104 resultados para time-dependent local density approximation


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Lamb waves propagation in composite materials has been studied extensively since it was first observed in 1982. In this paper, we show a procedure to simulate the propagation of Lamb waves in composite laminates using a two-dimensional model in ANSYS. This is done by simulating the Lamb waves propagating along the plane of the structure in the form of a time dependent force excitation. In this paper, an 8-layered carbon reinforced fibre plastic (CRFP) is modelled as transversely isotropic and dissipative medium and the effect of flaws is analyzed with respect to the defects induced between various layers of the composite laminate. This effort is the basis for the future development of a 3D model for similar applications.

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Is there timing ability in the exchange rate markets? We address this question by examining foreign firms' decisions to issue American Depositary Receipts (ADRs). Specifically, we test whether foreign firms consider currency market conditions in their ADR issuance decisions and, in doing so, display some ability to time their local exchange rate market. We study ADR issuances in the U.S. stock market between 1976 and 2003. We find that foreign firms tend to issue ADRs after their local currency has been abnormally strong against the U.S. dollar and before their local currency becomes abnormally weak. This evidence is statistically significant even after controlling for local and U.S. past and future stock market performance and predicable exchange rate movements. Currency market timing is especially significant i) for value companies, relatively small (yet absolutely large) companies issuing relatively large amounts of ADRs, companies with higher currency exposure, manufacturing companies, and emerging market companies, ii) during currency crises (when mispricings are rife) and after the integration of the issuer's local financial market with the world capital markets, iii) when the ADR issue raises capital for the issuing firm (Level III ADR), and iv) regardless of the identity of the underwriting investment bank. Currency market timing is also economically significant since it translates into total savings for the issuing firms of about $646 million (or 1.86% of the total capital-raising ADR issue volume). In contrast, we find no evidence of currency timing ability in a control sample made of non-capital raising ADRs (Level II ADRs). These findings suggest that some companies may have, at least occasionally, private information about foreign exchange.

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Differential axial shortening in vertical members of reinforced concrete high-rise buildings occurs due to shrinkage, creep and elastic shortening, which are time dependent effects of concrete. This has to be quantified in order to make adequate provisions and mitigate its adverse effects. This paper presents a novel procedure for quantifying the axial shortening of vertical members using the variations in vibration characteristics of the structure, in lieu of using gauges which can pose problems in use during and after the construction. This procedure is based on the changes in the modal flexiblity matrix which is expressed as a function of the mode shapes and the reciprocal of the natural frequencies. This paper will present the development of this novel procedure.

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Controlling differential axial shortening in vertical load bearing concrete elements is a major concern for new generation tall buildings with complex geometries and mechanisms. Quantification of axial shortening using gauges to verify the pre-estimated numerical values used at the design stage is a well established method. This method makes adequate provision to mitigate the adverse effects during the construction. However, this method is becoming increasingly unusable due to its drawbacks. This highlights the need a novel method to quantify the axial shortening using ambient measurements. This paper will first brief introduce the method and then illustrate its application to a high-rise building with two outrigger and belt systems. Moreover, this procedure can be used as a health or performance monitoring tool of the building structure, both during and after construction.

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Axial shortening in vertical load bearing elements of reinforced concrete high-rise buildings is caused by the time dependent effects of shrinkage, creep and elastic shortening of concrete under loads. Such phenomenon has to be predicted at design stage and then updated during and after construction of the buildings in order to provide mitigation against the adverse effects of differential axial shortening among the elements. Existing measuring methods for updating previous predictions of axial shortening pose problems. With this in mind, a innovative procedure with a vibration based parameter called axial shortening index is proposed to update axial shortening of vertical elements based on variations in vibration characteristics of the buildings. This paper presents the development of the procedure and illustrates it through a numerical example of an unsymmetrical high-rise building with two outrigger and belt systems. Results indicate that the method has the capability to capture influence of different tributary areas, shear walls of outrigger and belt systems as well as the geometric complexity of the building.

