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.

翻译：本文对集成于双速自动变速器中的离心式离合器系统进行了全面的数值分析，该系统是汽车扭矩传递的关键部件。离心式离合器能够基于转速实现扭矩传递，无需外部控制。本研究系统性地考察了不同离合器配置对变速器动力学的影响，重点关注不同工况下的扭矩传递、升档和降档行为。采用深度神经网络（DNN）模型，通过弹簧预紧力和蹄块质量等参数预测离合器接合状态，为复杂的仿真提供了一种高效替代方案。深度学习与数值建模的结合为优化离合器设计、提升变速器性能与效率提供了关键见解。