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This paper explores the use of an elitist-Jaya algorithm for economic optimization of shell-and-tube heat exchanger (STHE) design. Three different optimization problems of STHE are considered in this work. The same problems were earlier attempted by other researchers using genetic algorithm (GA), simulated annealing (SA), non-dominated sorting algorithm (NSGA-II) and design approaches motivated by constructional theory. Jaya algorithm is a newly developed algorithm and it does not require any specific parameters to be tuned except the common control parameters. Elitist version of the algorithm is proposed in this paper to optimize the setup cost and operational cost of STHE simultaneously. Discrete variable optimization is also carried out in order to take the advantage of the standard design available for heat exchanger parts. The effect of common controlling parameters e.g. population size, number of iterations and elite size, is tested by considering different combinations of the same. Furthermore, two other case studies of STHE design are considered from the literature and the performance of the elitist-Jaya algorithm is compared with the recently published results. The results of computational experiments proved the superiority of the proposed algorithm over the latest reported methods used for the optimization of the same problems. [ABSTRACT FROM AUTHOR]

Multi-objective optimization aspects of four modern machining processes namely wire-electro discharge machining process, laser cutting process, electrochemical machining process and focused ion beam micro-milling process are considered in this work. In WEDM process cutting velocity and surface quality are important objectives which are mutually conflicting in nature. Minimization of kerf taper is vital in the laser cutting process which increases with the increase in material removal rate. The ECM process is characterized by high material removal rate, but poor dimensional accuracy, high tool wear rate and high over cut. FIB micro-milling process is useful in applications where a nano-level surface finish is desired but this process is characterized by a very low material removal rate. All the above mentioned objectives are vital as they closely govern the performance of the machining processes considered in this work. Therefore, the aim of this work is to achieve these objectives through process parameter optimization. In order to handle multiple objectives simultaneously a new posteriori multi-objective optimization algorithm named as multi-objective Jaya (MO-Jaya) algorithm is proposed which can provide multiple optimal solutions in a single simulation run. The regression models for the above mentioned machining processes which were developed by previous researchers are used as fitness function for MO-Jaya algorithm. In the case of WEDM process the optimization problem is an unconstrained, linear and parameter bounded. In the case of laser cutting process the optimization problem is a non-linear, unconstrained, quadratic and parameter bounded. In the ECM process the optimization problem is a non-linear, unconstrained, quadratic and parameter bounded. The second case study of ECM process the optimization problem is a non-linear, constrained, non-quadratic and parameter bounded. In the case of FIB micro-milling process, the optimization problem is a non-linear, unconstrained, quadratic and parameter bounded. In addition, the performance of MO-Jaya algorithm is also tested on a non-linear, non-quadratic unconstrained multi-objective benchmark function of CEC2009. In order to handle the constraints effectively a heuristic approach for handling constraints known as the constrained-dominance concept is used in MO-Jaya algorithm. In order to ensure that the newly generated solutions are within the parameter bounds a parameter-bounding strategy is used in MO-Jaya algorithm. The results of MO-Jaya algorithm are compared with the results of GA, NSGA, NSGA-II, BBO, NSTLBO, PSO, SQP and Monte Carlo simulations. The results have shown the better performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In the present work, mathematical models of three important casting processes are considered namely squeeze casting, continuous casting and die casting for the parameters optimization of respective processes. A recently developed advanced optimization algorithm named as teaching–learning-based optimization (TLBO) is used for the parameters optimization of these casting processes. Each process is described with a suitable example which involves respective process parameters. The mathematical model related to the squeeze casting is a multi-objective problem whereas the model related to the continuous casting is multi-objective multi-constrained problem and the problem related to the die casting is a single objective problem. The mathematical models which are considered in the present work were previously attempted by genetic algorithm and simulated annealing algorithms. However, attempt is made in the present work to minimize the computational efforts using the TLBO algorithm. Considerable improvements in results are obtained in all the cases and it is believed that a global optimum solution is achieved in the case of die casting process. [ABSTRACT FROM AUTHOR]

Abstract: Teaching-learning-based optimization (TLBO) is a recently developed heuristic algorithm based on the natural phenomenon of teaching-learning process. In the present work, a modified version of the TLBO algorithm is introduced and applied for the multi-objective optimization of heat exchangers. Plate-fin heat exchanger and shell and tube heat exchanger are considered for the optimization. Maximization of heat exchanger effectiveness and minimization of total cost of the exchanger are considered as the objective functions. Two examples are presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The results of optimization using the modified TLBO are validated by comparing with those obtained by using the genetic algorithm (GA). [Copyright &y& Elsevier]

Abstract: An ever increasing variety of materials is available today, with each having its own characteristics, applications, advantages, and limitations. In choosing the right material, there is not always a single definite attribute of selection and the designers and engineers have to take into account a large number of material selection attributes. This paper presents a logical procedure for material selection for a given engineering application. The procedure is based on an improved compromise ranking method considering the material selection attributes and their relative importance for the application considered. Two examples are included to illustrate the approach. [Copyright &y& Elsevier]

Abstract: This paper presents a methodology for material selection for a given engineering component using graph theory and matrix approach. A ‘material suitability index’ is proposed that evaluates and ranks the materials for a given engineering component. The index is obtained from a material selection factors function, obtained from the material selection factors graph. The graph is developed considering material selection factors and their relative importance for the application considered. Two examples are included to illustrate the approach. [Copyright &y& Elsevier]

This paper presents a modified Jaya algorithm for optimizing the material costs and electric-thermal performance of an Underground Power Cable System (UPCS). A High Voltage (HV) underground cable line with three 400 kV AC cables arranged in flat formation in an exemplary case study is considered. When buried underground, three XLPE high voltage cables are situated in thermal backfill layer for ensuring the optimal thermal performance of the cable system. The study discusses the effect of thermal conductivities of soil and cable backfill material on the UPCS total investment costs. The soil thermal conductivity is assumed constant and equal to 0.8 W/(m K). The cable backfills considered in the study are as follows: sand and cement mix, Fluidized Thermal Backfill™ (FTB) and Powercrete™ a product of Heidelberg Cement Group. Constant thermal conductivities of the backfills in the dry state are assumed, respectively, 1.0 W/(m K), 1.54 W/(m K) and 3.0 W/(m K). The cable backfill dimensions and cable conductor area are selected as design variables in the optimization problem. The modified JAYA algorithm is applied to minimize material costs of UPCS under the constraint that the cable conductor temperature shall not exceed its optimum value of 65 °C. The cable temperature is determined from the two-dimensional steady state heat conduction equation discretized using the Finite Element Method (FEM). The performance of the modified Jaya algorithm was compared with classical Jaya and PSO algorithms. The modified Jaya algorithm, for the presented case study, allows one to obtain lower values of the cost function. [ABSTRACT FROM AUTHOR]