831 resultados para 0913 Mechanical Engineering


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Hydrogels provide a 3-dimensional network for embedded cells and offer promise for cartilage tissue engineering applications. Nature-derived hydrogels, including alginate, have been shown to enhance the chondrocyte phenotype but are variable and not entirely controllable. Synthetic hydrogels, including polyethylene glycol (PEG)-based matrices, have the advantage of repeatability and modularity; mechanical stiffness, cell adhesion, and degradability can be altered independently. In this study, we compared the long-term in vitro effects of different hydrogels (alginate and Factor XIIIa-cross-linked MMP-sensitive PEG at two stiffness levels) on the behavior of expanded human chondrocytes and the development of construct properties. Monolayer-expanded human chondrocytes remained viable throughout culture, but morphology varied greatly in different hydrogels. Chondrocytes were characteristically round in alginate but mostly spread in PEG gels at both concentrations. Chondrogenic gene (COL2A1, aggrecan) expression increased in all hydrogels, but alginate constructs had much higher expression levels of these genes (up to 90-fold for COL2A1), as well as proteoglycan 4, a functional marker of the superficial zone. Also, chondrocytes expressed COL1A1 and COL10A1, indicative of de-differentiation and hypertrophy. After 12 weeks, constructs with lower polymer content were stiffer than similar constructs with higher polymer content, with the highest compressive modulus measured in 2.5% PEG gels. Different materials and polymer concentrations have markedly different potency to affect chondrocyte behavior. While synthetic hydrogels offer many advantages over natural materials such as alginate, they must be further optimized to elicit desired chondrocyte responses for use as cartilage models and for development of functional tissue-engineered articular cartilage.

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This paper discusses diesel engine condition monitoring (CM) using acoustic emissions (AE)as well as some of the commonly encountered diesel engine problems. Also discussed are some of the underlying combustion related faults and the methods used in past studies to simulate diesel engine faults. The initial test involved an experimental simulation of two common combustion related diesel engine faults, namely diesel knock and misfire. These simulated faults represent the first step towards a comprehensive investigation and analysis into the characteristics of acoustic emission signals arising from combustion related diesel engine faults. Data corresponding to different engine running conditions was captured using in-cylinder pressure, vibration and acoustic emission transducers along with both crank angle encoder and top-dead centre (TDC) signals. Using these signals, it was possible to characterise the effect of different combustion conditions and hence, various diesel engine in-cylinder pressure profiles.

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This paper presents an overview of the CRC for Infrastructure and Engineering Asset Management (CIEAM)’s rotating machine health monitoring project and the status of the research progress. The project focuses on the development of a comprehensive diagnostic tool for condition monitoring and systematic analysis of rotating machinery. Particularly attention focuses on the machine health monitoring of diesel engines, compressors and pumps by using acoustic emission and vibration-based monitoring techniques. The paper also provides a brief summary of the work done by the three main research collaborating partners in the project, namely, Queensland University of Technology (QUT), Curtin University of Technology (CUT) and the University of Western Australia (UWA). Preliminary test and analysis results from this work are also reported in the paper

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Over recent years a significant amount of research has been undertaken to develop prognostic models that can be used to predict the remaining useful life of engineering assets. Implementations by industry have only had limited success. By design, models are subject to specific assumptions and approximations, some of which are mathematical, while others relate to practical implementation issues such as the amount of data required to validate and verify a proposed model. Therefore, appropriate model selection for successful practical implementation requires not only a mathematical understanding of each model type, but also an appreciation of how a particular business intends to utilise a model and its outputs. This paper discusses business issues that need to be considered when selecting an appropriate modelling approach for trial. It also presents classification tables and process flow diagrams to assist industry and research personnel select appropriate prognostic models for predicting the remaining useful life of engineering assets within their specific business environment. The paper then explores the strengths and weaknesses of the main prognostics model classes to establish what makes them better suited to certain applications than to others and summarises how each have been applied to engineering prognostics. Consequently, this paper should provide a starting point for young researchers first considering options for remaining useful life prediction. The models described in this paper are Knowledge-based (expert and fuzzy), Life expectancy (stochastic and statistical), Artificial Neural Networks, and Physical models.

