Variations of methane induced pyrite formation in the accretionary wedge sediments offshore southwestern Taiwan

The accretionary wedge of offshore southwestern Taiwan contains abundant deposits of gas hydrate beneath the sea floor. High concentrations of methane in pore waters are observed at several locations with little data concerning historical methane venting available. To understand temporal variation of methane venting in sediments over geologic time, a 23-m-long Calypso piston core (MD05-2911) was collected on the flank of the Yung-An Ridge. Pore water sulfate, dissolved sulfide, dissolved iron, methane, sedimentary pyrite, acid volatile sulfide, reactive iron, organic carbon and nitrogen as well as carbonate δ¹³C were analyzed.Three zones with markedly different pyrite concentration were found at the study site. Unit I sediments (>20 mbsf) were characterized with a high amount of pyrite (251–380 μmol/g) and a δ¹³C-depleted carbonate, Unit II sediments (15–20 mbsf) with a low pyrite (15–43 μmol/g) and a high content of iron oxide mineral and Unit III sediments (<10 mbsf) by a present-day sulfate–methane interface (SMI) at 5 m with a high amount of pyrite (84–221 μmol/g) and a high concentration of dissolved sulfide.The oscillation records of pyrite concentrations are controlled by temporal variations of methane flux. With an abundant supply of methane to Unit I and III, anaerobic methane oxidation and associated sulfate reduction favor diagenetic conditions conducive for significant pyrite formation. No AOM signal was found in Unit II, characterized by typical organically-limited normal marine sediments with little pyrite formation. The AOM induced pyrite formation near the SMI generates a marked pyrite signature, rendering such formation of pyrite as a useful proxy in identifying methane flux oscillation in a methane flux fluctuate environment.     

CFD simulations of the effects of fouling and antifouling

Biofouling is a global problem in the marine industry though its effects on lift and drag are rarely discussed. This paper seeks to employ Computational Fluid Dynamics (CFD) method to quantify the effects of this problem based on the obtained flow field information. The simulation is carried out on a NACA 4424 airfoil and Defence Research Establishment Atlantic (DREA) Submarine Hull. Different levels of fouling are studied with quantified fouling height and density. As an extension, the effects of two common antifouling methods, i.e. tin-free Self-Polishing Copolymer (SPC) and Foul Release on the drag of NACA 4424 foil and the submarine hull is investigated. For NACA 4424 airfoil, fouling reduces lift-drag ratio (C_L/C_D) by up to 80% in maximum and therefore result in the significant increase in fuel consumption. Predicted flow data shows this is related to the increased flow separation region caused by the fouling. It is found that pressure gradient gradually increases from the smallest fouling height to the largest but does not vary that much for fouling of varying densities. The general trend of C_L/C_D varies with angle of attack agrees well with others experimental data. Computed results also show good agreement with experimental data for the DREA bare hull. As to antifouling, Foul Release, despite being 30% more expensive than SPC, exhibits 10-40% higher C_L/C_D as compared to SPC for NACA foil and submarine hull.     

Exploring the influence of interparticle friction on critical state behaviour using DEM

Understanding the extent to which discrete element method (DEM) simulations can capture the critical state characteristics of granular materials is important to legitimize the use of DEM in geomechanics. This paper documents a DEM study that considered the sensitivity of the critical state response characteristics to the coefficient of interparticle friction (μ) using samples with gradings that are representative of a real soil. Most of the features that are typically associated with sand behaviour at the critical state were seen to emerge from the DEM simulation data. An important deviation occurs when high μ values (μ ≥ 0.5) are used, as has been the case in a number of prior DEM studies. While there is a systematic variation in the critical state behaviour with μ for μ < 0.5, when μ ≥ 0.5, the behaviour at the critical state seems to be insensitive to further increases in μ. In contrast to observations of conventional soil response, when μ ≥ 0.5, the void ratio at the critical state initially increases with increasing mean effective stress (p′). Analysis of the DEM data and use of simple models of isolated force chains enabled some key observations. When ‘floating’ particles that do not transmit stress are eliminated from the void ratio calculation, the void ratio at the critical state decreases consistently with increasing p′. There is a transition from sliding to rolling behaviour at the contact points as μ increases. Beyond a limiting value of μ, further increases in μ do not increase the buckling resistance of individual strong force chains. Copyright © 2014 John Wiley & Sons, Ltd.     

Some thoughts on spatial data and structure analysis

The discussion is structured around two themes, namely spatial data analysis and spatial structure/process analysis. With respect to spatial data analysis, I concentrate on the issues related to geographical information systems (GIS), as well as spatial data mining and knowledge discovery in databases. With reference to spatial structure/process analysis, the emphasis is placed on the analysis of complex systems.     

