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Accurate Modelling (accurate + modelling)
Selected AbstractsAdaptive moving mesh methods for simulating one-dimensional groundwater problems with sharp moving frontsINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Issue 11 2002Weizhang Huang Abstract Accurate modelling of groundwater flow and transport with sharp moving fronts often involves high computational cost, when a fixed/uniform mesh is used. In this paper, we investigate the modelling of groundwater problems using a particular adaptive mesh method called the moving mesh partial differential equation approach. With this approach, the mesh is dynamically relocated through a partial differential equation to capture the evolving sharp fronts with a relatively small number of grid points. The mesh movement and physical system modelling are realized by solving the mesh movement and physical partial differential equations alternately. The method is applied to the modelling of a range of groundwater problems, including advection dominated chemical transport and reaction, non-linear infiltration in soil, and the coupling of density dependent flow and transport. Numerical results demonstrate that sharp moving fronts can be accurately and efficiently captured by the moving mesh approach. Also addressed are important implementation strategies, e.g. the construction of the monitor function based on the interpolation error, control of mesh concentration, and two-layer mesh movement. Copyright © 2002 John Wiley & Sons, Ltd. [source] A novel approach to extract accurate design parameters of PiN diodeINTERNATIONAL JOURNAL OF NUMERICAL MODELLING: ELECTRONIC NETWORKS, DEVICES AND FIELDS, Issue 6 2007Tarek Ben Salah Abstract Accurate modelling of PiN diode transient behaviour is necessary to extract design parameters which are not documented in datasheets. To meet this requirement, this paper introduces a novel approach giving the possibility to identify accurate parameters of a given device. The used technique is based only on two stages. First, the design parameters are initialized and optimized. Second, they are refined by minimizing the cost function which depends on the transient switching parameters (IRM, VRM and trr). With a simple and CPU time-saving approach this technique leads to extract design parameters without necessarily knowing the exact technological architecture of the PiN diode. Moreover, in order to validate the proposed approach and the parameter extraction procedure three commercial diodes are tested. A good agreement between experimental and simulation data is obtained. Copyright © 2007 John Wiley & Sons, Ltd. [source] Discrimination of dynamical system models for biological and chemical processesJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 8 2007Sönke Lorenz Abstract In technical chemistry, systems biology and biotechnology, the construction of predictive models has become an essential step in process design and product optimization. Accurate modelling of the reactions requires detailed knowledge about the processes involved. However, when concerned with the development of new products and production techniques for example, this knowledge often is not available due to the lack of experimental data. Thus, when one has to work with a selection of proposed models, the main tasks of early development is to discriminate these models. In this article, a new statistical approach to model discrimination is described that ranks models wrt. the probability with which they reproduce the given data. The article introduces the new approach, discusses its statistical background, presents numerical techniques for its implementation and illustrates the application to examples from biokinetics. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2007 [source] Weak solutions to a stationary heat equation with nonlocal radiation boundary condition and right-hand side in Lp (p,1)MATHEMATICAL METHODS IN THE APPLIED SCIENCES, Issue 2 2009Pierre-Étienne Druet Abstract Accurate modelling of heat transfer in high-temperature situations requires accounting for the effect of heat radiation. In complex industrial applications involving dissipative heating, we hardly can expect from the mathematical theory that the heat sources will be in a better space than L1. In this paper, we focus on a stationary heat equation with nonlocal boundary conditions and Lp right-hand side, with p,1 being arbitrary. Thanks to new coercivity results, we are able to produce energy estimates that involve only the Lp norm of the heat sources and to prove the existence of weak solutions. Copyright © 2008 John Wiley & Sons, Ltd. [source] Rotation designs: orthogonal first-order designs with higher order projectivityAPPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 3 2002Dizza Bursztyn Abstract In many factorial experiments, just a few of the experimental factors account for most of the variation in the response, a situation known as factor sparsity. Accurate modelling of the factor,response relationship may require use of higher-order terms in the active factors. In such settings, it may be desirable to use a design that is able, simultaneously, to screen out the important factors and to fit higher-order models in those factors. We derive a useful class of designs by rotating standard two-level fractional factorials. A special class of rotations is developed that has some appealing symmetry properties and can accommodate more factors than the rotation designs in Bursztyn and Steinberg (J. Stat. Plann. Inference 2001;97:399). A comparison of designs based on their projection properties and alias matrices shows that the new designs are better than many other alternatives. Copyright © 2002 John Wiley & Sons, Ltd. [source] Remote visualisation of Labrador convection in large oceanic datasetsATMOSPHERIC SCIENCE LETTERS, Issue 4 2005L. J. West Abstract The oceans relinquish O(1PW) of heat into the atmosphere at high latitudes, the lion's share of which originates in localised ,hotspots' of violent convective mixing, but despite their small horizontal scale,O(10,100km),these features may penetrate deeply into the thermocline and are vital in maintaining the Atlantic Meridional Overturning Circulation (MOC). Accurate modelling of the MOC, therefore, requires a large-scale numerical model with very fine resolution. The global high-resolution ocean model, Ocean Circulation Climate Advanced Model (OCCAM) has been developed and run at the Southampton Oceanography Centre (SOC) for many years. It was configured to resolve the energetic scales of oceanic motions, and its output is stored at the Manchester Supercomputer