用机器学习与优化算法，设计出更耐用、更稳的无气轮胎结构。
Multi Objective Design Optimization of Non Pneumatic Passenger Car Tires Using Finite Element Modeling, Machine Learning, and Particle swarm Optimization and Bayesian Optimization Algorithms
- 用高阶多项式参数化轮辐，生成250种设计变体。
- 性能提升：刚度可调53%，耐久性提高50%，振动降低43%。
- 结合粒子群与贝叶斯优化，高效处理多目标权衡问题。
无气轮胎为充气轮胎提供了有前景的替代方案，但其不连续的轮辐结构在刚度调节、耐久性和高速振动方面仍存在挑战。本研究提出一种融合生成设计、机器学习与优化算法的集成框架，用于优化乘用车用非充气轮胎（UPTIS）的轮辐结构。通过高阶多项式参数化上下轮辐轮廓，基于PCHIP方法生成约250种几何变体。采用核岭回归（KRR）预测刚度，梯度提升树（XGBoost）预测耐久性与振动，模型具备强预测能力，显著减少对计算成本高的有限元模拟（FEM）的依赖。结合粒子群优化（PSO）与贝叶斯优化（Bayesian Optimization）进行多目标优化，实现性能全面改进。最终设计相较基线实现53%的刚度可调范围、最高50%的耐久性提升以及43%的振动降低。其中PSO实现快速精准收敛，贝叶斯优化则有效探索多目标权衡关系。整体框架支持下一代高性能UPTIS轮辐结构的系统化开发。
原文摘要 · Abstract (English)
Non Pneumatic tires offer a promising alternative to pneumatic tires. However, their discontinuous spoke structures present challenges in stiffness tuning, durability, and high speed vibration. This study introduces an integrated generative design and machine learning driven framework to optimize UPTIS type spoke geometries for passenger vehicles. Upper and lower spoke profiles were parameterized using high order polynomial representations, enabling the creation of approximately 250 generative designs through PCHIP based geometric variation. Machine learning models like KRR for stiffness and XGBoost for durability and vibration achieved strong predictive accuracy, reducing the reliance on computationally intensive FEM simulations. Optimization using Particle Swarm Optimization and Bayesian Optimization further enabled extensive performance refinement. The resulting designs demonstrate 53% stiffness tunability, up to 50% durability improvement, and 43% reduction in vibration compared to the baseline. PSO provided fast, targeted convergence, while Bayesian Optimization effectively explored multi objective tradeoffs. Overall, the proposed framework enables systematic development of high performance, next generation UPTIS spoke structures.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。