D. P. Vakharia

Bio: D. P. Vakharia is an academic researcher from Sardar Vallabhbhai National Institute of Technology, Surat. The author has contributed to research in topics: Bearing (mechanical) & Fixture. The author has an hindex of 10, co-authored 40 publications receiving 3805 citations.

Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems

Abstract: A new efficient optimization method, called 'Teaching-Learning-Based Optimization (TLBO)', is proposed in this paper for the optimization of mechanical design problems. This method works on the effect of influence of a teacher on learners. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The population is considered as a group of learners or a class of learners. The process of TLBO is divided into two parts: the first part consists of the 'Teacher Phase' and the second part consists of the 'Learner Phase'. 'Teacher Phase' means learning from the teacher and 'Learner Phase' means learning by the interaction between learners. The basic philosophy of the TLBO method is explained in detail. To check the effectiveness of the method it is tested on five different constrained benchmark test functions with different characteristics, four different benchmark mechanical design problems and six mechanical design optimization problems which have real world applications. The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort. Results show that TLBO is more effective and efficient than the other optimization methods for the mechanical design optimization problems considered. This novel optimization method can be easily extended to other engineering design optimization problems.

A Study on the Performance of a Magnetic-Fluid-Based Hydrodynamic Short Journal Bearing

Abstract: Efforts have been made to study and analyze the performance of a hydrodynamic short journal bearing under the presence of a magnetic fluid lubricant. With the usual assumptions of hydrodynamic lubrication, the associated Reynolds equation for the fluid pressure is solved with appropriate boundary conditions. In turn, this is then used to calculate the load-carrying capacity which results in the calculation of friction. The computed results presented in graphical form suggest that the bearing system registers an improved performance owing to the magnetic fluid as compared to the conventional lubricant. It is clearly observed that the load-carrying capacity increases nominally while the coefficient of friction decreases significantly. Besides, it is seen that the bearing can support a load even when there is no flow of lubricant. In addition, this type of study may offer an additional degree of freedom from design point of view in terms of the forms of the magnitude of the magnetic fluid.

SCA: A Sine Cosine Algorithm for solving optimization problems

Abstract: This paper proposes a novel population-based optimization algorithm called Sine Cosine Algorithm (SCA) for solving optimization problems. The SCA creates multiple initial random candidate solutions and requires them to fluctuate outwards or towards the best solution using a mathematical model based on sine and cosine functions. Several random and adaptive variables also are integrated to this algorithm to emphasize exploration and exploitation of the search space in different milestones of optimization. The performance of SCA is benchmarked in three test phases. Firstly, a set of well-known test cases including unimodal, multi-modal, and composite functions are employed to test exploration, exploitation, local optima avoidance, and convergence of SCA. Secondly, several performance metrics (search history, trajectory, average fitness of solutions, and the best solution during optimization) are used to qualitatively observe and confirm the performance of SCA on shifted two-dimensional test functions. Finally, the cross-section of an aircraft's wing is optimized by SCA as a real challenging case study to verify and demonstrate the performance of this algorithm in practice. The results of test functions and performance metrics prove that the algorithm proposed is able to explore different regions of a search space, avoid local optima, converge towards the global optimum, and exploit promising regions of a search space during optimization effectively. The SCA algorithm obtains a smooth shape for the airfoil with a very low drag, which demonstrates that this algorithm can highly be effective in solving real problems with constrained and unknown search spaces. Note that the source codes of the SCA algorithm are publicly available at http://www.alimirjalili.com/SCA.html .