Centre. Although this community resource represents a treasure trove of potential new insights into the nature of the world ocean, it remains relatively unexploited for a number of reasons, not the least of which is its sheer size. A system being developed at SOC under the auspices of the Grid for Ocean Diagnostics, Interactive Visualisation and Analysis (GODIVA) project makes the remote visualisation of very large volumes of data on modest hardware (e.g. a laptop with no special graphics capability) a present reality. The GODIVA system is enabling the unresolved question of oceanic convection and its relationship to large-scale flows to be investigated; a question that lies at the heart of many current climate change issues. In this article, one aspect of the GODIVA is presented, and used to locate and visualise regions of convective mixing in the OCCAM Labrador Sea. Copyright © 2006 Royal Meteorological Society [source] Interpolation processes using multivariate geostatistics for mapping of climatological precipitation mean in the Sannio Mountains (southern Italy)EARTH SURFACE PROCESSES AND LANDFORMS, Issue 3 2005Nazzareno Diodato Abstract The spatial variability of precipitation has often been a topic of research, since accurate modelling of precipitation is a crucial condition for obtaining reliable results in hydrology and geomorphology. In mountainous areas, the sparsity of the measurement networks makes an accurate and reliable spatialization of rainfall amounts at the local scale difficult. The purpose of this paper is to show how the use of a digital elevation model can improve interpolation processes at the subregional scale for mapping the mean annual and monthly precipitation from rainfall observations (40 years) recorded in a region of 1400 km2 in southern Italy. Besides linear regression of precipitation against elevation, two methods of interpolation are applied: inverse squared distance and ordinary cokriging. Cross-validation indicates that the inverse distance interpolation, which ignores the information on elevation, yields the largest prediction errors. Smaller prediction errors are produced by linear regression and ordinary cokriging. However, the results seem to favour the multivariate geostatistical method including auxiliary information (related to elevation). We conclude that ordinary cokriging is a very flexible and robust interpolation method because it can take into account several properties of the landscape; it should therefore be applicable in other mountainous regions, especially where precipitation is an important geomorphological factor. Copyright © 2005 John Wiley & Sons, Ltd. [source] Fringe element reconstruction for front tracking for three-dimensional incompressible flow analysisINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, Issue 6 2005Du-Soon Choi Abstract Fringe element reconstruction technique for tracking the free surface in three-dimensional incompressible flow analysis was developed. The flow field was calculated by the mixed formulation based on a four-node tetrahedral element with a bubble function at the centroid (P1+/P1). Since an Eulerian approach was employed in this study, the flow front interface was advected by the flow through a fixed mesh. For accurate modelling of interfacial movement, a fringe element reconstruction method developed can provide not only an accurate treatment of material discontinuity but also surface tension across the interface. The effect of surface tension was modelled by imposing tensile stress directly on the constructed surface elements at the flow front interface. To verify the numerical approach developed, the developed algorithm was applied to two examples whose solutions are available in references. Good agreement was obtained between the simulation results and these solutions. Copyright © 2005 John Wiley & Sons, Ltd. [source] Conditional phase-type distributions for modelling patient length of stay in hospitalINTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, Issue 6 2003A. H. Marshall Abstract The proportion of elderly in the population is continuing to increase, placing additional demands on highly competitive medical budgets. The management of the care of the elderly within hospitals can be assisted by the accurate modelling of the length of stay of patients in hospital. This paper uses conditional phase-type distributions for modelling the length of stay of a group of elderly patients in hospital. The model incorporates the use of Bayesian belief networks with Coxian phase-type distributions, a special type of Markov model that describes the duration of stay in hospital as a process consisting of a sequence of latent phases. The incorporation of the Bayesian belief network in the model permits the inclusion of additional patient information which may provide a better understanding of the system, in particular the incorporation of any potential causal information that may exist in the data. [source] Application of artificial neural network modelling to identify severely ill patients whose aminoglycoside concentrations are likely to fall below therapeutic concentrationsJOURNAL OF CLINICAL PHARMACY & THERAPEUTICS, Issue 5 2003S. Yamamura PhD Summary Objective:, Identification of ICU patients whose concentrations are likely to fall below therapeutic concentrations using artificial neural network (ANN) modelling and individual patient physiologic data. Method:, Data on indicators of disease severity and some physiologic data were collected from 89 ICU patients who received arbekacin (ABK) and 61 who received amikacin (AMK). Three-layer ANN modelling and multivariate logistic regression analysis were used to predict the plasma concentrations of the aminoglycosides (ABK and AMK) in the severely ill patients. Results:, Predictive performance analysis showed that the sensitivity and specificity of ANN modelling was superior to multivariate logistic regression analysis. For accurate modelling, a predictable range should be inferred from the data structure before the analysis. Restriction of the predictable region, based on the data structure, increased predictive performance. Conclusion:, ANN analysis was superior to multivariate logistic regression analysis in predicting which patients would have plasma concentrations lower than the minimum therapeutic concentration. To improve predictive performance, the predictable range should be inferred from the data structure before prediction. When applying ANN modelling in clinical settings, the predictive performance and predictable region should be investigated in detail to avoid the risk of harm to severely ill patients. [source] | ||||||||||||||||