2,323 research outputs found
Optimum structures of digital controllers in sampled-data systems: a roundoff noise analysis
Full text linkIn this paper, the effect of roundoff noise in a digital controller is analyzed for a sampled-data system in which the digital controller is implemented in a state-space realization. A new measure, called averaged roundoff noise gain, is derived. Unlike the traditionally used measure, where the analysis is done based on an equivalent digital control system, this newly defined averaged roundoff noise gain allows us to take consideration of the inter-sample behavior. It is shown that this measure is a function of the state-space realization. Noting the fact that the state-space realizations of a digital controller are not unique, the problem of optimum controller structure is to identify those realizations that minimize the averaged roundoff noise gain subject to the -scaling constraint which is for preventing the signals in the controller from overflow. An analytical solution to the problem is presented and a design example is given. Both theoretical analysis and simulation results show that the optimum controller realizations obtained with the proposed approach are superior to those obtained with the traditional analysis based on a digital control system
DS_10.1177_0022034520906384 – Supplemental material for Topical Fluoride to Prevent Root Caries: Systematic Review with Network Meta-analysis
No full textSupplemental material, DS_10.1177_0022034520906384 for Topical Fluoride to Prevent Root Caries: Systematic Review with Network Meta-analysis by J. Zhang, D. Sardana, K.Y. Li, K.C.M. Leung and E.C.M. Lo in Journal of Dental Research</p
Structure and electrical properties of silica-based polyethylene nanocomposites
No full textThe topic of polymer nanocomposites remains an active area of research in the dielectrics community, due to the unique electrical properties that these materials could exhibit. To explain the behaviour of these materials, the importance of clarifying the interfaces between nanoparticles and polymer matrices has been emphasised. However, understanding of the interface in nanocomposites is unsatisfactory and, consequently,many experimental results remain unexplained. This thesis reports on an investigation into a polyethylene nanocomposite system that contains varying amounts of nanosilica that differ with respect to their surface chemistry. The addition of nanosilica, even with different surface chemistries, was found to enhance the nucleation density of polyethylene and perturb the spherulitic development. While less organised lamellar structures would be expected to lead to a lower breakdown strength, this does not appear to be the case for the material systems considered here under alternating current (AC) fields. In addition, nanosilica filled polyethylene was found to absorb significantly more water than unfilled polyethylene, with the consequence that both the permittivity and the loss tangent increase with increasing duration of water immersion. However, appropriate surface treatment of nanosilica reduces the water absorption effect and modifies the dielectric response of the nanocomposites compared with those containing an equivalent amount of untreated nanosilica. Although water absorption may not be a technologically desirable characteristic, the results indicate that water molecules can act as effective dielectric probes of interfacial factors. Meanwhile, the direct current (DC) breakdown strength reduces with the inclusion of increasing amount of nanosilica in the polyethylene, but surface treatment of nanosilica improves the DC breakdown strength with respect to equivalent nanocomposites containing untreated nanosilica. Results from space charge studies reveal increased space charge accumulation in the presence of the untreated nanosilica and, upon surface treatment of the nanosilica, the charge development was suppressed in comparison with nanocomposites containing an equivalent amount of untreated nanosilica. This observation suggests that space charge accumulation and DC failure are related in these systems and it would seem that control of surface chemistry is particularly critical in connection with the use of nanocomposites in DC applications. Finally, the mechanisms underpinning the concept of filler functionalisation in nanocomposites were investigated via the use of different aliphatic chain length silane coupling agents, and the results show that long silane chains enhance the DC breakdown strength of the resulting nanocomposites. The possible further enhancement in DC breakdown strength is also highlighted. Overall, this thesis demonstrates how a nanoparticle’s interface chemistry can affect both the structure and the electrical properties of the resulting nanocomposites, and serves as an important foundation towards the engineering of nanocomposites as the reliable electricalinsulation materials of the future, through the understanding of the interface
A Multi-scale Finite Element Model of Tsing Ma Bridge for Hot Spot Stress Analysis
