用深度学习预测离心离合器结合时机，提升自动变速器性能。
Analysis of Centrifugal Clutches in Two-Speed Automatic Transmissions with Deep Learning-Based Engagement Prediction
- 基于深度神经网络，根据弹簧预紧力和摩擦块质量预测离合器结合
- 模型可高效替代复杂仿真，优化变速器换挡动态响应
- 适合汽车传动系统设计与智能控制研究者参考
本文对集成于双速自动变速器中的离心离合器系统进行了全面的数值分析，该部件是车辆扭矩传递的关键。离心离合器可在无外部控制的情况下，依据转速实现扭矩传递。研究系统考察了不同离合器配置对传动系统动态特性的影响，重点关注在各种工况下的扭矩传递、升档与降档行为。采用深度神经网络（DNN）模型，基于弹簧预紧力、摩擦块质量等参数预测离合器结合时机，提供了一种高效替代复杂仿真的方法。深度学习与数值建模的融合为优化离合器设计、提升变速器性能与效率提供了关键洞见。
原文摘要 · Abstract (English)
This paper presents a comprehensive numerical analysis of centrifugal clutch systems integrated with a two-speed automatic transmission, a key component in automotive torque transfer. Centrifugal clutches enable torque transmission based on rotational speed without external controls. The study systematically examines various clutch configurations effects on transmission dynamics, focusing on torque transfer, upshifting, and downshifting behaviors under different conditions. A Deep Neural Network (DNN) model predicts clutch engagement using parameters such as spring preload and shoe mass, offering an efficient alternative to complex simulations. The integration of deep learning and numerical modeling provides critical insights for optimizing clutch designs, enhancing transmission performance and efficiency.
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