State Vector: A New Approach to Prediction of the Failure of Brittle Heterogeneous Media and Large Earthquakes

The concept of state vector stems from statistical physics, where it is usually used to describe activity patterns of a physical field in its manner of coarsegrain. In this paper, we propose an approach by which the state vector was applied to describe quantitatively the damage evolution of the brittle heterogeneous systems, and some interesting results are presented, i.e., prior to the macro-fracture of rock specimens and occurrence of a strong earthquake, evolutions of the four relevant scalars time series derived from the state vectors changed anomalously. As retrospective studies, some prominent large earthquakes occurred in the Chinese Mainland (e.g., the M 7.4 Haicheng earthquake on February 4, 1975, and the M 7.8 Tangshan earthquake on July 28, 1976, etc) were investigated. Results show considerable promise that the time-dependent state vectors could serve as a kind of precursor to predict earthquakes.

Load-Unload Response Ratio and Accelerating Moment/Energy Release Critical Region Scaling and Earthquake Prediction

The main idea of the Load-Unload Response Ratio (LURR) is that when a system is stable, its response to loading corresponds to its response to unloading, whereas when the system is approaching an unstable state, the response to loading and unloading becomes quite different. High LURR values and observations of Accelerating Moment/Energy Release (AMR/AER) prior to large earthquakes have led different research groups to suggest intermediate-term earthquake prediction is possible and imply that the LURR and AMR/AER observations may have a similar physical origin. To study this possibility, we conducted a retrospective examination of several Australian and Chinese earthquakes with magnitudes ranging from 5.0 to 7.9, including Australia's deadly Newcastle earthquake and the devastating Tangshan earthquake. Both LURR values and best-fit power-law time-to-failure functions were computed using data within a range of distances from the epicenter. Like the best-fit power-law fits in AMR/AER, the LURR value was optimal using data within a certain epicentral distance implying a critical region for LURR. Furthermore, LURR critical region size scales with mainshock magnitude and is similar to the AMR/AER critical region size. These results suggest a common physical origin for both the AMR/AER and LURR observations. Further research may provide clues that yield an understanding of this mechanism and help lead to a solid foundation for intermediate-term earthquake prediction.

Prediction of structural dynamic plastic shear failure by Johnson's damage number

It is proved that Johnson's damage number is the sole similarity parameter for dynamic plastic shear failure of structures loaded impulsively, therefore, dynamic plastic shear failure can be understood when damage number reaches a critical value. It is suggested that the damage number be generally used to predict the dynamic plastic shear failure of structures under various kinds of dynamic loads (impulsive loading, rectangular pressure pulse, exponential pressure pulse, etc.,). One of the advantages for using the damage number to predict such kind of failure is that it is conveniently used for dissimilar material modeling.

High Aspect Ratio (L/D) Riser Viv Prediction Using Wake Oscillator Model

A two-dimensional (2-D) vortex-induced vibration (VIV) prediction model for high aspect ratio (LID) riser subjected to uniform and sheared flow is studied in this paper. The nonlinear structure equations are considered. The near wake dynamics describing the fluctuating nature of vortex shedding is modeled using classical van der Pol equation. A new approach was applied to calibrate the empirical parameters in the wake oscillator model. Compared the predicted results with the experimental data and computational fluid dynamic (CFD) results. Good agreements are observed. It can be concluded that the present model can be used as simple computational tool in predicting some aspects of VIV of long flexible structures. (C) 2008 Elsevier Ltd. All rights reserved.

Fast Computing For Lurr Of Earthquake Prediction

The LURR theory is a new approach for earthquake prediction, which achieves good results in earthquake prediction within the China mainland and regions in America, Japan and Australia. However, the expansion of the prediction region leads to the refinement of its longitude and latitude, and the increase of the time period. This requires increasingly more computations, and the volume of data reaches the order of GB, which will be very difficult for a single CPU. In this paper, a new method was introduced to solve this problem. Adopting the technology of domain decomposition and parallelizing using MPI, we developed a new parallel tempo-spatial scanning program.