No full textFailure due to fatigue damage is an important failure mode for large suspension bridges and welded connections are usually identified as the most vulnerable locations for accumulative fatigue damage of existing bridges. The hot-spot stress approach has been successfully applied in fatigue evaluation of the welded joints. Traditionally a structural analysis using a global FE model is first conducted to determine the critical locations, based on which a local analysis is then carried out to obtain the hot-spot stress distribution that is the basis of fatigue status assessment process. Alternatively, a multi-scale model is proposed by using the mixed dimensional coupling method merging typical detailed joint geometry models into the global model so that the hot-spot stress can be directly output through a single step of analysis. As a case study, a multi-scale model of Tsing Ma Bridge was developed accordingly and the calculated results were compared with those of the global structural model and the structural health monitoring data with respect to first few order natural frequencies and vertical displacement at GPS level sensor-installed locations, and hot spot stress situation combined with stress concentration factors at a typical intersection joint. The comparison results show that the multi-scale model output agrees well with those of global model and monitoring data within the acceptable range, indicating that, at the same engineering level, the developed multi-scale model is more convenient and yet appropriate for the purpose of hot spot stress analysis in fatigue evaluation
EXPERIMENTAL STUDY ON THE BREAKUP MECHANISM OF A THIN LIQUID SHEET FROM AN AIR-ASSISTED PLANAR NOZZLE
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Analysis of the impact of climate-driven extreme weather events (EWEs) on the UK train delays: A data-driven BN approach
Full text linkClimate change exacerbates the occurrence of frequent Extreme Weather Events (EWEs), directly disrupting railway operations in numerous countries, notably the United Kingdom. Projections for the UK climate indicate an increase in rainfall intensity, warmer and wetter winters, hotter and drier summers, and more frequent and intense EWEs. Such climatic shifts cause increased weather-related railway delays, which in turn result in significant economic loss. This study develops a new risk model using a data-driven Bayesian Network (BN) to analyse the impact of climate-induced EWEs on UK train delays. The model quantifies the influence of various factors on delays, providing deeper insights into their individual and combined effects. The new model and the f indings contribute to the disclosure of 1) the interconnections among the different variables influencing train delays, including the origin and destination of the train and traction type, and 2) the prediction of the quantitative extent to which the variables can jointly lead to train delays of different severity levels, incident reason, the month of occurrence, the responsible operator, and the train schedule type. Critical findings highlight the substantial negative impact of severe flooding on the operational reliability of the UK railway system. An important insight was the significant clustering of delays ranging from 80 to 90 min, particularly on Fridays, suggesting the need for targeted operational interventions in specific regions. Additionally, the analysis identified December as the most hazardous month for train delays due to EWEs, with January and July also showing elevated risk levels. This paper offers valuable insights for transport planners, enabling them to prioritise climate-related scenarios causing the most severe train delays and to formulate the associated adaptation measures and strategies rationally
Operation of the boreal peatland methane cycle across the past 16 k.y.
Full text linkThe role of boreal wetlands in driving variations in atmospheric methane (CH4) concentrations across the last deglaciation (20-10 ka) and the Holocene is debated. Most studies infer the sources of atmospheric methane via ice-core records of methane concentration and its light stable isotopic composition. However, direct evidence for variations in the methane cycle from the wetlands themselves is relatively limited. Here, we used a suite of biomarker proxies to reconstruct the methane cycle in the Chinese Hani peat across the past 16 k.y. We found two periods of enhanced methanogenesis, at ca. 15-11 ka and ca. 10-6 ka, whereas weak methanogenesis characterized the late Holocene. These periods of enhanced methanogenesis relate to periods of high/increasing temperatures, supporting a temperature control on the wetland methane cycle. We found no biomarker evidence for intense methanotrophy throughout the past 16 k.y., and, contrary to previous studies, we found no clear control of hydrology on the peatland methane cycle. Although the onset of methanogenesis at Hani at ca. 15 ka coincided with a negative shift in methane δ13C in the ice cores, there is no consistent correlation between changes in the reconstructed methane cycle of the boreal Hani peat and atmospheric CH4 concentrations.</p
A Feasibility Study on Damage Detection of three Cable-Supported Bridges in Hong Kong using Vibration Measurement
No full textA feasibility study on vibration-based damage detection methods for the three cable supported bridges in Hong Kong is carried out. Emphasis is placed on how to deal with the noise corrupted/uncertain measurement data and how to use the series data from the on-line monitoring system for damage detection. Numerical simulation studies of using the noisy series measurement modal data for damage occurrence detection in terms of the auto-associative neural network arc presented. Neural network based novelty detectors using only natural frequencies of the intact and damaged structure are developed for the detection of damage occurrence in the three bridges. The noisy/uncertain measurement data are produced by polluting the analytical natural frequencies with random noise. Numerical simulations of a series of damage scenarios show that when the maximum frequency change caused by damage exceeds a cel1ain threshold, the occurrence of damage can be unambiguously flagged with the novelty detectors
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