Browsing by Author "Tiwari, R."

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Identification of unbalance characteristics of rotating machinery using a novel optimization-based methodology
(Springer, 2022-02-26) Abbasi, Ahmad; Firouzi, Behnam; Şendur, Polat; Ranjan, G.; Tiwari, R.; Mechanical Engineering; ŞENDUR, Polat; Abbasi, Ahmad; Firouzi, Behnam
In this study, a novel optimization-based method is proposed to determine the parameters of a rotating unbalance in a rotor-bearing system. For that purpose, the weighted sum of squared difference between the analytical and predicted unbalance response due to rotational unbalance is considered as the objective function. A hybrid algorithm integrating salp swarm algorithm and Nelder–Mead algorithms is presented for detecting unbalance magnitude and phase as the unbalance parameters. Parameters of the aforementioned optimization algorithm are determined systematically using the Taguchi design of experiments method. The efficiency of the proposed method is compared with various optimization algorithms in the literature. The optimization method is validated with different unbalances experimentally to consider the real-world conditions. The results show the superiority of the proposed hybrid algorithm in terms of the accuracy of the unbalance parameters and computational efficiency.
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Multi-strategy Gaussian Harris hawks optimization for fatigue life of tapered roller bearings
(Springer, 2022-12) Abbasi, Ahmad; Firoozi, Behnam; Şendur, Polat; Heidari, A. A.; Tiwari, R.; Mechanical Engineering; ŞENDUR, Polat; Abbasi, Ahmad; Firoozi, Behnam
Bearing is one of the most fundamental components of rotary machinery, and its fatigue life is a crucial factor in designing. The design optimization of tapered roller bearing (TRB) is a complex design problem because various arrays of designing parameters and functional requirements should be fulfilled. Since there are many design variables and nonlinear constraints, presenting an optimal design of TRBs poses some challenges for metaheuristic algorithms. The Harris hawks optimization (HHO) algorithm is a robust nature-inspired method with unique exploitation and exploration phases due to its time-varying structure. However, this metaheuristic algorithm may still converge to local optima for more challenging problems such as the design of TRBs. Therefore, this study aims to improve the accuracy and efficiency of the shortcomings of this algorithm. The performance of the proposed algorithm is first evaluated for the TRB optimization problem. The TRB optimization design has nine design variables and 26 constraints because of geometrical dimensions and strength conditions. The productivity of the proposed method is compared with diverse metaheuristic algorithms in the literature. The results demonstrate the significant development of dynamic load capacity in comparison to the standard value. Furthermore, the enhanced version of the HHO algorithm presented in this study is benchmarked with various well-known engineering problems. For supplementary materials regarding algorithms in this research, readers can refer to https://aliasgharheidari.com.