Nonlinear Vibration Model Parameter Optimization for Complex Devices
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods struggle to effectively improve the vibration damping performance of complex devices like tanks and tracked vehicles, which affects their smoothness and maneuverability.
Innovation Solution
A parameter optimization method for a nonlinear vibration model of complex devices using a tree structure model, dynamic simulation, and small sample deep learning to simulate and optimize vibration characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manufacturing methods are used for complex devices, then structural parameters can be optimized, but the manufacturing cycle is long and optimization is inconvenient
Solution Approach 1:
The patent replaces traditional mechanical manufacturing optimization with a computational approach using neural networks and stochastic gradient descent algorithms. The system substitutes physical trial-and-error manufacturing iterations with digital simulation and mathematical optimization, dramatically reducing the manufacturing cycle while improving optimization convenience through automated parameter adjustment.
Solution Approach 2:
The patent systematically changes structural parameters of the complex device through automated optimization algorithms. By using neural networks to model the relationship between structural parameters and vibration characteristics, the system can efficiently explore parameter space and identify optimal configurations without lengthy manufacturing cycles.
2Object-affected harmful factors
If small sample deep learning is applied to optimize structural parameters, then vibration acceleration is reduced and manufacturing cost is lowered, but the method requires sophisticated algorithm implementation
Solution Approach 1:
The patent introduces a neural network as an intermediary computational model that bridges the gap between complex physical systems and optimization objectives. This intermediary absorbs the algorithmic complexity, providing a user-friendly interface where designers can specify optimization goals without needing to master sophisticated algorithms, while still achieving vibration reduction through small sample learning.
3Measurement precision
If comprehensive dynamic simulation is performed for different pavement spectra and vehicle speeds, then vibration characteristics are accurately captured, but computational time increases
Solution Approach 1:
The patent performs preliminary actions by pre-computing vibration characteristics across multiple pavement spectra and vehicle speeds using dynamic simulation. These pre-computed results are stored and used to train the neural network, allowing the system to accurately capture vibration characteristics without repeating computationally intensive simulations during the optimization phase, thus reducing overall computational time.
Data Source
AI summary
Parameter optimization method for nonlinear vibration model of complex device, comprising: 1) constructing various structures of complex device into tree structure, to form tree-shaped complex device model subsystem, and carrying out sign convention for dynamic analysis; 2) establishing complex device dynamic model to obtain dynamic relationships among all parts of complex device; 3) according to contact and collision conditions in advancing process of physical complex device, adding constraint relationships among parts in dynamic simulation software; 4) on basis of dynamic simulation software, establishing virtual prototype model of complex device, and determining target parameter and optimization target; 5) simulating vibration characteristics of complex device for different levels of pavement spectrums and different vehicle speeds; 6) adding required input point and output point for virtual prototype model; 7) on basis of optimization algorithm of numerical solution in small sample deep learning, obtaining optimal parameter.