Multi-objective Hybrid Teaching-learning-based Optimization Method for Structural Parameters of Finger Seal

doi:10.3901/JME.2023.09.157

Abstract

Abstract: The finger seal is an advanced flexible seal, but the influence of its structural parameters on its performance is so complex that it is difficult for traditional modeling methods to optimize them for these methods are unable to describe the mapping relationship between structural parameters and its performance. To solve this problem, BP neural network is used to construct the fitting relationship among the hysteresis rate, the average contact pressure and the radius of the base circle, the number of finger beams and other structural parameters based on which a multi-objective optimization model is established to simultaneously minimize hysteresis-rate and average contact pressure (MHACP); Then the hybrid teaching-learning-based optimization (HTLBO) method is combined with Pareto domination method to solve the model; Finally, BP neural network, MHACP and HTLBO are verified by simulation experiments. The data indicated that the overall linear regression fit of the BP neural network exceeds 0.99; the simulation results of MHACP are equivalent to ANSYS on the equivalence interval[0.7, 1] at 95% Confidence Interval (CI); and MHACP can better reflect the characteristics of finger seal structure and the optimization of finger seal structure parameters can be realized by solving MHACP. HTLBO, due to its good stability and optimization performance, can effectively improve the hysteresis and wearing of finger seals and provide multiple sets of solutions with different performance preferences to meet the actual engineering requirements. A universal method is provided for structural parameter optimization in the field of mechanical seals.

Key words: structural of finger seal, multi-objective optimization, pareto, teaching-learning-based optimization

CLC Number:

TG156

WANG Juan, LIU Meihong, ZHU Shixing, CHEN Wenbo, LI Yuxian, SUN Junfeng. Multi-objective Hybrid Teaching-learning-based Optimization Method for Structural Parameters of Finger Seal[J]. Journal of Mechanical Engineering, 2023, 59(9): 157-170.

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