Shaleena Manafuddin

Bio: Shaleena Manafuddin is an academic researcher. The author has contributed to research in topics: Soft computing & Pulse tube refrigerator. The author has an hindex of 1, co-authored 2 publications receiving 2 citations.

A review of soft computing techniques in materials engineering

Abstract: Within the last three decades, a solid and real amount of research efforts has been directed at the application of soft computing (SC) techniques in engineering. This paper provides a systematic review of the literature originating from these efforts which focus on materials engineering (ME) particularly sheet metals. The primary aim is to provide background information, motivation for application and an exposition to the methodologies employed in the development of soft computing technologies in engineering. Our review shows that all the works on the application of SC to sheet metal have reported excellent, good, positive or at least encouraging results. Our appraisal of the literature suggest that the interface between material engineering and intellectual systems engineering techniques, such as soft computing, is still blur. The need to formalize the computational and intelligent system engineering methodology used in sheet material, therefore, arises. We also provide a brief exposition to our on-going efforts in this direction. Although our study focuses on materials engineering in particular, we think that our findings applies to other areas of engineering as well.

Temperature prediction of a two stage pulse tube cryocooler by neural network

Abstract: Cryocoolers are refrigerating machines which are able to achieve and to maintain cryogenic temperature, i.e. temperature below 120K. The presence of moving parts in the cold area of cryocooler makes it difficult to meet the required efficiency. Thus a new concept of cryocooler, the single stage pulse tube refrigerator (PTR) satisfied many of the requirements, but the temperature attained at the cold end side is only 30K. Thus for attaining a temperature below 30K, multistaging of PTR is an attractive option. A lot of numerical models for the two stage pulse tube have been developed during the last few decades. Unfortunately, not a single simulated model has been published that can give all the design data related to the prediction of the two stage pulse tube cold end temperature. Thus the thought of a new concept for prediction of temperature of two stage pulse tube lead to the approach of artificial neural network. The objective of this work is to train an artificial neural network to learn and predict the lowest temperature attained by a two stage pulse tube cryocooler. The training is done with input as the experimental data and its output temperature as the target. The data presented as input were, the diameter and length of pulse tubes, frequency and orifice diameters. The network output is the minimum temperature attained by the cryocooler. After successful training, the validation of the network is done with validation input. When the percentage error is within the tolerance limit, the network weights and biases are used for the prediction purposes and the predicted temperature obtained by neural network approach is within the tolerance limit

Parameter estimation for chaotic systems using the cuckoo search algorithm with an orthogonal learning method

Abstract: We study the parameter estimation of a nonlinear chaotic system,which can be essentially formulated as a multidimensional optimization problem.In this paper,an orthogonal learning cuckoo search algorithm is used to estimate the parameters of chaotic systems.This algorithm can combine the stochastic exploration of the cuckoo search and the exploitation capability of the orthogonal learning strategy.Experiments are conducted on the Lorenz system and the Chen system.The proposed algorithm is used to estimate the parameters for these two systems.Simulation results and comparisons demonstrate that the proposed algorithm is better or at least comparable to the particle swarm optimization and the genetic algorithm when considering the quality of the solutions obtained.