Residual stress and fatigue crack growth life prediction in fastener holes cold-worked by uniform indentation in 2024-T351 aluminium alloy

This thesis concerns primarily the residual stress characterisation in fastener holes coldworked by a novel StressWave process, and the prediction of the fatigue crack growth under the influence of such residual stress. Aerospace 2024—T351 aluminium alloy plate of 6.35 mm thickness containing a nominal 06.35 mm hole was used. Using neutron and laboratory X-ray diffraction measurements, a large compressive residual stress was found in StressWave and split-sleeve cold-worked holes. Detailed stress mapping indicates that a StressWave hole contains a highly symmetric residual stress field with a wider compressive region. Conversely, the split-sleeve technique generates a complex asymmetric stress variation through the specimen thickness and around the hole. Independently, a comprehensive finite element study was conducted to reveal the residual stress development associated with the two distinct cold-working techniques at various stages. Favourable agreement was achieved between the experiment and simulations. The deformation mechanism associated with the cold-working process is decisive to the behaviour of the residual stress field created. The symmetric crack growth behaviour observed in StressWave specimens permits a through-thickness crack geometry to be considered. Accordingly, Green’s functions for a single crack and two symmetric cracks originating from the edge of a circular hole were developed. These solutions were verified using weight function and finite element analysis and are therefore appropriate for subsequent study of fatigue crack growth. A theoretical framework was proposed to explicate the interaction of residual stress with the superimposed loading at the crack tip, which was mathematically expounded as a function of stress intensity factor and stress ratio. This analytical framework provides a reasonable correlation between the mean stress and crack closure criteria. As a demonstration, a finite-width plate containing a centre hole with a single crack, with surface residual stress measured by X-Ray diffraction was analysed. It was revealed that for a predictive task, both the mean stress and crack closure definitions necessitate different requirements of material database and parametric definitions. Next, the fatigue testing suggested that the fatigue durability of fastener holes treated by the StressWave method generally outperformed those observed for split-sleeve samples. Prediction according to the unified theory produced encouraging results matching the experimental fatigue crack growth measurement. Detailed analysis showed that suitable parametric calibration and the appropriate use of crack models were imperative to achieve reliable prediction. Future efforts necessary for accuracy of prediction work for StressWave cold-worked holes are discussed

Bioenergy

In contrast to fossil fuels, the use of biofuels for thermal applications and power generation provides significant environmental advantages. Since Bio-Oil is extracted from organic wastes, it is a CO2 Neutral technique and can generate CO2 credits. In the present scenario energy sectors and individual entrepreneurs can opt a new way of power generation using the most abundantly available renewable source of energy in the form of Biomass wastes. Rice husks, groundnut shells, powdery husks, sugar cane (baggasse), corn cobs are some of the carbonaceous biomass fuels. Among the Biomass resources Coconuts are the abundant renewable resource of Energy available all around the world. Literature review showed that limited research studies had been carried out on yielding the product from coconut shell pyrolysis. The objective of present work is to envisage the methodology of generating power from biomass wastes using pyrolysis techniques. Pyrolysis is a thermal decomposition technique which decomposes carbonaceous biowastes into liquids, gases, and char (solid residue) in the absence of oxygen. Bio-Oil can be used as a fuel in diesel engine with modifications in fuel pump, linings, and the injection system. High carbonaceous Bio-Oil extracted from pyrolysis of coconut shell can be used in oil burners for thermal applications and in combustion boilers to generate electricity. Also can be blended with standard diesel fuels to form a pollution free green bio-diesel fuel. Hence biofuels based power generation system would be a boon to the energy crisis in an environmental friendly way using coconut shells for rural electrification

Quantum Illumination with Gaussian States

An optical transmitter irradiates a target region containing a bright thermal-noise bath in which a low-reflectivity object might be embedded. The light received from this region is used to decide whether the object is present or absent. The performance achieved using a coherent-state transmitter is compared with that of a quantum illumination transmitter, i.e., one that employs the signal beam obtained from spontaneous parametric downconversion (SPDC). By making the optimum joint measurement on the light received from the target region together with the retained SPDC idler beam, the quantum illumination system realizes a 6 dB advantage in error probability exponent over the optimum reception coherent-state system. This advantage accrues despite there being no entanglement between the light collected from the target region and the retained idler beam.Comment: 4 pages, 1 figur