On the influence zone and the prediction of tensile strength of particulate polymeric composites

An intended numerical investigation is carried out. The results indicate that, even if a perfect adhesive bond is preserved between the particles and matrix materials, the two-phase element cell model is unable to predict the strength increment of the particulate polymeric composites (PPC). To explore the main reinforcing mechanism, additional microscopic experiment is performed. An ''influence zone'' was observed around each particle which is measured about 2 to 10 micrometers in thickness for a glass-polyethylene mixture. Then, an improved computational model is presented to include the ''influence zone'' effect and several mechanical behaviors of PPC are well simulated through this new model.

Prediction of mixed-mode cracking direction in random, short-fibre composite-materials

The prediction of cracking direction in composite materials is of significance to the design of composite structures. This paper presents several methods for predicting the cracking direction in the double grooved tension-shear specimen which gives mixed-mode cracking. Five different criteria are used in this analysis: two of them have been used by other investigators and the others are proposed by the present authors. The strain energy density criterion proposed by G.C. Sih is modified to take account of the influence of the anisotropy of the strength on the direction of crack. The two failure criteria of Tsai-Hill and Norris are extended to predict the crack orientation. The stress distributions in the near-notch zone are calculated by using the 8-node quadrilateral isoparametric finite element method. The predictions of all the criteria except one are in good agreement with the experimental measurement. In addition, on the basis of the FEM results, the size of the zone in which the singular term is dominant is estimated.

Prediction of particle distribution in isotropic turbulence by large-eddy simulation

The recent application of large-eddy simulation (LES) to particle-laden turbulence requires that the LES with a subgrid scale (SGS) model could accurately predict particle distributions. Usually, a SGS particle model is used to recover the small-scale structures of velocity fields. In this study, we propose a rescaling technique to recover the effects of small-scale motions on the preferential concentration of inertial particles. The technique is used to simulate particle distribution in isotropic turbulence by LES and produce consistent results with direct numerical simulation (DNS). Key words: particle distribution, particle-laden turbulence, large-eddy simulation, subgrid scale model.

Prediction Of Shear-Band Thickness In Metallic Glasses

We derive an explicit expression for predicting the thicknesses of shear bands in metallic glasses. The model demonstrates that the shear-band thickness is mainly dominated by the activation size of the shear transformation zone (STZ) and its activation free volume concentration. The predicted thicknesses agree well with the results of measurements and simulations. The underlying physics is attributed to the local topological instability of the activated STZ. The result is of significance in understanding the origin of inhomogeneous flow in metallic glasses. (C) 2009 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Prediction of Effective Moduli of Carbon Nanotube-Reinforced Composites with Waviness and Debonding

Carbon nanotubes (CNTs) have been regarded as ideal reinforcements of high-performance composites with enormous applications. However, the waviness of the CNTs and the interfacial bonding condition between them and the matrix are two key factors that influence the reinforcing efficiency. In this paper, the effects of the waviness of the CNTs and the interfacial debonding between them and the matrix on the effective moduli of CNT-reinforced composites are studied. A simple analytical model is presented to investigate the influence of the waviness on the effective moduli. Then, two methods are proposed to examine the influence of the debonding. It is shown that both the waviness and debonding can significantly reduce the stiffening effect of the CNTs. The effective moduli are very sensitive to the waviness when the latter is small, and this sensitivity decreases with the increase of the waviness. (C) 2008 Elsevier Ltd. All rights reserved.

