A. Vasan

Comparative analysis of Simulated Annealing, Simulated Quenching and Genetic Algorithms for optimal reservoir operation

The present study deals with the application of non-traditional optimization techniques, namely, Simulated Annealing (SA), Simulated Quenching (SQ) and Real-coded Genetic Algorithms (RGA) to a case study of Mahi Bajaj Sagar Project, India. The objective of the study is to maximize the annual net benefits subjected to various irrigation planning constraints for 75% dependable flow scenario. Extensive sensitivity analysis on various parameters used in above techniques indicated that they yielded same solution corresponding to a set of optimal combination of parameters. It is concluded that SA, SQ and RGA can be utilized for efficient planning of any irrigation system with suitable modifications.

Integrated Reservoir Management System for Flood Risk Assessment Under Climate Change

Operations of existing reservoirs will be affected by climate change. Reservoir operating rules developed using historical information will not provide the optimal use of storage under changing hydrological conditions. In this paper, an integrated reservoir management system has been developed to adapt existing reservoir operations to changing climatic conditions. The reservoir management system integrates: (1) the K-Nearest Neighbor (K-NN) weather generator model; (2) the HEC-HMS hydrological model; and (3) the Differential Evolution (DE) optimization model. Six future weather scenarios are employed to verify the integrated reservoir management system using Upper Thames River basin in Canada as a case study. The results demonstrate that the integrated system provides optimal reservoir operation rule curves that reflect the hydrologic characteristics of future climate scenarios. Therefore, they may be useful for the development of reservoir climate change adaptation strategy.

Fuzzy Multiobjective Irrigation Planning Using Particle Swarm Optimization

AbstractParticle swarm optimization (PSO) technique is applied in multiobjective irrigation planning environment, to the case study of Khadakwasla complex, India. The project consists of Khadakwasla irrigation project, Janai Sirsai lift irrigation scheme, and Purandar lift irrigation scheme. Objectives considered are net benefits, crop production, and labor employment on an annual basis. Uncertainty in the three objectives is tackled by a fuzzy approach and through hyperbolic and exponential membership functions. An irrigation planning scenario of 75% dependable inflow with groundwater and treated wastewater is analyzed (termed as 75WGWHM) and is the basis for formulating the multiobjective problem. Two additional scenarios, S1 with 75% dependable inflow without groundwater using a hyperbolic membership function and S2 with 75% dependable inflow with groundwater using an exponential membership function, are also explored and compared with 75WGWHM. It is observed from the results that hyperbolic membership...

Multi-objective differential evolution application to irrigation planning

In the present study, applicability of Multi-objective Differential Evolution (MODE) in irrigation planning perspective is demonstrated through a case study of Mahi Bajaj Sagar Project, Rajasthan, India. Three objectives, namely, net benefits, agricultural production and labour employment are analysed in the multi-objective environment. Non-dominated alternatives generated by MODE are reduced to a manageable subset with the help of K-means cluster analysis for effective decision making. Optimal number of groups is determined based on the cluster validation indices, namely, Davies–Bouldin and Dunns. It is concluded that selection of suitable parameters is necessary for effective implementation of above methodologies in real-world planning situations.

Evaluation of penalty function methods for constrained optimization using particle swarm optimization

Solving complex problems with higher dimensions involving many constraints is often a very challenging task. While solving multidimensional problems with particle swarm optimization involving several constraint factors, the penalty function approach is widely used. This paper provides a comprehensive survey of some of the frequently used constraint handling techniques currently used with particle swarm optimization. In this paper some of the penalty functional approaches for solving evolutionary algorithms are discussed and a comparative study is being performed with respect to various benchmark problems to assess their performance.

A Review on Studies of Fracture Parameters of Self-compacting Concrete

In the recent past, the use of self-compacting concrete (SSC) as a primary structural material in complex structures such as tall buildings, submerged structures, bridges, dams, liquid and gas containment structure has increased enormously. Proper understanding of the structural behavior of SCC is absolutely necessary in designing complex concrete structures. Due to the presence of micro‐cracks and other inherent flaws, the strength of the concrete structure decreases. Engineering fracture mechanics can deliver the methodology to compensate the inadequacies of conventional design concepts. It might be expected that SCC would exhibit more brittle behavior than normal/conventional concrete. The improved pore structure and better densification of matrix have great influence on the fracture characteristics of SCC. It is widely agreed that the strength, elastic modulus and fracture resistance of SCC decreases slightly with increased paste content. Increasing the volume of paste tends to make SCC brittle. Due to the quasi‐brittle nature of concrete; various computational fracture models have been developed to study the crack characterizing parameters in concrete structures, such as fictitious crack model, crack band model, two parameter fracture model, size effect model, smeared crack model, cohesive crack band model and effective crack model. Compared to conventional vibrated concrete, self‐compacting concrete often has a higher susceptibility to crack due to different mixture design, material properties and construction practices. Many studies have addressed the SCC fracture properties using different computational models. As mentioned above, all these studies are purely computational and there is no support or evidence from the experiments. This paper deals with presenting the various models as well as experimental investigations that have already been conducted by some of the researchers to study the exposure of self-compacting concrete to crack.