An adaptive compromise programming method for multi-objective path optimization

Network routing problems generally involve multiple objectives which may conflict one another. An effective way to solve such problems is to generate a set of Pareto-optimal solutions that is small enough to be handled by a decision maker and large enough to give an overview of all possible trade-offs among the conflicting objectives. To accomplish this, the present paper proposes an adaptive method based on compromise programming to assist decision makers in identifying Pareto-optimal paths, particularly for non-convex problems. This method can provide an unbiased approximation of the Pareto-optimal alternatives by adaptively changing the origin and direction of search in the objective space via the dynamic updating of the largest unexplored region till an appropriately structured Pareto front is captured. To demonstrate the efficacy of the proposed methodology, a case study is carried out for the transportation of dangerous goods in the road network of Hong Kong with the support of geographic information system. The experimental results confirm the effectiveness of the approach.     

An intelligent expert system shell for knowledge-based Geographical Information Systems: 1. The tools

Abstract

An intelligent expert system shell for the development of knowledge-based geographical information systems (GIS) is examined in this two-part article. Basic concepts and the overall architecture of the shell are discussed in the present part. Fuzzy logic and expert systems technology are demonstrated to be appropriate methods for approximating human reasoning and enhancing the level of intelligence in GIS. The shell can be employed as an effective and efficient tool for developing knowledge-based GIS.     

A rough set approach to the discovery of classification rules in spatial data

This paper proposes a novel rough set approach to discover classification rules in real‐valued spatial data in general and remotely sensed data in particular. A knowledge induction process is formulated to select optimal decision rules with a minimal set of features necessary and sufficient for a remote sensing classification task. The approach first converts a real‐valued or integer‐valued decision system into an interval‐valued information system. A knowledge induction procedure is then formulated to discover all classification rules hidden in the information system. Two real‐life applications are made to verify and substantiate the conceptual arguments. It demonstrates that the proposed approach can effectively discover in remotely sensed data the optimal spectral bands and optimal rule set for a classification task. It is also capable of unraveling critical spectral band(s) discerning certain classes. The framework paves the road for data mining in mixed spatial databases consisting of qualitative and quantitative data.     

A locational error model for spatial features

A locational error model for spatial features in vector-based geographical information systems (GIS) is proposed in this paper. Using error in points as the fundamental building block, a stochastic model is constructed to analyse point, line, and polygon errors within a unified framework, a departure from current practices which treat errors in point and line separately. The proposed model gives, as a special case, the epsilon band model a true probabilistic meaning. Moreover, the model can also be employed to derive accuracy standards and cartographic estimates in GIS.     

Numerical modelling of the turbulent flow developing within and over a 3-d building array,part iii: a istributed drag force approach,its implementation and application

A modified k- model is used for the simulation of the mean wind speed and turbulence for a neutrally-stratified flow through and over a building array, where the array is treated as a porous medium with the drag on the unresolved buildings in the array represented by a distributed momentum sink. More specifically, this model is based on time averaging the spatially averaged Navier–Stokes equation, in which the effects of the obstacle-atmosphere interaction are included through the introduction of a distributed mean-momentum sink (representing drag on the unresolved buildings in the array). In addition, closure of the time-averaged, spatially averaged Navier–Stokes equation requires two additional prognostic equations, namely one for the time-averaged resolved-scale kinetic energy of turbulence,, and another for its dissipation rate, . The performance of the proposed model and some simplified versions derived from it is compared with the spatially averaged, time-mean velocity and various spatially averaged Reynolds stresses diagnosed from a high-resolution computational fluid dynamics (CFD) simulation of the flow within and over an aligned array of sharp-edged cubes with a plan area density of 0.25. Four different methods for diagnosis of the drag coefficient C_Dfor the aligned cube array, required for the volumetric drag force representation of the cubes, are investigated here. We found that the model predictions for mean wind speed and turbulence in the building array were not sensitive to the differing treatments of the source and sink terms in the and equations (e.g., inclusion of only the `zeroth-order' approximation for the source/sink terms compared with inclusion of a higher-order approximation for the source/sink terms in the and equations), implying that the higher-order approximations of these source/sink terms did not offer any predictive advantage. A possible explanation for this is the utilization of the Boussinesq linear stress–strain constitutive relation within the k– modelling framework, whose implicit omission of any anisotropic eddy-viscosity effects renders it incapable of predicting any strong anisotropy of the turbulence field that might exist in the building array.