Dynamic rupture in a 3-D particle-based simulation of a rough planar fault

An appreciation of the physical mechanisms which cause observed seismicity complexity is fundamental to the understanding of the temporal behaviour of faults and single slip events. Numerical simulation of fault slip can provide insights into fault processes by allowing exploration of parameter spaces which influence microscopic and macroscopic physics of processes which may lead towards an answer to those questions. Particle-based models such as the Lattice Solid Model have been used previously for the simulation of stick-slip dynamics of faults, although mainly in two dimensions. Recent increases in the power of computers and the ability to use the power of parallel computer systems have made it possible to extend particle-based fault simulations to three dimensions. In this paper a particle-based numerical model of a rough planar fault embedded between two elastic blocks in three dimensions is presented. A very simple friction law without any rate dependency and no spatial heterogeneity in the intrinsic coefficient of friction is used in the model. To simulate earthquake dynamics the model is sheared in a direction parallel to the fault plane with a constant velocity at the driving edges. Spontaneous slip occurs on the fault when the shear stress is large enough to overcome the frictional forces on the fault. Slip events with a wide range of event sizes are observed. Investigation of the temporal evolution and spatial distribution of slip during each event shows a high degree of variability between the events. In some of the larger events highly complex slip patterns are observed.

London-van der Waals adhesiveness of rough particles

Van der Waals forces often dominate interactions and adhesion between fine particles and, in turn, decisively influence the bulk behaviour of powders. However, so far there is no effective means to characterize the adhesive behaviour of such particles. A complication is that most powder particles have rough surfaces, and it is the asperities on the surfaces that touch, confounding the actual surface that is in contact. Conventional approaches using surface energy provide limited information regarding adhesion, and pull-off forces measured through atomic force microscope (AFM) are highly variable and difficult to interpret. In this paper we develop a model which combines the Rumpf-Rabinovich and the JKR-DMT theories to account simultaneously for the effects of surface roughness and deformation on adhesion. This is applied to a 'characteristic asperity' which may be easily obtained from AFM measurements. The concept of adhesiveness, a material property reflecting the influences of elastic deformability, surface roughness, and interfacial surface energy, is introduced as an efficient and quantitative measure of the adhering tendency of a powder. Furthermore, a novel concept of specific adhesiveness is proposed as a convenient tool for characterizing and benchmarking solid materials. This paper provides an example to illustrate the use of the proposed theories. (c) 2005 Elsevier B.V. All rights reserved.

A rough cluster analysis of shopping orientation data

This paper describes the application of a new technique, rough clustering, to the problem of market segmentation. Rough clustering produces different solutions to k-means analysis because of the possibility of multiple cluster membership of objects. Traditional clustering methods generate extensional descriptions of groups, that show which objects are members of each cluster. Clustering techniques based on rough sets theory generate intensional descriptions, which outline the main characteristics of each cluster. In this study, a rough cluster analysis was conducted on a sample of 437 responses from a larger study of the relationship between shopping orientation (the general predisposition of consumers toward the act of shopping) and intention to purchase products via the Internet. The cluster analysis was based on five measures of shopping orientation: enjoyment, personalization, convenience, loyalty, and price. The rough clusters obtained provide interpretations of different shopping orientations present in the data without the restriction of attempting to fit each object into only one segment. Such descriptions can be an aid to marketers attempting to identify potential segments of consumers.