A Review of Dynamic Fracture Studies in Functionally Graded Materials

Abstract:  This article presents a review of dynamic fracture studies on functionally graded materials. A brief literature review on the fracture mechanics of graded materials is presented first. This is followed by a discussion on the higher-order asymptotic analysis of the transient elastic field surrounding the tip of a dynamically growing crack in a functionally graded material. A comprehensive experimental study of dynamic crack growth in model functionally graded material using the optical method of reflection photoelasticity and high-speed photography is then presented. The results are analysed to establish a generalised relationship between the crack velocity and the dynamic mode-I stress intensity factor (SIF). This relationship is found to be unique and is distinctly different from that previously established for the matrix material (polyester). Finally, an innovative experimental procedure is used to demonstrate the necessity of employing a fully transient stress-field representation in the analysis of optical data for an accurate prediction of the dynamic SIF history.     

A parallel technique for signal-level perceptual organization

Due to the potential for essentially unbounded scene complexity, it is often necessary to translate the sensor-derived signals into richer symbolic representations. A key initial stage in this abstraction process is signal-level perceptual organization (SLPO) involving the processes of partitioning and identification. A parallel SLPO algorithm that follows the global hypothesis testing paradigm, but breaks the iterative structure of conventional region growing through the use of α-partitioning and region filtering is presented. These two techniques segment an image such that the gray-level variation within each region can be described by a regression model. Experimental results demonstrate the effectiveness of this algorithm     

集成模拟和实际测试带来更短的设计周期和更高质量的产品

尽管模拟和测试在产品开发过程中扮演着相似的角色,但到目前为止功能上的互补并未臻完整.它们存在于各自相对隔绝的环境中(一个处于实际环境中,另一个则处于虚拟环境中),彼此的结果不能互用,在许多情况下甚至不相信彼此的结果.……     

An integrated modeling approach to predict flooding on urban basin.

Correct prediction of flood extents in urban catchments has become a challenging issue. The traditional urban drainage models that consider only the sewerage-network are able to simulate the drainage system correctly until there is no overflow from the network inlet or manhole. When such overflows exist due to insufficient drainage capacity of downstream pipes or channels, it becomes difficult to reproduce the actual flood extents using these traditional one-phase simulation techniques. On the other hand, the traditional 2D models that simulate the surface flooding resulting from rainfall and/or levee break do not consider the sewerage network. As a result, the correct flooding situation is rarely addressed from those available traditional 1D and 2D models. This paper presents an integrated model that simultaneously simulates the sewerage network, river network and 2D mesh network to get correct flood extents. The model has been successfully applied into the Tenpaku basin (Nagoya, Japan), which experienced severe flooding with a maximum flood depth more than 1.5 m on September 11, 2000 when heavy rainfall, 580 mm in 28 hrs (return period > 100 yr), occurred over the catchments. Close agreements between the simulated flood depths and observed data ensure that the present integrated modeling approach is able to reproduce the urban flooding situation accurately, which rarely can be obtained through the traditional 1D and 2D modeling approaches.     

A genetic‐based neuro‐fuzzy controller for blind equalization of time‐varying channels

This paper presents a neuro‐fuzzy network (NFN) where all its parameters can be tuned simultaneously using genetic algorithms (GAs). The approach combines the merits of fuzzy logic theory, neural networks and GAs. The proposed NFN does not require a priori knowledge about the system and eliminates the need for complicated design steps such as manual tuning of input–output membership functions, and selection of fuzzy rule base. Although, only conventional GAs have been used, convergence results are very encouraging. A well‐known numerical example derived from literature is used to evaluate and compare the performance of the network with other equalizing approaches. Simulation results show that the proposed neuro‐fuzzy controller, all parameters of which have been tuned simultaneously using GAs, offers advantages over existing equalizers and has improved performance. From the perspective of application and implementation, this paper is very interesting as it provides a new method for performing blind equalization. The main contribution of this paper is the use of learning algorithms to train a feed‐forward neural network for M‐ary QAM and PSK signals. This paper also provides a platform for researchers of the area for further development. Copyright © 2008 John Wiley & Sons, Ltd.     

Healing substrates with mobile, particle-filled microcapsules: designing a 'repair and go' system.

We model the rolling motion of a fluid-driven, particle-filled microcapsule along a heterogeneous, adhesive substrate to determine how the release of the encapsulated nanoparticles can be harnessed to repair damage on the underlying surface. We integrate the lattice Boltzmann model for hydrodynamics and the lattice spring model for the micromechanics of elastic solids to capture the interactions between the elastic shell of the microcapsule and the surrounding fluids. A Brownian dynamics model is used to simulate the release of nanoparticles from the capsule and their diffusion into the surrounding solution. We focus on a substrate that contains a damaged region (e.g. a crack or eroded surface coating), which prevents the otherwise mobile capsule from rolling along the surface. We isolate conditions where nanoparticles released from the arrested capsule can repair the damage and thereby enable the capsules to again move along the substrate. Through these studies, we establish guidelines for designing particle-filled microcapsules that perform a 'repair and go' function and thus, can be utilized to repair damage in microchannels and microfluidic devices.     

Nano-octopus: a new form of branching carbon nanofiber

A new multibranched octopus-type structure of carbon nanofibers is synthesized from a natural precursor, camphor, by a thermal chemical vapor deposition technique. An alloy of Cu:Ni catalyst is prepared by electrochemically coating nickel on a copper sheet, with nickel sulfate as an electrolyte, and heating that nickel-coated copper sheet to a higher temperature. Deposition of carbon on these substrates leads to the formation of a branched nanostructure in the temperature range of 923 K to 1023 K. The fiber diameter increases from 30 nm to 250 nm with increasing pyrolysis temperature. Detailed morphology and the internal structure of these fibers are studied by scanning and transmission electron microscopy.