High-Speed Ball Bearing Simulation via CPSO Optimization
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Solution Overview
Problem
High-speed heavy-load ball bearings in liquid rocket engines face frequent failure due to slip, fatigue, and abrasion, leading to reduced precision and increased vibration, with existing dynamic models being sensitive to initial values and prone to non-convergence, especially in high-pressure and low-temperature environments.
Innovation Solution
A simulation method is developed to obtain dynamic contact characteristic parameters of high-speed heavy-load ball bearings, involving a low-speed model computation, quasi-static analysis, and a dual-population co-evolutionary particle swarm optimization (CPSO) algorithm to determine contact angles and rigidity, using linear least-squares regression and Newton-Raphson iterative methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional dynamic models are used for high-speed heavy-load ball bearings, then the model can be established quickly, but the model is sensitive to initial values and prone to non-convergence in the solving process
Solution Approach 1:
The patent applies preliminary action by using static analysis results to provide initial values for dynamic analysis, and using low-speed analysis results to provide initial values for high-speed analysis. This preliminary preparation of initial values based on simpler cases ensures that the dynamic model solving process starts from reasonable initial conditions, significantly improving convergence reliability while maintaining modeling efficiency.
2Speed
If the ball bearing operates at high speed and heavy load, then the bearing can meet the high thrust and high rotational speed requirements, but bearing failure arising from slip, fatigue, and abrasion occurs frequently
Solution Approach 1:
The patent applies dynamics by developing a dynamic analysis model that accounts for the changing contact angles, contact forces, and deformation characteristics of ball bearings under high-speed heavy-load conditions. The model captures the dynamic behavior including spin-to-roll ratio and contact rigidity variations, enabling accurate prediction of bearing performance and failure modes under actual operating conditions, thus improving reliability assessment.
Solution Approach 2:
The patent applies parameter changes by systematically varying operating parameters (rotational speed, load conditions) and observing their effects on contact angles, contact forces, and deformation. By establishing the relationship between operating parameters and bearing performance, the model identifies optimal parameter ranges that maximize reliability while maintaining high speed and heavy load capabilities.
3Ease of manufacture
If the ball bearing is designed as a standard component, then the manufacturing and installation are simplified, but the bearing performance is greatly different from common bearings due to complicated working conditions
Solution Approach 1:
The patent applies local quality by developing specialized analysis methods for specific critical regions and parameters of ball bearings under high-speed heavy-load conditions. Rather than requiring complete redesign of standard components, the model focuses on accurately capturing local contact characteristics, deformation patterns, and stress distributions in critical areas, allowing standard bearings to be optimized for specific demanding applications.
Data Source
AI summary
A simulation method for dynamic contact characteristics of a high-speed heavy-load ball bearing in a liquid rocket engine, mainly including: establishing a low-speed heavy-load ball bearing model of a liquid rocket engine according to a normal contact stress of a ball bearing, and performing iterative computation on the low-speed heavy-load ball bearing model of the liquid rocket engine to obtain static contact characteristics of the heavy-load ball bearing; establishing a high-speed heavy-load ball bearing model of the liquid rocket engine based on a theory of quasi-static analysis; and taking computed values for the static contact characteristics of the heavy-load ball bearing as initial values, substituting the initial values into the high-speed heavy-load ball bearing model, and performing iterative operation through a dual-population co-evolutionary particle swarm optimization (CPSO) algorithm to obtain dynamic contact characteristics of each of balls in the ball bearing.