Mixtures of shredded tyres with sand have become accepted geo-materials in the past decade, especially for use as fill materials. Owing to the large size of tyre shreds commonly used in practice, large-scale direct shear testing equipment is specially fabricated to obtain the shear strength parameters of mixtures. The effect of tyre shred-to-sand mixing ratio on the mechanical response of the mixtures is studied by performing direct shear tests on mixtures prepared at different mixing ratios. Out of the four types of samples tested, three (with mixing ratios of 20/80, 25/75 and 35/65 by weight of tyre shreds to sand) are prepared using 50 to 100 mm long tyre shreds and one (25/75 by weight of tyre chips to sand) using tyre chips with nominal size of 9·5 mm. The end-of-test friction angle of all the tyre shred–sand mixtures considered in this research is in the 30–33° range. Results of direct shear testing from the present study and other studies in the literature show that the shear strength of tyre shred–sand mixtures is greater than that of tyre chip–sand mixtures because the elongated tyre shred pieces are able to provide greater reinforcement effects than are possible with equidimensional (cubical) tyre chips.

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<p>Low probability events occurring in sequence, within a limited operational time (damage and recovery window between events), are a key consideration in multi-hazard safety assessments of nuclear power plants (NPPs). Cascading effects from hazards and associated event sequences could potentially have a significant impact on risk estimates. The Bayesian network can act as a framework to consider aforementioned statistical dependencies between various hazards in multi-risk analyses of nuclear power plants.</p><p>Within the EU project NARSIS (New Approach to Reactor Safety Improvements), a Bayesian network-based risk assessment framework was developed to perform multi-hazard risk assessment of NPPs.</p><p>The Bayesian network method was applied for an external-event related station blackout (SBO) scenario at a NPP. Earthquake, flooding, and tornado were among the hazards considered at a decommissioned NPP site location in Europe. Both hazard dependency in time as well as a cascading scenario was also considered. The hazards, their interactions and the fragilities of selected systems, structures and components within the nuclear power plant were represented in the network and their probability distributions were obtained based on the multi-hazard and fragility approaches adopted within the NARSIS project.</p><p>Sensitivity analyses in the network were used to identify key hazards and interactions. Most influential hazard combinations and ranges of intensity measures were identified through diagnostic inference in the network. Discretisation of continuous variables (hazard curves in this case) is a key aspect of performing inference in Bayesian networks. The effect of various levels of discretisation of hazard probability distributions was assessed, to identify suitable discretisations of hazard data.</p><p>This application demonstrates the use and advantages of the Bayesian network methodology, developed in the NARSIS project, for probabilistic safety assessments of NPPs.</p>

Abstract:The aim of research work is to analyze residual stresses generated during shaper machining. This paper presents the simulation of single point cutting process by finite element method and compared with experimental work results. Taguchi L9 experimental design is used to obtain effective process parameters of rake angle, depth of cut and cutting speed. High speed cutting tool steel is used for orthogonal cutting of AISI 1020 steel on shaper machine. The residual stresses are measured by X-ray sine 2 ѱ diffraction method and finite element software used to simulate residual stress analysis. In Taguchi designed experimentation rake angle 12 0 , depth of cut 1mm, and cutting speed 220m/min has been found optimum cutting parameter for optimize compressive residual stresses.

<p>The methodology for Probabilistic Safety Assessment (PSA) of Nuclear Power Plants (NPPs) has been used for decades by practitioners to better understand the most probable initiators of nuclear accidents by identifying potential accident scenarios, their consequences, and their probabilities. However, despite the remarkable reliability of the methodology, the Fukushima Dai-ichi nuclear accident in Japan, which occurred in March 2011, highlighted a number of challenging issues (e.g. cascading event - cliff edge - scenarios) with respect to the application of PSA questioning the relevance of PSA practice, for such low-probability but high-consequences external events. Following the Fukushima Dai-ichi accident, several initiatives at the international level, have been launched in order to review current practices and identify shortcomings in scientific and technical approaches for the characterization of external natural extreme events and the evaluation of their consequences on the safety of nuclear facilities.</p><p>The H2020 project “New Approach to Reactor Safety ImprovementS” (NARSIS, 2017-2021) aims at proposing some improvements to be integrated in existing PSA procedures for NPPs, considering single, cascade and combined external natural hazards (earthquakes, flooding, extreme weather, tsunamis). It coordinates the research efforts of eighteen partners encompassing leading universities, research institutes, technical support organizations (TSO), nuclear power producers and suppliers, reactor designers and operators from ten countries.</p><p>The project will lead to the release of various tools together with recommendations and guidelines for use in nuclear safety assessment, including a Bayesian-based multi-risk framework able to account for causes and consequences of technical, social/organizational and human aspects and as well as a supporting Severe Accident Management decision-making tool for demonstration purposes.</p><p>The NARSIS project has now been running for two years and a half, and the first set of deliverables and tools have been produced as part of the effort of the consortium. Datasets have been collected, methodologies tested, states of the art have been produced, and various criteria and plans developed. First results have started to emerge and will be presented here.</p>