Costs and benefits of agricultural price stabilization in Brazil

In recent years, agricultural price stabilization policies have been recommended in Brazil as a way to reduce government intervention and open the sector for international trade without internalizing the instability of world prices. The proposal discussed (and eventually implemented in 1987) was to establish a system of price bands around a moving average of past prices, with the government relying on stocks to defend the bands. The authors evaluated the"band proposal"for six commodities, using historical data and posing this question: what would have happened if price bands had been adopted in the past six to ten years (compared with free trade)? There were two major findings. First, the implications of adopting a band-rule policy depend heavily on the specific characteristics of the commodities. Second, the welfare gains for risk reduction through agricultural price stabilization are unlikely to be large relative to the welfare gains from price reform that reduces market distortions for these six agricultural commodities. More research into the macroeconomic implications of price stabilization policies is necessary, particularly in countries with unstable but moderate rates of inflation.Environmental Economics&Policies,Economic Theory&Research,Markets and Market Access,Access to Markets,Insurance&Risk Mitigation

Extracellular signal-regulated kinases mediate the enhancing effects of inflammatory mediators on resurgent currents in dorsal root ganglion neurons

Previously we reported that a group of inflammatory mediators significantly enhanced resurgent currents in dorsal root ganglion neurons. To understand the underlying intracellular signaling mechanism, we investigated the effects of inhibition of extracellular signal-regulated kinases and protein kinase C on the enhancing effects of inflammatory mediators on resurgent currents in rat dorsal root ganglion neurons. We found that the extracellular signal-regulated kinases inhibitor U0126 completely prevented the enhancing effects of the inflammatory mediators on both Tetrodotoxin-sensitive and Tetrodotoxin-resistant resurgent currents in both small and medium dorsal root ganglion neurons. U0126 substantially reduced repetitive firing in small dorsal root ganglion neurons exposed to inflammatory mediators, consistent with prevention of resurgent current amplitude increases. The protein kinase C inhibitor Bisindolylmaleimide I also showed attenuating effects on resurgent currents, although to a lesser extent compared to extracellular signal-regulated kinases inhibition. These results indicate a critical role of extracellular signal-regulated kinases signaling in modulating resurgent currents and membrane excitability in dorsal root ganglion neurons treated with inflammatory mediators. It is also suggested that targeting extracellular signal-regulated kinases-resurgent currents might be a useful strategy to reduce inflammatory pain

Replay Real World Network Conditions To Test Cellular Switching

A system and method for capturing real-world conditions for designing test cases for testing of network switching algorithms is disclosed. Two devices working on different communication networks are taken while commuting from home to office or vice versa. Different real-world conditions like time, network type: e.g. LTE, 3G, etc. signal strength, location, device state, e.g. moving or stationary etc. are continuously captured on the way. These pieces of information are captured by an app installed on the respective devices. It is feasible to capture additional information that a test case designer is interested in. In the end, the device will have captured a log file of the various conditions encountered. All the scenarios logged can be replayed on a simulation engine evaluating the quality of switching algorithms. A major advantage of the method is to automate test case generation by capturing and replaying actual events, thereby making the process scalable

Cross-Platform Normalization of Microarray and Rna-Seq Data for Machine Learning Applications

Large, publicly available gene expression datasets are often analyzed with the aid of machine learning algorithms. Although RNA-seq is increasingly the technology of choice, a wealth of expression data already exist in the form of microarray data. If machine learning models built from legacy data can be applied to RNA-seq data, larger, more diverse training datasets can be created and validation can be performed on newly generated data. We developed Training Distribution Matching (TDM), which transforms RNA-seq data for use with models constructed from legacy platforms. We evaluated TDM, as well as quantile normalization, nonparanormal transformation, and a simple log 2 transformation, on both simulated and biological datasets of gene expression. Our evaluation included both supervised and unsupervised machine learning approaches. We found that TDM exhibited consistently strong performance across settings and that quantile normalization also performed well in many circumstances. We also provide a TDM package for the R programming language