Multi objective optimization algorithm for hybrid quantum harmonic oscillator and its application in rotor system optimization

The importance of support system dynamic fidelity in capturing turbine rotor performance in unbalance response and critical speed is considered. Lobal optimization methods based on multi-scale quantum harmonic oscillator algorithm and genetic algorithm are used, and the extent to which the non-dominated solution of objective function interactions is appropriately captured by the different algorithm models is explored. The support system models are deployed within a multi-objective optimization framework. This framework pairs a rotor finite element model with parameters to guide the search for an optimal geometry over harmonic response modes. We demonstrate the use of the quantum harmonic oscillator algorithms and hybrid genetic algorithm to capture the behavior of the high-fidelity model over the design space with reduced computational needs. Results of the optimization show quantum harmonic oscillator perturbation to be a dominant factor, with different design implications for convergence speed and repetition retention. Control parameters and convergence scale were also critical. Importantly, a number of design candidates were encountered during the optimization that performed very closely to the non-dominant frontiers, highlighting the conflicting objective functions and multi-modal nature of the problem.