Corrosion fatigue behavior of dissimilar metal weldments under nominal constant ΔK loading mode in a simulated BWR coolant environment

Corrosion fatigue behavior of the dissimilar metal weldment, Alloy 52-A508, under a simulated BWR coolant condition was studied. Corrosion fatigue crack growth rates of the dissimilar metal weldments were observed to increase with crack extension under the nominal constant ΔK loading mode. It could be accounted for by a decrease in the Cr and Ni contents and the crack closure effect with the weld depth. Finite element analysis on the welding residual stress was performed. The trend of analytical results agreed well with that of the residual stress measurements obtained by a hole-drilling strain gauge method.     

An expanded SEMATECH CIM framework for heterogeneous applications integration

A significant effort in recent computer-integrated manufacturing (CIM) development was carried out by SEMATECH who had laid out and created an open framework for the integration of manufacturing execution system applications running in semiconductor industries at the factory operation level. This paper presents the feasibility to incorporate basic manufacturing applications at the factory engineering level, which include product design, process planning, and material requirement planning, into the SEMATECH CIM framework to form a generic framework across both factory operation level and factory engineering level. The CIM framework established in this paper was aimed to provide a reusable integrated system framework that clearly specifies the functional interface boundaries and standard information model of the required components, in general, manufacturing systems at both factory operation and factory engineering levels. Standard unified modeling language (UML) diagrams and Petri nets have been utilized to model and analyze the specifications and dynamic behaviors of this generic CIM framework. The goal is to build a framework by creating a common, modular, flexible, and integrated object model that unifies an advanced object-oriented architecture concept and heterogeneous manufacturing application development in an open and multisupplier CIM system environment.     

Robust support vector regression networks for function approximation with outliers

Support vector regression (SVR) employs the support vector machine (SVM) to tackle problems of function approximation and regression estimation. SVR has been shown to have good robust properties against noise. When the parameters used in SVR are improperly selected, overfitting phenomena may still occur. However, the selection of various parameters is not straightforward. Besides, in SVR, outliers may also possibly be taken as support vectors. Such an inclusion of outliers in support vectors may lead to seriously overfitting phenomena. In this paper, a novel regression approach, termed as the robust support vector regression (RSVR) network, is proposed to enhance the robust capability of SVR. In the approach, traditional robust learning approaches are employed to improve the learning performance for any selected parameters. From the simulation results, our RSVR can always improve the performance of the learned systems for all cases. Besides, it can be found that even the training lasted for a long period, the testing errors would not go up. In other words, the overfitting phenomenon is indeed suppressed.     

Rapid calculation for convective heat transfer in laminar boundary layer by Gaussian quadratures

Summary The integral of the form , which arises in the convective heat transfer with constant wall temperature, is integrated by using Gauss-Laguerre and Gauss-Legendre Quadrature formulae. It is shown that the Nusselt number can be expressed explicitly in terms of the Prandtl number and the method proposed in this paper is valid for wide range of Prandtl numbers. Examples are given for the cases of flow over a semi-infinite plate and two-dimensional and axisymmetrical stagnations. The results are compared with the exact solutions for Prandtl numbers ranging from 0.006 to 100 (flat plate) and 0.01 to 50 (two-dimensional and axisymmetrical stagnation flows).     

Minimum end time policies for batchwise radical chain polymerization

For batchwise radical chain polymerization, the minimum end time problem is studied for the “optimal” process and “best” isothermal process by     

Use of a water-soluble fullerene derivative as precipitating reagent and matrix-assisted laser desorption/ionization matrix to selectively detect charged species in aqueous solutions

A starlike water-soluble fullerene derivative, hexa(sulfonbutyl)fullerene (C60[(CH2)4SO3-]6; HSBF), consisting of a C60 cage covalently bonded with six negatively charged sulfonate arms, was synthesized and used to selectively precipitate positively charged surfactants, amino acids, peptides, and proteins. The affinity of HSBF to the analytes depends on the charge, structure, and hydrophobic characteristics of the analytes. The ion pair precipitate was easily removed from the solution by centrifugation. After washing, the precipitate was redissolved in the solvent or buffer solution and the analyte was characterized by laser desorption ionization-time-of-flight mass spectrometry (LD-TOF). HSBF shows strong optical absorbance in the UV range, so no additional organic matrix was required to conduct LD-TOF analysis of small analytes. For the solution that contained five quaternary amines differing only in alkyl chain length, HSBF exhibits the highest affinity to the amine with the longest alkyl chain. Only the arginine signal was detected from the solution that contained 14 amino acids. The peptides with arginine as the end groups interacted most strongly with HSBF and could be selectively precipitated from a solution of a mixture of five peptides. The signals associated with a trace amount of charged peptides derived from the digestion of proteins by trypsin were greatly enhanced after concentration with HSBF. Among eight proteins in the sample solution, insulin had the strongest affinity to the HSBF and exhibited the strongest signal on the matrix-assisted laser desorption/ionization mass spectrum.     

Ocean waves propagating over a porous seabed: Residual and oscillatory mechanisms

Two mechanisms for the wave-induced pore pressures in a porous seabed, i.e. oscillatory and residual excess pore pressures, have been observed in laboratory experiments and field measurements. Most previous investigations have focused on one of the mechanisms individually. In this paper, an analytical solution for the wave-induced residual pore pressure, which is not available yet, is derived, and compared with the existing experimental data. With the new solution, a parametric analysis is performed to clarify the applicable ranges of two mechanisms. Then, a simplified approximation for the prediction of wave-induced liquefaction potential is proposed for engineering practice.     

Predictions of bridge scour: Application of a feed-forward neural network with an adaptive activation function

In this study, a new procedure to determine the optimum activation function for a neural network is proposed. Unlike previous methods of optimising activation functions, the proposed approach regards selection of the most suitable activation function as a discrete optimisation problem, which involves generating various combinations of function then evaluating their performance as activation functions in a neural network, returning the function or combination of functions which yields best result as the optimum. The efficacy of the proposed optimisation method is compared with conventional approaches using the data generated from several synthetic functions. Numerical results indicate that the network produced using the proposed method achieves a better accuracy with a smaller network size, compared to other approaches.Bridge scour problem is used to further demonstrate the performance of the proposed algorithm. Based on the training and validation results, a better estimation of both equilibrium and time dependent scour depth is produced by the neural network developed using the proposed optimisation method, compared to networks with a priori chosen activation functions. Furthermore, the performance of the proposed model is compared with predictions of empirical methods, with the former making more accurate predictions.