Assessment of Trend in Global Drought Propensity in the Twenty-First Century Using Drought Management Index

This study attempts to perform a global analysis of the trend in drought propensity in the twenty-first century using bias corrected soil moisture simulations from two General Circulation Model (GCMs) outputs based on the Representative Concentration Pathway-8.5 (RCP8.5) scenario. Drought propensity is characterized in terms of the probabilistic index – Drought Management Index (DMI), which is suitable for the assessment of slowly varying changes in soil moisture drought on a multi-year time scale. A global gridded analysis is performed to assess the future trend in drought propensity at each location on the globe over the twenty-first century. Regional analysis is also carried out to investigate the trends, if any, at the continental scale. A significant increasing trend in drought propensity is observed in large parts of Africa, South America and Asia, whereas a significant decreasing trend is observed in the northern parts of Europe and North America. This study helps to assess the spatio-temporal propagation of global drought propensity in future and aids in identifying the regions that would be relatively more/less prone to droughts towards the end of the century.     

Study of the effect of different weave constructions on the drapeability of jute woven fabric

The growing global concern for environment on one hand and the alarming danger of carbon footprint generation along with non-biodegradability and higher toxicity generation from the use of synthetic fibres on the other have created a dire need to come back to natural fibres. During the application of fabrics at the sites for geotextile application and even in designing garments in the apparel sector, it has been observed that the ability of the fabric to assume a graceful appearance of the contour is very crucial in conveying the significance of drapeability of the natural fibre-made fabric. An attempt has been made in this work to evaluate the effect of different weave constructions on the drapeability of jute-woven fabrics to justify their application on the sites.     

A Novel Devolatilization Technique of Pre-reduction of Iron Ore Using Lean Grade Coal

The problem of properly utilizing non-coking coals in alternative iron making processes dates back to long ago. Lean grade coals with higher ash and volatile matter content (e.g. boiler grade coals) have always challenged the metallurgists to develop a suitable process for their utilization. The aim of this work is to achieve an energy efficient method for the reduction of briquetted iron ore fines using ‘SYNGAS’—which is the gaseous product (enriched with reducing agents) generated by pyrolysis of coal along with fine sized carbon particles produced during pyrolysis. A laboratory scale reduction furnace with pyrolysis facility has been designed and fabricated after in depth thermo-gravimetric analysis (TGA), differential thermal analysis (DTA), and gas chromatograph (GC) studies. A particular temperature profile has been maintained inside the furnace to achieve the optimum reduction temperature. The briquettes are reduced in the pyrolysis furnace and the extent of reduction has been calculated from the weight loss. The reduced specimens are characterized using X-ray diffractometer (XRD), scanning electron microscope (SEM), energy dispersive X-ray spectrometer (EDX), field emission scanning electron microscope (FESEM), and chemical analysis method. It is observed that iron ore can be significantly reduced to metallic iron by using the devolatilization product of lean grade coal.     

Assessment of Streamflow Variability with Upgraded HydroClimatic Conceptual Streamflow Model

Water Resources Management - HydroClimatic Conceptual Streamflow (HCCS) model is a conceptual model for prediction and future assessment of daily streamflow using climate inputs and time-varying...     

Materialized view selection using evolutionary algorithm for speeding up big data query processing

For speeding up query processing on Big Data, frequent sub-queries or views may be materialized such that the query processing cost is minimized with optimum cost of maintaining the materialized views and/or queries. Materializing frequent sub-queries and views means that resultant data set of the views reside in the memory of one or more nodes in the cluster, so that it reduces the MapReduce cost, submission and scheduling cost of Distributed File System jobs for query processing. We have defined materialized views as resultant data of frequent sub-queries and aggregation functions of a set of Big Data warehousing queries that are saved for enhancing query performance. The problem is defined as a multi-objective optimization problem for minimizing the total query processing MapReduce cost, MapReduce cost for maintaining the materialized views and the number of views selected for materializing with maximized total size of the views selected. We applied Differential Evolution algorithm and NSGA-II to study their performances for developing a recommendation system for selecting views for materializing in Big Data warehousing.     

Analytical moment based approximation for robust design optimization

The role of robust design optimization (RDO) has been eminent, ascertaining optimal configuration of engineering systems in the presence of uncertainties. However, computational aspect of RDO can often get tediously intensive in dealing with large scale systems. To address this issue, hybrid polynomial correlated function expansion (H-PCFE) based RDO framework has been developed for solving computationally expensive problems. H-PCFE performs as a bi-level approximation tool, handling the global model behavior and local functional variation. Analytical formula for the mean and standard deviation of the responses have been proposed, which reduces significant level of computations as no further simulations are required for evaluating the statistical moments within the optimization routine. Implementation of the proposed approaches have been demonstrated with two benchmark examples and two practical engineering problems. The performance of H-PCFE and its analytical version have been assessed by comparison with direct Monte Carlo simulation (MCS). Comparison with popular state-of-the-art techniques has also been presented. Excellent results in terms of accuracy and computational effort obtained makes the proposed methodology potential for further large scale industrial applications.     

Flooding Probability-Based Optimal Design of Trapezoidal Open Channel Using Freeboard as a Design Variable

A flooding probability based cost effective design of open channel section has been proposed using freeboard as an additional design variable. The freeboard of the channel is calculated based on the flooding probability value. The proposed model is solved using classical optimization techniques as well as a nondominated sorting genetic algorithm. The results of the model are compared with an earlier reported model to demonstrate its superiority and field applicability.     

Optimal Design of Composite Channels Using Genetic Algorithm

In the past, studies involving optimal design of composite channels have employed Horton’s equivalent roughness coefficient, which uses a lumped approach in assuming constant velocity across a composite channel cross section. In this paper, a new nonlinear optimization program (NLOP) is proposed based on a distributed approach that is equivalent to Lotter’s observations, which allows spatial variations in velocity across a composite channel cross section. The proposed NLOP, which consists of an objective function of minimizing total construction cost per unit length of a channel, is solved using genetic algorithm (GA). Several scenarios are evaluated, including no restrictions, restricted top width, and restricted channel side slopes, to account for certain site conditions. In addition, the proposed NLOP is modified to include constraints on maximum permissible velocities corresponding to different lining materials of the composite channel cross section, probably for the first time. The proposed methodology is applied to trapezoidal and triangular channel cross sections but can be easily extended to other shapes or compound channels. Optimal design graphs are presented to determine the channel dimensions of a composite trapezoidal channel cross section. The results obtained in this study indicate that cost savings up to 35% can be achieved for the unconstrained velocity case and up to 55% for the limiting velocity case when the proposed NLOP is solved using GA as compared with the existing NLOP solved using either the classical optimization solution technique or GA.     

Noise-induced contrast enhancement using stochastic resonance on singular values

In this paper, a dynamic stochastic resonance (DSR)-based technique in singular value domain for contrast enhancement of dark images has been presented. The internal noise due to the lack of illumination is utilized using a DSR iterative process to obtain enhancement in contrast, colorfulness as well as perceptual quality. DSR is a phenomenon that has been strategically induced and exploited and has been found to give remarkable response when applied on the singular values of a dark low-contrast image. When an image is represented as a summation of image layers comprising of eigen vectors and values, the singular values denote luminance information of each such image layer. By application of DSR on the singular values using the analogy of a bistable double-well potential model, each of the singular values is scaled to produce an image with enhanced contrast as well as visual quality. When compared with performance of some existing spatial domain enhancement techniques, the proposed DSR-SVD technique is found to give noteworthy better performance in terms of contrast enhancement factor, color enhancement factor and perceptual quality measure.