An interval uncertainty analysis method for structural response bounds using feedforward neural network differentiation

This paper proposes a new interval uncertainty analysis method for structural response bounds with uncertain‑but-bounded parameters by using feedforward neural network (FNN) differentiation. The information of partial derivative may be unavailable analytically for some complicated engineering problems. To overcome this drawback, the FNNs of real structural responses with respect to structure parameters are first constructed in this work. The first-order and second-order partial derivative formulas of FNN are derived via the backward chain rule of partial differentiation, thus the partial derivatives could be determined directly. Especially, the influences of structures of multilayer FNNs on the accuracy of the first-order and second-order partial derivatives are analyzed. A numerical example shows that an FNN with the appropriate structure parameters is capable of approximating the first-order and second-order partial derivatives of an arbitrary function. Based on the parameter perturbation method using these partial derivatives, the extrema of the FNN can be approximated without requiring much computational time. Moreover, the subinterval method is introduced to obtain more accurate and reliable results of structural response with relatively large interval uncertain parameters. Three specific examples, a cantilever tube, a Belleville spring, and a rigid-flexible coupling dynamic model, are employed to show the effectiveness and feasibility of the proposed interval uncertainty analysis method compared with other methods.     

Application of system NCF method to ice flood prediction of the Yellow River

Combined forecasts is a well-established procedure for improving forecasting accuracy which takes advantage of the availability of both multiple information and computing resources for data-intensive forecasting. Therefore, based on the combination of engineering fuzzy set theory and artificial neural network theory as well as genetic algorithms and combined forecast theory, the system Non-linear Combined Forecast (NCF) method is established for accuracy enhancement of prediction, especially of ice flood prediction. The NCF values from single forecast model for Inner Mongolia Reach of the Yellow River are given. The case shows that the method has clear physical meanings and precise consequences. Compared with any single model, the system NCF method is more rational, effective and accurate.     

Geometric phase in SU(N) interferometry

An interferometric scheme to study Abelian geometric phase shift over the manifold SU(N)/U(N − 1) is presented. Presented by H. de Guise at the DI-CRM Workshop held in Prague, 18–21 June 2000. This work has been supported by two Macquarie University Research Grants and by an Australian Research Council Large Grant.     

On solution of nonlinear singular integral equations with shift in generalized Hölder space

This paper produces sufficient conditions for the convergence of the modified Newton–Kantorovich method for the solution of a certain class of nonlinear singular integral equations with Carleman shift (NLSIES).     

A two-fluid model of gas-assisted atomization including flow through the nozzle,phase inversion,and spray dispersion

This paper describes a new approach to modelling compressible gas–liquid flows that undergo change of the continuous phase. The presented model includes the system of the ensemble averaged Navier–Stokes equations together with the particle number density equation for each phase. The constitutive equations that depend on the flow regime are obtained from many sub-models that have been developed alongside the main model. Droplet size is allowed to vary in the flow field but is considered constant within a control volume. Bubbles and droplets break-up and coalescence models are adapted to the flow conditions. The proposed model for atomization treats it as a catastrophic phase inversion that takes place over the surface determined by the local values of phase volume fractions. The model is applied to simulate the premixed air-assisted atomization of water in a nozzle-type device. The computational domain includes the nozzle and the surrounding area of the spray dispersion. The model performance has been verified by comparing the predicted and measured liquid flow rates in the spray as well as the pressure values along the nozzle wall. Computational results are analysed, and the main flow features are presented.     

Kinetic Studies of Metal Speciation Using Inductively-Coupled Plasma Mass Spectrometry

Abstract

Kinetic studies of uptake of metal ions by the Chelex batch technique were made to determine Cd, Cu and Pb speciation in model solutions, a snow sample and a river surface water sample. Inductively-coupled plasma mass spectrometry (ICP-MS) and graphite furnace atomic absorption spectrometry (GFAAS) were used for direct determination of these metals. ICP-MS with the solution nebulization technique minimized contamination and adsorption problems involved in the discrete sampling technique of GFAAS, and hence, gave more precise and accurate results. Also, ICP-MS allowed collection of many more data points than GFAAS and was able to resolve components with similar rates of dissociation, which could not always be resolved by GFAAS with its discrete sampling technique. ICP-MS was therefore preferable to GFAAS for kinetic studies of metal speciation. The kinetic data were analyzed by the iterative deconvolution method. The applicability of the Chelex batch technique to metal speciation was validated by analysis of model solutions containing these metal ions with or without EDTA, NTA and fulvic acid. Use of the Chelex batch technique for Cd, Cu and Pb speciation in snow and river surface water samples revealed a number of kinetically distinguishable components of these metals (as complexes) ranging from one to three, probably present as aquo ions or inorganic complexes in the snow sample, and bound to macromolecules/and or colloidal materials in the river surface water sample.