Mechanism based emulation of dynamic simulation models : concept and application in hydrology

Many model-based investigation techniques, such as sensitivity analysis, optimization, and statistical inference, require a large number of model evaluations to be performed at different input and/or parameter values. This limits the application of these techniques to models that can be implemented in computationally efficient computer codes. Emulators, by providing efficient interpolation between outputs of deterministic simulation models, can considerably extend the field of applicability of such computationally demanding techniques. So far, the dominant techniques for developing emulators have been priors in the form of Gaussian stochastic processes (GASP) that were conditioned with a design data set of inputs and corresponding model outputs. In the context of dynamic models, this approach has two essential disadvantages: (i) these emulators do not consider our knowledge of the structure of the model, and (ii) they run into numerical difficulties if there are a large number of closely spaced input points as is often the case in the time dimension of dynamic models. To address both of these problems, a new concept of developing emulators for dynamic models is proposed. This concept is based on a prior that combines a simplified linear state space model of the temporal evolution of the dynamic model with Gaussian stochastic processes for the innovation terms as functions of model parameters and/or inputs. These innovation terms are intended to correct the error of the linear model at each output step. Conditioning this prior to the design data set is done by Kalman smoothing. This leads to an efficient emulator that, due to the consideration of our knowledge about dominant mechanisms built into the simulation model, can be expected to outperform purely statistical emulators at least in cases in which the design data set is small. The feasibility and potential difficulties of the proposed approach are demonstrated by the application to a simple hydrological model.

Traffic incident clearance time and arrival time prediction based on hazard models

Accurate prediction of incident duration is not only important information of Traffic Incident Management System, but also an ffective input for travel time prediction. In this paper, the hazard based prediction odels are developed for both incident clearance time and arrival time. The data are obtained from the Queensland Department of Transport and Main Roads’ STREAMS Incident Management System (SIMS) for one year ending in November 2010. The best fitting distributions are drawn for both clearance and arrival time for 3 types of incident: crash, stationary vehicle, and hazard. The results show that Gamma, Log-logistic, and Weibull are the best fit for crash, stationary vehicle, and hazard incident, respectively. The obvious impact factors are given for crash clearance time and arrival time. The quantitative influences for crash and hazard incident are presented for both clearance and arrival. The model accuracy is analyzed at the end.

Simulation-based fully Bayesian experimental design for mixed effects models

In this paper, we present fully Bayesian experimental designs for nonlinear mixed effects models, in which we develop simulation-based optimal design methods to search over both continuous and discrete design spaces. Although Bayesian inference has commonly been performed on nonlinear mixed effects models, there is a lack of research into performing Bayesian optimal design for nonlinear mixed effects models that require searches to be performed over several design variables. This is likely due to the fact that it is much more computationally intensive to perform optimal experimental design for nonlinear mixed effects models than it is to perform inference in the Bayesian framework. In this paper, the design problem is to determine the optimal number of subjects and samples per subject, as well as the (near) optimal urine sampling times for a population pharmacokinetic study in horses, so that the population pharmacokinetic parameters can be precisely estimated, subject to cost constraints. The optimal sampling strategies, in terms of the number of subjects and the number of samples per subject, were found to be substantially different between the examples considered in this work, which highlights the fact that the designs are rather problem-dependent and require optimisation using the methods presented in this paper.

Compressed sensing using hidden Markov models with application to vision based aircraft tracking

This paper investigates compressed sensing using hidden Markov models (HMMs) and hence provides an extension of recent single frame, bounded error sparse decoding problems into a class of sparse estimation problems containing both temporal evolution and stochastic aspects. This paper presents two optimal estimators for compressed HMMs. The impact of measurement compression on HMM filtering performance is experimentally examined in the context of an important image based aircraft target tracking application. Surprisingly, tracking of dim small-sized targets (as small as 5-10 pixels, with local detectability/SNR as low as − 1.05 dB) was only mildly impacted by compressed sensing down to 15% of original image size.

Self-organization and dynamics of nanoparticles in chemically active plasmas for low-temperature deposition of silicon and carbon-based nanostructured films

Self-organization and dynamic processes of nano/micron-sized solid particles grown in low-temperature chemically active plasmas as well as the associated physico-chemical processes are reviewed. Three specific reactive plasma chemistries, namely, of silane (SiH4), acetylene (C 2H2), and octafluorocyclobutane (c-C4F 8) RF plasma discharges for plasma enhanced chemical vapor deposition of amorphous hydrogenated silicon, hydrogenated and fluorinated carbon films, are considered. It is shown that the particle growth mechanisms and specific self-organization processes in the complex reactive plasma systems are related to the chemical organization and size of the nanoparticles. Correlation between the nanoparticle origin and self-organization in the ionized gas phase and improved thin film properties is reported. Self-organization and dynamic phenomena in relevant reactive plasma environments are studied for equivalent model systems comprising inert buffer gas and mono-dispersed organic particulate powders. Growth kinetics and dynamic properties of the plasma-assembled nanoparticles can be critical for the process quality in microelectronics as well as a number of other industrial applications including production of fine metal or ceramic powders, nanoparticle-unit thin film deposition, nanostructuring of substrates, nucleating agents in polymer and plastics synthesis, drug delivery systems, inorganic additives for sunscreens and UV-absorbers, and several others. Several unique properties of the chemically active plasma-nanoparticle systems are discussed as well.