Multiobjective Differential Evolution and Differential Evolution for Irrigation Planning

The present paper discusses the applicability of Multiobjective Differential Evolution (MODE) and single objective Differential Evolution (DE) to a case study of Mahi Bajaj Sagar Project, Rajasthan, India. Three objectives, namely, net benefits, agricultural production and labour employment are analyzed in the multiobjective framework using MODE. Four variations (strategies) of Differential Evolution, namely, DE/rand/1/bin, DE/rand/1/exp, DE/best/1/bin and DE/best/1/exp are explored. Population size, crossover and mutation probabilities and number of generations are the parameters that are required as input to MODE. In order to have a better insight on the performance of the strategies, DE in single objective framework is also employed with the same four strategies for all the three objectives with the above settings. In addition, a comparative analysis is also made for all the four strategies in both single and multiobjective framework.

Application of Multiple Linear Regression as Downscaling Methodology for Lower Godavari Basin

This paper focused on future precipitation scenarios adopting statistical downscaling approach, namely, Multiple linear regression (MLR) for Lower Godavari basin, India. Global Climate Model (GCM), namely, GFDL-CM3 simulations, are used for downscaling purpose. Five grid points of Lower Godavari basin are considered. Reanalysis data from National Centre for Environmental Prediction (NCEP) of the study area from 1969 to 2005 is used for analysis. Precipitation is chosen as predictand. Representative Concentration Pathways (RCPs) scenarios, 4.5 and 6.0 are used for the study. Projected precipitation from 2006 to 2100 is obtained by the developed MLR model. Downscaled precipitation predictions show that there is increase in precipitation in the future.

Computational Strategy for Structural Analysis, Design, and Optimization of Trusses Using Genetic Algorithm and Particle Swarm Optimization

Integrated structural analysis and design software packages, which generally work on finite element method for analysis and design, have been gaining popularity in the field of designing since they have reduced the tedious calculation process to a simple process of just giving input values. The result generated is according to the values entered without the consideration of the feasibility. Moreover, optimization of structures has been a lesser used concept in day-to-day working and is independent of design and analysis of the structures. In this paper, an attempt has been made to integrate analysis and design along with optimization for a 3-bar truss. The objective functions for optimization are weight minimization and minimization of vertical displacement. The design and analysis components are included as constraints of the objective function to be optimized. Optimization is performed by two different methods: Genetic Algorithm and Particle Swarm Optimization. This is done to compare which of the algorithms gives better results and is less time consuming. The objective functions, that are weight and vertical displacement, are evaluated individually as single objective functions and combined as multi-objective functions. Pareto-optimal method is used to get a non-dominated set in multi-objective optimization. The inputs given are range of the area of the bar and number of generations. Also, the comparisons of results are of Particle Swarm Optimization and Genetic Algorithm is done.

Experimental Investigation and Numerical Validation on the Effect of NaOH Concentration on GGBS Based Self-compacting Geopolymer Concrete

The construction sector is booming all over the world with an increase in the demand for the production of cement. Cement produced by India by the end of the financial year 2012–2013 was about 8 % of the global production. Cement production accounts for 7 % of total CO2 emission into the atmosphere. It’s high time for a sustainable replacement for cement in order to prevent greenhouse effect and global warming and other environmental impacts. In the present study, laboratory tests were conducted to investigate the effect of sodium hydroxide concentration on the fresh properties and compressive and flexural strength of self-compacting geopolymer concrete (SCGC) incorporating ground granulated blast slag (GGBS). The experiments were conducted for five different molarities of NaOH varying between 3 and 11 M with an increment of 2 M. In order to investigate the fresh concrete properties of SCGC, slump flow, V-Funnel, and T50 tests were carried out. The workability of GGBS based self-compacting geopolymer concrete showed an evident decrease with the increase in sodium hydroxide concentration. Standard cubes and beams were casted and cured in the open atmosphere. Its 28 days compressive strength and flexural strength were found to be decreasing with the increase in sodium hydroxide concentration. Using ABAQUS numerical modeling for compressive strength and flexural strength was determined and the results obtained were found to be similar to that of the experimental results.