Studies on flow characteristics of coal–oil–water slurry system

Experiments were carried out to investigate the rheological properties of coal–oil–water suspension containing solids of different sizes. Two different coal samples with mean particle sizes of 120 mesh, 175 mesh and 220 mesh were used. The coal concentration was varied from 5% to 25% by weight. Sodium silicate has been used as an additive to study the behavior of the variation of average viscosity of the suspension. A generalized correlation has been developed to predict the average viscosity of suspension in terms of particle diameter of the coal, concentration of coal, viscosity of the suspending medium and the concentration of water. Experimental investigations revealed that coal–oil–water suspensions show an increase in the viscosity with decrease in coal size but with the addition of an additive, the average viscosity tends to decrease initially up to a certain optimum dosages and thereafter it increases with further addition of additives. Two empirical correlations are proposed for average viscosity of the coal–oil–water suspension, μ_sL in terms of physical properties of the solid and viscosity of the suspending medium with and without additives.     

Ensemble prediction of regional droughts using climate inputs and the SVM–copula approach

In this study, the climate teleconnections with meteorological droughts are analysed and used to develop ensemble drought prediction models using a support vector machine (SVM)–copula approach over Western Rajasthan (India). The meteorological droughts are identified using the Standardized Precipitation Index (SPI). In the analysis of large‐scale climate forcing represented by climate indices such as El Niño Southern Oscillation, Indian Ocean Dipole Mode and Atlantic Multidecadal Oscillation on regional droughts, it is found that regional droughts exhibits interannual as well as interdecadal variability. On the basis of potential teleconnections between regional droughts and climate indices, SPI‐based drought forecasting models are developed with up to 3 months' lead time. As traditional statistical forecast models are unable to capture nonlinearity and nonstationarity associated with drought forecasts, a machine learning technique, namely, support vector regression (SVR), is adopted to forecast the drought index, and the copula method is used to model the joint distribution of observed and predicted drought index. The copula‐based conditional distribution of an observed drought index conditioned on predicted drought index is utilized to simulate ensembles of drought forecasts. Two variants of drought forecast models are developed, namely a single model for all the periods in a year and separate models for each of the four seasons in a year. The performance of developed models is validated for predicting drought time series for 10 years' data. Improvement in ensemble prediction of drought indices is observed for combined seasonal model over the single model without seasonal partitions. The results show that the proposed SVM–copula approach improves the drought prediction capability and provides estimation of uncertainty associated with drought predictions. Copyright © 2013 John Wiley & Sons, Ltd.     

Assessing the feasibility of CO<Subscript>2</Subscript>-enhanced oil recovery and storage in mature oil field: A case study from Cambay basin

The utilization of anthropogenic CO₂ for enhanced oil recovery (EOR) can significantly extend the production life of an oil field, and help in the reduction of atmospheric emission of anthropogenic CO₂ if sequestration is considered. This work summarizes the prospect of EOR and sequestration using CO₂ flooding from an Indian mature oil field at Cambay basin through numerical modelling, simulation and pressure study based on limited data provided by the operator. To get an insight into CO₂-EOR and safe storage process in this oil field, a conceptual sector model is developed and screening standard is proposed keeping in mind the major pay zone of the producing reservoir. To construct the geomodel, depth maps, well positions and coordinates, well data and well logs, perforation depths and distribution of petrophysical properties as well as fluid properties provided by the operator, has been considered. Based on the results from the present study, we identified that the reservoir has the potential for safe and economic geological sequestration of 15.04×10⁶ metric ton CO₂ in conjunction with a substantial increase in oil recovery of 10.4% of original oil in place. CO₂-EOR and storage in this mature field has a bright application prospect since the findings of the present work could be a better input to manage the reservoir productivity, and the pressure field for significant enhancement of oil recovery followed by safe storage.     

Hybrid Gaussian-cubic radial basis functions for scattered data interpolation

Scattered data interpolation schemes using kriging and radial basis functions (RBFs) have the advantage of being meshless and dimensional independent; however, for the datasets having insufficient observations, RBFs have the advantage over geostatistical methods as the latter requires variogram study and statistical expertise. Moreover, RBFs can be used for scattered data interpolation with very good convergence, which makes them desirable for shape function interpolation in meshless methods for numerical solution of partial differential equations. For interpolation of large datasets, however, RBFs in their usual form, lead to solving an ill-conditioned system of equations, for which, a small error in the data can cause a significantly large error in the interpolated solution. In order to reduce this limitation, we propose a hybrid kernel by using the conventional Gaussian and a shape parameter independent cubic kernel. Global particle swarm optimization method has been used to analyze the optimal values of the shape parameter as well as the weight coefficients controlling the Gaussian and the cubic part in the hybridization. Through a series of numerical tests, we demonstrate that such hybridization stabilizes the interpolation scheme by yielding a far superior implementation compared to those obtained by using only the Gaussian or cubic kernels. The proposed kernel maintains the accuracy and stability at small shape parameter as well as relatively large degrees of freedom, which exhibit its potential for scattered data interpolation and intrigues its application in global as well as local meshless methods for numerical solution of PDEs.     