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Asset health inspections can produce two types of indicators: (1) direct indicators (e.g. the thickness of a brake pad, and the crack depth on a gear) which directly relate to a failure mechanism; and (2) indirect indicators (e.g. the indicators extracted from vibration signals and oil analysis data) which can only partially reveal a failure mechanism. While direct indicators enable more precise references to asset health condition, they are often more difficult to obtain than indirect indicators. The state space model provides an efficient approach to estimating direct indicators by using indirect indicators. However, existing state space models to estimate direct indicators largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires fixed inspection intervals. The discrete state assumption entails discretising continuous degradation indicators, which often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This paper proposes a state space model without these assumptions. Monte Carlo-based algorithms are developed to estimate the model parameters and the remaining useful life. These algorithms are evaluated for performance using numerical simulations through MATLAB. The result shows that both the parameters and the remaining useful life are estimated accurately. Finally, the new state space model is used to process vibration and crack depth data from an accelerated test of a gearbox. During this application, the new state space model shows a better fitness result than the state space model with linear and Gaussian assumption.

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Acoustic emission (AE) is the phenomenon where high frequency stress waves are generated by rapid release of energy within a material by sources such as crack initiation or growth. AE technique involves recording these stress waves by means of sensors placed on the surface and subsequent analysis of the recorded signals to gather information such as the nature and location of the source. AE is one of the several non-destructive testing (NDT) techniques currently used for structural health monitoring (SHM) of civil, mechanical and aerospace structures. Some of its advantages include ability to provide continuous in-situ monitoring and high sensitivity to crack activity. Despite these advantages, several challenges still exist in successful application of AE monitoring. Accurate localization of AE sources, discrimination between genuine AE sources and spurious noise sources and damage quantification for severity assessment are some of the important issues in AE testing and will be discussed in this paper. Various data analysis and processing approaches will be applied to manage those issues.

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A Computational fluid dynamics (CFD) approach is used to model fluid flow in a journal bearing with three equi-spaced axial grooves and supplied with water from one end. Water is subjected to both velocity (Couette) & pressure induced (Poiseuille) flow. The working fluid passing through the bearing clearance generates driving force components that may increase the unstable vibration of the rotor. It is important to know the accurate rotor dynamic force component for predicting the instability of rotor bearing systems. In this paper a study has been made to obtain the stiffness and damping coefficients of 3 axial groove bearing using Perturbation technique.

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When crest-fixed thin trapezoidal steel cladding with closely spaced ribs is subjected to wind uplift/suction forces, local dimpling or pull-through failures occur prematurely at their screw connections because of the large stress concentrations in the cladding under the screw heads. Currently, the design of crest-fixed profiled steel cladding is mainly based on time consuming and expensive laboratory tests due to the lack of adequate design rules. In this research, a shell finite element model of crest-fixed trapezoidal steel cladding with closely spaced ribs was developed and validated using experimental results. The finite element model included a recently developed splitting criterion and other advanced features including geometric imperfections, buckling effects, contact modelling and hyperelastic behaviour of neoprene washers, and was used in a detailed parametric study to develop suitable design formulae for local failures. This paper presents the details of the finite element analyses, large scale experiments and their results including the new wind uplift design strength formulae for trapezoidal steel cladding with closely spaced ribs. The new design formulae can be used to achieve both safe and optimised solutions.

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The LiteSteel beam (LSB) is a new hollow flange channel section developed by OneSteel Australian Tube Mills using their patented dual electric resistance welding and automated continuous roll-forming process. It has a unique geometry consisting of torsionally rigid rectangular hollow flanges and a relatively slender web. The LSBs are commonly used as flexural members in buildings. However, the LSB flexural members are subjected to lateral distortional buckling, which reduces their member moment capacities. Unlike the commonly observed lateral torsional buckling of steel beams, the lateral distortional buckling of LSBs is characterised by simultaneous lateral deflection, twist, and cross sectional change due to web distortion. An experimental study including more than 50 lateral buckling tests was therefore conducted to investigate the behaviour and strength of LSB flexural members. It included the available 13 LSB sections with spans ranging from 1200 to 4000 mm. Lateral buckling tests based on a quarter point loading were conducted using a special test rig designed to simulate the required simply supported and loading conditions accurately. Experimental moment capacities were compared with the predictions from the design rules in the Australian cold-formed steel structures standard. The new design rules in the standard were able to predict the moment capacities more accurately than previous design rules. This paper presents the details of lateral distortional buckling tests, in particular the features of the lateral buckling test rig, the results and the comparisons. It also includes the results of detailed studies into the mechanical properties and residual stresses of LSBs.