Optimization design of magnetorheological damper based on multi-objective whale algorithm

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出版年:SN Applied Sciences vol. 7, no. 6 (Jun 2025), p. 531
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Springer Nature B.V.
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245 1 |a Optimization design of magnetorheological damper based on multi-objective whale algorithm 
260 |b Springer Nature B.V.  |c Jun 2025 
513 |a Journal Article 
520 3 |a An efficient optimization design method for magnetorheological (MR) dampers, aimed at enhancing the damping force output and the adjustable coefficient, is explored in this study. The structural parameters of the double-rod MR damper, which significantly influence dynamic performance, were systematically analyzed and determined through Sobol Sensitivity Analysis. On this basis, the critical parameters were automatically optimized using Non-Dominated Sorting Whale Optimization Algorithm. By analyzing the unified Pareto front, the optimal structural parameters of the MR damper are determined and verified through numerical simulations and experimental comparisons. The results show that the key parameters affecting the mechanical performance of MR dampers can be reduced to five. The MR damper designed with these optimal parameters demonstrated a 17.1% increase in the adjustable coefficient and a 1.6-fold increase in damping force. Additionally, the optimization design method exhibited notable computational efficiency with superior global convergence characteristics, effectively solving the challenges in the optimization design of MR dampers. This study further deepens the optimization design theory of MR dampers and broadens the potential for diverse engineering applications.Article Highlights<list list-type="bullet"><list-item></list-item>Sobol sensitivity analysis pinpoints critical parameters to boost optimization efficiency;<list-item>Integrated Sobol-NSWOA methodology advances MR damper optimization;</list-item><list-item>High-precision rapid-response method enables scalable MR damper production and applications.</list-item> 
653 |a Design optimization 
653 |a Control algorithms 
653 |a Sensitivity analysis 
653 |a Algorithms 
653 |a Parameter identification 
653 |a Parameter sensitivity 
653 |a Dampers 
653 |a Optimization techniques 
653 |a Genetic algorithms 
653 |a Neural networks 
653 |a Mechanical properties 
653 |a Damping 
653 |a Design 
653 |a Sorting algorithms 
653 |a Optimization algorithms 
653 |a Energy consumption 
653 |a Vibration 
653 |a Efficiency 
653 |a Parameter estimation 
653 |a Economic 
773 0 |t SN Applied Sciences  |g vol. 7, no. 6 (Jun 2025), p. 531 
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