Image analysis of seafloor photographs for estimation of deep-sea minerals

Factors such as non-uniform illumination of seafloor photographs and partial burial of polymetallic nodules and crusts under sediments have prevented the development of a fully automatic system for evaluating the distribution characteristics of these minerals, necessitating the involvement of a user input. A method has been developed whereby spectral signatures of different features are identified using a software ‘trained’ by a user, and the images are digitized for coverage estimation of nodules and crusts. Analysis of >20,000 seafloor photographs was carried out along five camera transects covering a total distance of 450 km at 5,100–5,300 m water depth in the Central Indian Ocean. The good positive correlation (R² > 0.98) recorded between visual and computed estimates shows that both methods of estimation are highly reliable. The digitally computed estimates were ∼10% higher than the visual estimates of the same photographs; the latter have a conservative operator error, implying that computed estimates would more accurately predict a relatively high resource potential. The fact that nodules were present in grab samples from some locations where photographs had nil nodule coverage emphasises that nodules may not always be exposed on the seafloor and that buried nodules will also have to be accounted for during resource evaluation. When coupled with accurate positioning/depth data and grab sampling, photographic estimates can provide detailed information on the spatial distribution of mineral deposits, the associated substrates, and the topographic features that control their occurrences. Such information is critical for resource modelling, the selection of mine sites, the designing of mining systems and the planning of mining operations.     

On the vibrations of a composite orthotropic cylinder placed in a magnetic field

Summary The problems of radial vibration of a composite orthotropic cylinder subjected to a uniform axial magnetic field forms the chief interest of the paper.     

Search for periodicities of the solar irradiance data from the Earth Radiation Budget Satellite (ERBS) using the periodogram method

We have analyzed the solar irradiance data from the Earth Radiation Budget Satellite(ERBS)during the time period from 1984 October 15 to 2003 October 15.By first filtering the data by Simple Exponential Smoothing,we have applied the periodogram method to the processed data in order to search for its time variation.The study exhibits multi-periodicities on these data around 110,118,574 and 740d with very high confidence levels(more than 99%).These periods are significantly similar to the periods of other solar activities which may suggest that solar irradiance may be associated with other solar activities.     

Effect of quarrying on the slope stability in Banasuramala: an offshoot valley of Western Ghats,Kerala, India

The Narippara area, which is nestled in the Banasuramala of Wayanad district, witnessed initiation of landslide, which was manifested in the form of curvilinear cracks running for a length of 170 m. In the event of continuous rainfall, these cracks may get reactivated, leading to a major landslide. This could affect the life and livelihood of a large number of families living downslope and could cause extensive damage to property. Extensive mining for building stones in this area has been a cause for much concern amongst the local population. This study aims at understanding the impact of quarrying on slope stability through geotechnical, geophysical and environmental impact assessment (EIA). Geotechnical analysis shows that the area is stable under dry condition but parts of the area are critical under wet and saturated conditions. Moreover, resistivity studies revealed that the cracks that had developed extend up to the bedrock and the suspected mechanism of failure will be translational in nature. EIA reveal that due to quarrying and mining operations, the possibility of land disturbances cannot be ruled out during the monsoon season. In the present study, an attempt is also made to suggest management strategies for mitigating further effects of landslides in this area.     

Probabilistic assessment of flood risks using trivariate copulas

In this paper, a copula-based methodology is presented for probabilistic assessment of flood risks and investigated the performance of trivariate copulas in modeling dependence structure of flood properties. The flood is a multi-attribute natural hazard and is characterized by mutually correlated flood properties peak flow, volume, and duration of flood hydrograph. For assessing flood risk, many studies have used bivariate analysis, but a more effective assessment can be possible considering all three mutually correlated flood properties simultaneously. This study adopts trivariate copulas for multivariate analysis of flood risks, and applied to a case study of flood flows of Delaware River basin at Port Jervis, NY, USA. On evaluation of various probability distributions for representation of flood variables, it is found that the flood peak flow and volumes can be best represented by Fréchet distribution, whereas flood duration by log-normal distribution. The joint distribution is modeled using four trivariate copulas, namely, three fully nested form of Archimedean copulas: Clayton, Gumbel–Hougaard, Frank copulas; and one elliptical copula: Student’s t copula. Based on distance-based performance measures, graphical tests, and tail-dependence measures, it is found that the Student’s t copula best representing the trivariate dependence structure of flood properties as compared to the other copulas. Similar results are found for bivariate copula modeling of flood variables pairs, where Student’s t copula performed better than the other copulas. The obtained copula-based joint distributions are used for multivariate analysis of flood risks, in terms of primary and secondary return periods. The resultant trivariate return periods are compared with univariate and bivariate return periods, and addressed the necessity of multivariate flood risk analysis. The study concludes that the trivariate copula-based methodology is a viable choice for effective risk assessment of floods.