Modification of the trapped field in bulk high-temperature superconductors as a result of the drilling of a pattern of artificial columnar holes

The trapped magnetic field is examined in bulk high-temperature superconductors that are artificially drilled along their c-axis. The influence of the hole pattern on the magnetization is studied and compared by means of numerical models and Hall probe mapping techniques. To this aim, we consider two bulk YBCO samples with a rectangular cross-section that are drilled each by six holes arranged either on a rectangular lattice (sample I) or on a centered rectangular lattice (sample II). For the numerical analysis, three different models are considered for calculating the trapped flux: (i), a two-dimensional (2D) Bean model neglecting demagnetizing effects and flux creep, (ii), a 2D finite-element model neglecting demagnetizing effects but incorporating magnetic relaxation in the form of an E-J power law, and, (iii), a 3D finite element analysis that takes into account both the finite height of the sample and flux creep effects. For the experimental analysis, the trapped magnetic flux density is measured above the sample surface by Hall probe mapping performed before and after the drilling process. The maximum trapped flux density in the drilled samples is found to be smaller than that in the plain samples. The smallest magnetization drop is found for sample II, with the centered rectangular lattice. This result is confirmed by the numerical models. In each sample, the relative drops that are calculated independently with the three different models are in good agreement. As observed experimentally, the magnetization drop calculated in the sample II is the smallest one and its relative value is comparable to the measured one. By contrast, the measured magnetization drop in sample (1) is much larger than that predicted by the simulations, most likely because of a change of the microstructure during the drilling process.

The role of artificial intelligence as 'text' within design

The paper describes a new approach to artificial intelligence (AI) and its role in design. This approach argues that AI can be seen as 'text', or in other words as a medium for the communication of design knowledge and information between designers. This paper will apply these ideas to reinterpreting an existing knowledge-based system (KBS) design tool, that is, CADET - a product design evaluation tool. The paper will discuss the authorial issues, amongst others, involved in the development of AI and KBS design tools by adopting this new approach. Consequently, the designers' rights and responsibilities will be better understood as the knowledge medium, through its concern with authorship, returns control to users rather than attributing the system with agent status. © 1998 Elsevier Science Ltd. All rights reserved.

STUDIES ON THE PURIFYING SPACE IN ARTIFICIAL WETLANDS WITH CATTAIL AND RUSH

调查了人工湿地水生植物根区理化特性，根系扩展的深度和位置，微生物和酶的分布状况；比较了不同深度人工湿地污水净化效果；探讨了人工湿地污水处理系统最佳净化空间位点。通过对香蒲、灯心草人工湿地的研究，发现植物的根系主要分布在基质上层２５ｃｍ区域内，在５到１０ｃｍ区域内，微生物数量最多，２５ｃｍ区域次之，３５ｃｍ以下较少。系统表层磷酸酶，葡聚糖脱水酶和蛋白酶的活性较２０ｃｍ区域内各酶活性强。对于废水的净化而言，系统２０ｃｍ和６０ｃｍ处的净化效果差别很小。结果表明，人工湿地废水处理系统上部区域为较佳净化空间。

Influence of Peripheral Velocity on Vane Shear Strength of an Artificial Clay

Shearing rate is among the most important factors affecting the undrained shear strength of clays. In particular, for seismic or storm-wave loading conditions, the shearing rate is much higher than that used in many common laboratory or field tests. The testing program described here evaluates the effect of peripheral velocity on the undrained strength inferred from the shear vane test. The study was conducted on a lightly cemented bentonite-kaolinite mixture manufactured in the laboratory, which possesses many characteristics similar to those of natural materials. Results show that the shear strength increases with increasing peripheral velocity, while the residual shear strength seems to be nearly independent of rotation rate.

Swimming dynamics of bidirectional artificial flagella

We study magnetic artificial flagella whose swimming speed and direction can be controlled using light and magnetic field as external triggers. The dependence of the swimming velocity on the system parameters (e.g., length, stiffness, fluid viscosity, and magnetic field) is explored using a computational framework in which the magnetostatic, fluid dynamic, and solid mechanics equations are solved simultaneously. A dimensionless analysis is carried out to obtain an optimal combination of system parameters for which the swimming velocity is maximal. The swimming direction reversal is addressed by incorporating photoresponsive materials, which in the photoactuated state can mimic natural mastigonemes. © 2013 American Physical Society.

Preliminary model development for predicting strength and stiffness of cement-stabilized soils using artificial neural networks

This paper presents ongoing work on data collection and collation from a large number of laboratory cement-stabilization projects worldwide. The aim is to employ Artificial Neural Networks (ANN) to establish relationships between variables, which define the properties of cement-stabilized soils, and the two parameters determined by the Unconfined Compression Test, the Unconfined Compressive Strength (UCS), and stiffness, using E50 calculated from UCS results. Bayesian predictive neural network models are developed to predict the UCS values of cement-stabilized inorganic clays/silts, as well as sands as a function of selected soil mix variables, such as grain size distribution, water content, cement content and curing time. A model which can predict the stiffness values of cement-stabilized clays/silts is also developed and compared to the UCS model. The UCS model results emulate known trends better and provide more accurate estimates than the results from the E50 stiffness model. © 2013 American Society of Civil Engineers.

Swimming dynamics of bidirectional artificial flagella.

We study magnetic artificial flagella whose swimming speed and direction can be controlled using light and magnetic field as external triggers. The dependence of the swimming velocity on the system parameters (e.g., length, stiffness, fluid viscosity, and magnetic field) is explored using a computational framework in which the magnetostatic, fluid dynamic, and solid mechanics equations are solved simultaneously. A dimensionless analysis is carried out to obtain an optimal combination of system parameters for which the swimming velocity is maximal. The swimming direction reversal is addressed by incorporating photoresponsive materials, which in the photoactuated state can mimic natural mastigonemes.