies patients in whom post-EVAR surveillance may be unnecessary

Lifelong surveillance is not cost-effective after endovascular aneurysm repair (EVAR), but is required to detect aortic complications which are fatal if untreated (type 1/3 endoleak, sac expansion, device migration). Aneurysm morphology determines the probability of aortic complications and therefore the need for surveillance, but existing analyses have proven incapable of identifying patients at sufficiently low risk to justify abandoning surveillance. This study aimed to improve the prediction of aortic complications, through the application of machine-learning techniques. Patients undergoing EVAR at 2 centres were studied from 2004–2010. Aneurysm morphology had previously been studied to derive the SGVI Score for predicting aortic complications. Bayesian Neural Networks were designed using the same data, to dichotomise patients into groups at low- or high-risk of aortic complications. Network training was performed only on patients treated at centre 1. External validation was performed by assessing network performance independently of network training, on patients treated at centre 2. Discrimination was assessed by Kaplan-Meier analysis to compare aortic complications in predicted low-risk versus predicted high-risk patients. 761 patients aged 75 +/− 7 years underwent EVAR in 2 centres. Mean follow-up was 36+/− 20 months. Neural networks were created incorporating neck angu- lation/length/diameter/volume; AAA diameter/area/volume/length/tortuosity; and common iliac tortuosity/diameter. A 19-feature network predicted aor- tic complications with excellent discrimination and external validation (5-year freedom from aortic complications in predicted low-risk vs predicted high-risk patients: 97.9% vs. 63%; p < 0.0001). A Bayesian Neural-Network algorithm can identify patients in whom it may be safe to abandon surveillance after EVAR. This proposal requires prospective study.