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Differential distortion comprising axial shortening and consequent rotation in concrete buildings is caused by the time dependent effects of “shrinkage”, “creep” and “elastic” deformation. Reinforcement content, variable concrete modulus, volume to surface area ratio of elements and environmental conditions influence these distortions and their detrimental effects escalate with increasing height and geometric complexity of structure and non vertical load paths. Differential distortion has a significant impact on building envelopes, building services, secondary systems and the life time serviceability and performance of a building. Existing methods for quantifying these effects are unable to capture the complexity of such time dependent effects. This paper develops a numerical procedure that can accurately quantify the differential axial shortening that contributes significantly to total distortion in concrete buildings by taking into consideration (i) construction sequence and (ii) time varying values of Young’s Modulus of reinforced concrete and creep and shrinkage. Finite element techniques are used with time history analysis to simulate the response to staged construction. This procedure is discussed herein and illustrated through an example.

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The numerical modelling of electromagnetic waves has been the focus of many research areas in the past. Some specific applications of electromagnetic wave scattering are in the fields of Microwave Heating and Radar Communication Systems. The equations that govern the fundamental behaviour of electromagnetic wave propagation in waveguides and cavities are the Maxwell's equations. In the literature, a number of methods have been employed to solve these equations. Of these methods, the classical Finite-Difference Time-Domain scheme, which uses a staggered time and space discretisation, is the most well known and widely used. However, it is complicated to implement this method on an irregular computational domain using an unstructured mesh. In this work, a coupled method is introduced for the solution of Maxwell's equations. It is proposed that the free-space component of the solution is computed in the time domain, whilst the load is resolved using the frequency dependent electric field Helmholtz equation. This methodology results in a timefrequency domain hybrid scheme. For the Helmholtz equation, boundary conditions are generated from the time dependent free-space solutions. The boundary information is mapped into the frequency domain using the Discrete Fourier Transform. The solution for the electric field components is obtained by solving a sparse-complex system of linear equations. The hybrid method has been tested for both waveguide and cavity configurations. Numerical tests performed on waveguides and cavities for inhomogeneous lossy materials highlight the accuracy and computational efficiency of the newly proposed hybrid computational electromagnetic strategy.

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While recent research has provided valuable information as to the composition of laser printer particles, their formation mechanisms, and explained why some printers are emitters whilst others are low emitters, fundamental questions relating to the potential exposure of office workers remained unanswered. In particular, (i) what impact does the operation of laser printers have on the background particle number concentration (PNC) of an office environment over the duration of a typical working day?; (ii) what is the airborne particle exposure to office workers in the vicinity of laser printers; (iii) what influence does the office ventilation have upon the transport and concentration of particles?; (iv) is there a need to control the generation of, and/or transport of particles arising from the operation of laser printers within an office environment?; (v) what instrumentation and methodology is relevant for characterising such particles within an office location? We present experimental evidence on printer temporal and spatial PNC during the operation of 107 laser printers within open plan offices of five buildings. We show for the first time that the eight-hour time-weighted average printer particle exposure is significantly less than the eight-hour time-weighted local background particle exposure, but that peak printer particle exposure can be greater than two orders of magnitude higher than local background particle exposure. The particle size range is predominantly ultrafine (< 100nm diameter). In addition we have established that office workers are constantly exposed to non-printer derived particle concentrations, with up to an order of magnitude difference in such exposure amongst offices, and propose that such exposure be controlled along with exposure to printer derived particles. We also propose, for the first time, that peak particle reference values be calculated for each office area analogous to the criteria used in Australia and elsewhere for evaluating exposure excursion above occupational hazardous chemical exposure standards. A universal peak particle reference value of 2.0 x 104 particles cm-3 has been proposed.

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Discrete stochastic simulations, via techniques such as the Stochastic Simulation Algorithm (SSA) are a powerful tool for understanding the dynamics of chemical kinetics when there are low numbers of certain molecular species. However, an important constraint is the assumption of well-mixedness and homogeneity. In this paper, we show how to use Monte Carlo simulations to estimate an anomalous diffusion parameter that encapsulates the crowdedness of the spatial environment. We then use this parameter to replace the rate constants of bimolecular reactions by a time-dependent power law to produce an SSA valid in cases where anomalous diffusion occurs or the system is not well-mixed (ASSA). Simulations then show that ASSA can successfully predict the temporal dynamics of chemical kinetics in a spatially constrained environment.

