Optimum design of simple rotor system supported by journal bearing using enhanced genetic algorithm

This paper presents a combined algorithm for optimum design of flexible rotor system supported by journal bearing. The proposed algorithm (Enhanced Genetic Algorithm, EGA) is the synthesis of a modified genetic algorithm and simplex method. A genetic algorithm (GA) is well known as a useful optimization technique for complex, nonlinear, and multi-optimization problems. The modified GA gives the candidate solutions in global search and then the solutions will be treated as initial values in the local search by the simplex method. The EGA is not only faster than the standard genetic algorithm, but also provides a more accurate solution. In addition, this algorithm can find both the global and the local optimum solutions at the same time. Through two standard test functions, the advantages of the proposed hybrid algorithm has been confirmed. Finally, to optimize a simple rotor system supported by journal bearing, EGA is applied. The radial clearance, length to diameter ratio and average viscosity of the journal bearing are chosen as the design parameters. The objective function is the minimization of a maximum quality factor of a flexible rotor system in the operating speed range.