ZnO layers for opto-electronic applications from solution-based and low-temperature processing of an organometallic precursor

As printed and flexible plastic electronic gadgets become increasingly viable today, there is a need to develop materials that suit the fabrication processes involved. Two desirable requirements are solution-processable active materials or precursors and low-temperature processability. In this article, we describe a straightforward method of depositing ZnO films by simple spin coating of an organometallic diethylzinc precursor solution and annealing the resulting film at low temperatures (≤200 °C) without involving any synthetic steps. By controlling the humidity in which annealing is conducted, we are able to adjust the intrinsic doping level and carrier concentration in diethylzinc-derived ZnO. Doped or conducting transport layers are greatly preferable to undoped layers as they enable low-resistance contacts and minimize the potential drops. This ability to controllably realize doped ZnO is a key feature of the fabrication process that we describe in this article. We employ field-effect measurements as a diagnostic tool to measure doping levels and mobilities in ZnO and demonstrate that doped ZnO with high charge carrier concentration is ideal for solar cell applications. Respectable power conversion efficiencies (up to 4.5%) are achieved in inverted solar cells that incorporate diethylzinc-derived ZnO films as the electron transport layer and organic blends as the active material. Extensions of this approach to grow ternary and quaternary films with organometallic precursor chemicals will enable solution based growth of a number of semiconductor films as well as a method to dope them.

Integrating the impact of climate into event based failure models for water pipes

This paper presents an event-based failure model to predict the number of failures that occur in water distribution assets. Often, such models have been based on analysis of historical failure data combined with pipe characteristics and environmental conditions. In this paper weather data have been added to the model to take into account the commonly observed seasonal variation of the failure rate. The theoretical basis of existing logistic regression models is briefly described in this paper, along with the refinements made to the model for inclusion of seasonal variation of weather. The performance of these refinements is tested using data from two Australian water authorities.

Bayesian hierarchical models for analysing spatial point-based data at a grid level: A comparison of approaches

Spatial data are now prevalent in a wide range of fields including environmental and health science. This has led to the development of a range of approaches for analysing patterns in these data. In this paper, we compare several Bayesian hierarchical models for analysing point-based data based on the discretization of the study region, resulting in grid-based spatial data. The approaches considered include two parametric models and a semiparametric model. We highlight the methodology and computation for each approach. Two simulation studies are undertaken to compare the performance of these models for various structures of simulated point-based data which resemble environmental data. A case study of a real dataset is also conducted to demonstrate a practical application of the modelling approaches. Goodness-of-fit statistics are computed to compare estimates of the intensity functions. The deviance information criterion is also considered as an alternative model evaluation criterion. The results suggest that the adaptive Gaussian Markov random field model performs well for highly sparse point-based data where there are large variations or clustering across the space; whereas the discretized log Gaussian Cox process produces good fit in dense and clustered point-based data. One should generally consider the nature and structure of the point-based data in order to choose the appropriate method in modelling a discretized spatial point-based data.

Graph theory based representation of building information models for access control applications

Building information models are increasingly being utilised for facility management of large facilities such as critical infrastructures. In such environments, it is valuable to utilise the vast amount of data contained within the building information models to improve access control administration. The use of building information models in access control scenarios can provide 3D visualisation of buildings as well as many other advantages such as automation of essential tasks including path finding, consistency detection, and accessibility verification. However, there is no mathematical model for building information models that can be used to describe and compute these functions. In this paper, we show how graph theory can be utilised as a representation language of building information models and the proposed security related functions. This graph-theoretic representation allows for mathematically representing building information models and performing computations using these functions.

Development of realistic design fire time-temperature curves for the testing of cold-formed steel wall system

Fire resistance rating of light gauge steel frame (LSF) wall systems is obtained from fire tests based on the standard fire time-temperature curve. However, fire severity has increased in modern buildings due to higher fuel loads as a result of modern furniture and light weight constructions that make use of thermoplastics materials, synthetic foams and fabrics. Some of these materials are high in calorific values and increase both the spread of fire growth and heat release rate, thus increasing the fire severity beyond that of the standard fire curve. Further, the standard fire curve does not include a decay phase that is present in natural fires. Despite the increasing usage of LSF walls, their behaviour in real building fires is not fully understood. This paper presents the details of a research study aimed at developing realistic design fire curves for use in the fire tests of LSF walls. It includes a review of the characteristics of building fires, previously developed fire time-temperature curves, computer models and available parametric equations. The paper highlights that real building fire time-temperature curves depend on the fuel load representing the combustible building contents, ventilation openings and thermal properties of wall lining materials, and provides suitable values of many required parameters including fuel loads in residential buildings. Finally, realistic design fire time-temperature curves simulating the fire conditions in modern residential buildings are proposed for the testing of LSF walls.