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Lately, there has been increasing interest in the association between temperature and adverse birth outcomes including preterm birth (PTB) and stillbirth. PTB is a major predictor of many diseases later in life, and stillbirth is a devastating event for parents and families. The aim of this study was to assess the seasonal pattern of adverse birth outcomes, and to examine possible associations of maternal exposure to temperature with PTB and stillbirth. We also aimed to identify if there were any periods of the pregnancy where exposure to temperature was particularly harmful. A retrospective cohort study design was used and we retrieved individual birth records from the Queensland Health Perinatal Data Collection Unit for all singleton births (excluding twins and triplets) delivered in Brisbane between 1 July 2005 and 30 June 2009. We obtained weather data (including hourly relative humidity, minimum and maximum temperature) and air-pollution data (including PM10, SO2 and O3) from the Queensland Department of Environment and Resource Management. We used survival analyses with the time-dependent variables of temperature, humidity and air pollution, and the competing risks of stillbirth and live birth. To assess the monthly pattern of the birth outcomes, we fitted month of pregnancy as a time-dependent variable. We examined the seasonal pattern of the birth outcomes and the relationship between exposure to high or low temperatures and birth outcomes over the four lag weeks before birth. We further stratified by categorisation of PTB: extreme PTB (< 28 weeks of gestation), PTB (28–36 weeks of gestation), and term birth (≥ 37 weeks of gestation). Lastly, we examined the effect of temperature variation in each week of the pregnancy on birth outcomes. There was a bimodal seasonal pattern in gestation length. After adjusting for temperature, the seasonal pattern changed from bimodal, to only one peak in winter. The risk of stillbirth was statistically significant lower in March compared with January. After adjusting for temperature, the March trough was still statistically significant and there was a peak in risk (not statistically significant) in winter. There was an acute effect of temperature on gestational age and stillbirth with a shortened gestation for increasing temperature from 15 °C to 25 °C over the last four weeks before birth. For stillbirth, we found an increasing risk with increasing temperatures from 12 °C to approximately 20 °C, and no change in risk at temperatures above 20 °C. Certain periods of the pregnancy were more vulnerable to temperature variation. The risk of PTB (28–36 weeks of gestation) increased as temperatures increased above 21 °C. For stillbirth, the fetus was most vulnerable at less than 28 weeks of gestation, but there were also effects in 28–36 weeks of gestation. For fetuses of more than 37 weeks of gestation, increasing temperatures did not increase the risk of stillbirth. We did not find any adverse affects of cold temperature on birth outcomes in this cohort. My findings contribute to knowledge of the relationship between temperature and birth outcomes. In the context of climate change, this is particularly important. The results may have implications for public health policy and planning, as they indicate that pregnant women would decrease their risk of adverse birth outcomes by avoiding exposure to high temperatures and seeking cool environments during hot days.

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Concrete is commonly used as a primary construction material for tall building construction. Load bearing components such as columns and walls in concrete buildings are subjected to instantaneous and long term axial shortening caused by the time dependent effects of "shrinkage", "creep" and "elastic" deformations. Reinforcing steel content, variable concrete modulus, volume to surface area ratio of the elements and environmental conditions govern axial shortening. The impact of differential axial shortening among columns and core shear walls escalate with increasing building height. Differential axial shortening of gravity loaded elements in geometrically complex and irregular buildings result in permanent distortion and deflection of the structural frame which have a significant impact on building envelopes, building services, secondary systems and the life time serviceability and performance of a building. Existing numerical methods commonly used in design to quantify axial shortening are mainly based on elastic analytical techniques and therefore unable to capture the complexity of non-linear time dependent effect. Ambient measurements of axial shortening using vibrating wire, external mechanical strain, and electronic strain gauges are methods that are available to verify pre-estimated values from the design stage. Installing these gauges permanently embedded in or on the surface of concrete components for continuous measurements during and after construction with adequate protection is uneconomical, inconvenient and unreliable. Therefore such methods are rarely if ever used in actual practice of building construction. This research project has developed a rigorous numerical procedure that encompasses linear and non-linear time dependent phenomena for prediction of axial shortening of reinforced concrete structural components at design stage. This procedure takes into consideration (i) construction sequence, (ii) time varying values of Young's Modulus of reinforced concrete and (iii) creep and shrinkage models that account for variability resulting from environmental effects. The capabilities of the procedure are illustrated through examples. In order to update previous predictions of axial shortening during the construction and service stages of the building, this research has also developed a vibration based procedure using ambient measurements. This procedure takes into consideration the changes in vibration characteristic of structure during and after construction. The application of this procedure is illustrated through numerical examples which also highlight the features. The vibration based procedure can also be used as a tool to assess structural health/performance of key structural components in the building during construction and service life.