Switch Region for Pathogenic Structural Change in Conformational Disease and Its Prediction

Many diseases are believed to be related to abnormal protein folding. In the first step of such pathogenic structural changes, misfolding occurs in regions important for the stability of the native structure. This destabilizes the normal protein conformation, while exposing the previously hidden aggregation-prone regions, leading to subsequent errors in the folding pathway. Sites involved in this first stage can be deemed switch regions of the protein, and can represent perfect binding targets for drugs to block the abnormal folding pathway and prevent pathogenic conformational changes. In this study, a prediction algorithm for the switch regions responsible for the start of pathogenic structural changes is introduced. With an accuracy of 94%, this algorithm can successfully find short segments covering sites significant in triggering conformational diseases (CDs) and is the first that can predict switch regions for various CDs. To illustrate its effectiveness in dealing with urgent public health problems, the reason of the increased pathogenicity of H5N1 influenza virus is analyzed; the mechanisms of the pandemic swine-origin 2009 A(H1N1) influenza virus in overcoming species barriers and in infecting large number of potential patients are also suggested. It is shown that the algorithm is a potential tool useful in the study of the pathology of CDs because: (1) it can identify the origin of pathogenic structural conversion with high sensitivity and specificity, and (2) it provides an ideal target for clinical treatment.

Numerical investigation on a transition prediction model

A new transition prediction model is introduced, which couples the intermittency effect into the turbulence transport equations and takes the characteristics of fluid transition into consideration to mimic the exact process of transition. Test cases include a two-dimensional incompressible plate and a two-dimensional NACA0012 airfoil. Performance of this transition model for incompressible flows is studied, with numerical results consistent to experimental data. The requirement of grid resolution for this transition model is also studied.

Cardioviral RNA structure logo analysis: entropy, correlations, and prediction

In recent years, there has been an increased number of sequenced RNAs leading to the development of new RNA databases. Thus, predicting RNA structure from multiple alignments is an important issue to understand its function. Since RNA secondary structures are often conserved in evolution, developing methods to identify covariate sites in an alignment can be essential for discovering structural elements. Structure Logo is a technique established on the basis of entropy and mutual information measured to analyze RNA sequences from an alignment. We proposed an efficient Structure Logo approach to analyze conservations and correlations in a set of Cardioviral RNA sequences. The entropy and mutual information content were measured to examine the conservations and correlations, respectively. The conserved secondary structure motifs were predicted on the basis of the conservation and correlation analyses. Our predictive motifs were similar to the ones observed in the viral RNA structure database, and the correlations between bases also corresponded to the secondary structure in the database.

Prediction and elucidation of the population dynamics of Microcystis spp. in Lake Dianchi (China) by means of artificial neural networks

Lake Dianchi is a shallow and turbid lake, located in Southwest China. Since 1985, Lake Dianchi has experienced severe cyanabacterial blooms (dominated by Microcystis spp.). In extreme cases, the algal cell densities have exceeded three billion cells per liter. To predict and elucidate the population dynamics ofMicrocystis spp. in Lake Dianchi, a neural network based model was developed. The correlation coefficient (R 2) between the predicted algal concentrations by the model and the observed values was 0.911. Sensitivity analysis was performed to clarify the algal dynamics to the changes of environmental factors. The results of a sensitivity analysis of the neural network model suggested that small increases in pH could cause significantly reduced algal abundance. Further investigations on raw data showed that the response of Microcystis spp. concentration to pH increase was dependent on algal biomass and pH level. When Microcystis spp. population and pH were moderate or low, the response of Microcystis spp. population would be more likely to be positive in Lake Dianchi; contrarily, Microcystis spp. population in Lake Dianchi would be more likely to show negative response to pH increase when Microcystis spp. population and pH were high. The paper concluded that the extremely high concentration of algal population and high pH could explain the distinctive response of Microcystis spp. population to +1 SD (standard deviation) pH increase in Lake Dianchi. And the paper also elucidated the algal dynamics to changes of other environmental factors. One SD increase of water temperature (WT) had strongest positive relationship with Microcystis spp. biomass. Chemical oxygen demand (COD) and total phosphorus (TP) had strong positive effect on Microcystis spp. abundance while total nitrogen (TN), biological oxygen demand in five days (BOD5), and dissolved oxygen had only weak relationship with Microcystis spp. concentration. And transparency (Tr) had moderate positive relationship with Microcystis spp. concentration.