A strain-based failure criterion for pillar stability analysis

Strain-based failure criteria have several advantages over stress-based failure criteria: they can account for elastic and inelastic strains, they utilise direct, observables effects instead of inferred effects (strain gauges vs. stress estimates), and model complete stress-strain curves including pre-peak, non-linear elasticity and post-peak strain weakening. In this study, a strain-based failure criterion derived from thermodynamic first principles utilising the concepts of continuum damage mechanics is presented. Furthermore, implementation of this failure criterion into a finite-element simulation is demonstrated and applied to the stability of underground mining coal pillars. In numerical studies, pillar strength is usually expressed in terms of critical stresses or stress-based failure criteria where scaling with pillar width and height is common. Previous publications have employed the finite-element method for pillar stability analysis using stress-based failure criterion such as Mohr-Coulomb and Hoek-Brown or stress-based scalar damage models. A novel constitutive material model, which takes into consideration anisotropy as well as elastic strain and damage as state variables has been developed and is presented in this paper. The damage threshold and its evolution are strain-controlled, and coupling of the state variables is achieved through the damage-induced degradation of the elasticity tensor. This material model is implemented into the finite-element software ABAQUS and can be applied to 3D problems. Initial results show that this new material model is capable of describing the non-linear behaviour of geomaterials commonly observed before peak strength is reached as well as post-peak strain softening. Furthermore, it is demonstrated that the model can account for directional dependency of failure behaviour (i.e. anisotropy) and has the potential to be expanded to environmental controls like temperature or moisture.

Remaining useful life prediction of rotating equipment using covariate-based hazard models : industry applications

The ability to estimate the expected Remaining Useful Life (RUL) is critical to reduce maintenance costs, operational downtime and safety hazards. In most industries, reliability analysis is based on the Reliability Centred Maintenance (RCM) and lifetime distribution models. In these models, the lifetime of an asset is estimated using failure time data; however, statistically sufficient failure time data are often difficult to attain in practice due to the fixed time-based replacement and the small population of identical assets. When condition indicator data are available in addition to failure time data, one of the alternate approaches to the traditional reliability models is the Condition-Based Maintenance (CBM). The covariate-based hazard modelling is one of CBM approaches. There are a number of covariate-based hazard models; however, little study has been conducted to evaluate the performance of these models in asset life prediction using various condition indicators and data availability. This paper reviews two covariate-based hazard models, Proportional Hazard Model (PHM) and Proportional Covariate Model (PCM). To assess these models’ performance, the expected RUL is compared to the actual RUL. Outcomes demonstrate that both models achieve convincingly good results in RUL prediction; however, PCM has smaller absolute prediction error. In addition, PHM shows over-smoothing tendency compared to PCM in sudden changes of condition data. Moreover, the case studies show PCM is not being biased in the case of small sample size.

A Web-based system enabling the integration, analysis, and 3D sub-surface visualization of groundwater monitoring data and geological models

The 3D Water Chemistry Atlas is an intuitive, open source, Web-based system that enables the three-dimensional (3D) sub-surface visualization of ground water monitoring data, overlaid on the local geological model (formation and aquifer strata). This paper firstly describes the results of evaluating existing virtual globe technologies, which led to the decision to use the Cesium open source WebGL Virtual Globe and Map Engine as the underlying platform. Next it describes the backend database and search, filtering, browse and analysis tools that were developed to enable users to interactively explore the groundwater monitoring data and interpret it spatially and temporally relative to the local geological formations and aquifers via the Cesium interface. The result is an integrated 3D visualization system that enables environmental managers and regulators to assess groundwater conditions, identify inconsistencies in the data, manage impacts and risks and make more informed decisions about coal seam gas extraction, waste water extraction, and water reuse.

Elevated temperature mechanical properties of hollow flange channel sections

The fire performance of cold-formed steel members is an important criterion to be verified for their successful use in structural applications. However, lack of clear design guidance on their fire performance has inhibited their usage in buildings. Their elevated temperature mechanical properties, i.e., yield strengths, elastic moduli and stress–strain relationships, are imperative for the fire design. In the past many researchers have proposed elevated temperature mechanical property reduction factors for cold-formed steels, however, large variations exist among them. The LiteSteel Beam (LSB), a hollow flange channel section, is manufactured by a combined cold-forming and electric resistance welding process. Its web, inner and outer flange elements have different yield strengths due to varying levels of cold-working caused by their manufacturing process. Elevated temperature mechanical properties of LSBs are not the same even within their cross-sections. Therefore an experimental study was undertaken to determine the elevated temperature mechanical properties of steel plate elements in LSBs. Elevated temperature tensile tests were performed on web, inner and outer flange specimens taken from LSBs, and their results are presented in this paper including their comparisons with previous studies. Based on the test results and the proposed values from previous studies and fire design standards, suitable predictive equations are proposed for the determination of elevated temperature mechanical properties of LSB web and flange elements. Suitable stress–strain models are also proposed for the plate elements of this cold-formed and welded hollow flange channel section.