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Background Many previous studies have found seasonal patterns in birth outcomes, but with little agreement about which season poses the highest risk. Some of the heterogeneity between studies may be explained by a previously unknown bias. The bias occurs in retrospective cohorts which include all births occurring within a fixed start and end date, which means shorter pregnancies are missed at the start of the study, and longer pregnancies are missed at the end. Our objective was to show the potential size of this bias and how to avoid it. Methods To demonstrate the bias we simulated a retrospective birth cohort with no seasonal pattern in gestation and used a range of cohort end dates. As a real example, we used a cohort of 114,063 singleton births in Brisbane between 1 July 2005 and 30 June 2009 and examined the bias when estimating changes in gestation length associated with season (using month of conception) and a seasonal exposure (temperature). We used survival analyses with temperature as a time-dependent variable. Results We found strong artificial seasonal patterns in gestation length by month of conception, which depended on the end date of the study. The bias was avoided when the day and month of the start date was just before the day and month of the end date (regardless of year), so that the longer gestations at the start of the study were balanced by the shorter gestations at the end. After removing the fixed cohort bias there was a noticeable change in the effect of temperature on gestation length. The adjusted hazard ratios were flatter at the extremes of temperature but steeper between 15 and 25°C. Conclusions Studies using retrospective birth cohorts should account for the fixed cohort bias by removing selected births to get unbiased estimates of seasonal health effects.

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Complex flow datasets are often difficult to represent in detail using traditional vector visualisation techniques such as arrow plots and streamlines. This is particularly true when the flow regime changes in time. Texture-based techniques, which are based on the advection of dense textures, are novel techniques for visualising such flows (i.e., complex dynamics and time-dependent). In this paper, we review two popular texture-based techniques and their application to flow datasets sourced from real research projects. The texture-based techniques investigated were Line Integral Convolution (LIC), and Image-Based Flow Visualisation (IBFV). We evaluated these techniques and in this paper report on their visualisation effectiveness (when compared with traditional techniques), their ease of implementation, and their computational overhead.

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The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model. The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model. This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived. EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model. A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.

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CC-chemokine receptor 2 (CCR2) and its ligand, monocyte chemotactic protein-1 (MCP-1, also known as CCL2), are crucial for the recruitment of monocytes/macrophages to sites of inflammation. We conducted a series of experiments to investigate the relationship between stress, monocyte CCR2 expression and migration activity. First, we collected peripheral blood mononuclear cells (PBMC) from untrained subjects (n=8) and measured CCR2 expression on CD14(+) monocytes cultured with cortisol, epinephrine and norepinephrine. Second, we collected PBMC from the subjects before and after they cycled for 60 min at 70% peak O(2) uptake (VO2(peak)), and measured alterations in CCR2 expression on monocytes following exercise. Third, we cultured PBMC with serum obtained before and after exercise and the glucocorticoid antagonist RU-486 to determine the effect of cortisol on CCR2 expression in vitro. Last, we measured the ability of PBMC treated with serum or cortisol to migrate through membrane filters in response to CCL2. Cortisol (but not epinephrine or norepinephrine) increased CCR2 expression on monocytes in a dose- and time-dependent manner. Exercise did not influence CCR2 expression on PBMC, whereas incubation of PBMC with post-exercise serum significantly increased CCR2 expression. Both cortisol and post-exercise serum increased the migration of PBMC toward CCL2. The increase in CCR2 expression on PBMC following stimulation with cortisol and serum was blocked by the glucocorticoid receptor antagonist RU-486. In conclusion, cortisol released during exercise increased monocyte CCR2 expression and migration activity in vitro. These alterations may influence inflammation and regeneration of damaged tissue after acute stress.