Wu Huan, Hu Yiming, Li Yinong, Gu Sanbao, Yue Ziyang, Yang Xiaoxue, Zheng Ling
State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, China.
School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China.
Materials (Basel). 2023 Oct 23;16(20):6820. doi: 10.3390/ma16206820.
Magnetorheological damper (MRD) has been successfully applied to vehicle suspension systems as an intelligent core component. Most conventional MRDs have closed rectangle-shaped magnetic circuits, resulting in a short effective working length and negligible damping force. To address the above issues, a novel full-channel effective MRD with trapezoidal magnetic rings (FEMRD_TMR) is proposed. The trapezoidal magnetic ring can shunt the magnetic circuit, distributing it evenly along the damping channel and increasing the effective working length. Additionally, which has the same variation trend as the magnetic flux through it, makes the magnetic induction intensity distribution more uniform to reduce the magnetic saturation problem. Theoretically analyzing the damping characteristics of the FEMRD_TMR, a quasi-static model is developed to forecast the output damping force. The structural design of MRD is challenging since conventional quasi-static models rely on the yield stress of magnetorheological fluid (MRF) to reflect the rheological property, which cannot be directly observed and is challenging to calculate. The Takagi-Sugeno (T-S) fuzzy neural network and a unique magnetic circuit computation are offered as a novel quasi-static modeling approach to address the issue. The MRF's yield stress is linearized into magnetic induction intensity functions by the T-S fuzzy neural network and then converted into the MRD's structural size by the special magnetic circuit calculation. Therefore, the proposed quasi-static model can directly reflect the relationship between the damping force and structure size, simplifying MRD's structure design. The novel quasi-static model is shown to be more straightforward and understandable than the conventional Bingham quasi-static model and to have approximately accurate damping force prediction when compared to experimental data.
磁流变阻尼器（MRD）作为一种智能核心部件已成功应用于车辆悬架系统。大多数传统的磁流变阻尼器具有封闭的矩形磁路，导致有效工作长度较短且阻尼力可忽略不计。为了解决上述问题，提出了一种新型的带有梯形磁环的全通道有效磁流变阻尼器（FEMRD_TMR）。梯形磁环可以使磁路分流，使其沿阻尼通道均匀分布，并增加有效工作长度。此外，它与通过它的磁通量具有相同的变化趋势，使得磁感应强度分布更加均匀，以减少磁饱和问题。对FEMRD_TMR的阻尼特性进行理论分析，建立了一个准静态模型来预测输出阻尼力。磁流变阻尼器的结构设计具有挑战性，因为传统的准静态模型依赖于磁流变液（MRF）的屈服应力来反映其流变特性，而屈服应力无法直接观测且计算具有挑战性。提出了Takagi-Sugeno（T-S）模糊神经网络和独特的磁路计算方法作为一种新型的准静态建模方法来解决这一问题。通过T-S模糊神经网络将磁流变液的屈服应力线性化为磁感应强度函数，然后通过特殊的磁路计算将其转换为磁流变阻尼器的结构尺寸。因此，所提出的准静态模型可以直接反映阻尼力与结构尺寸之间的关系，简化了磁流变阻尼器的结构设计。结果表明，与传统的宾汉姆准静态模型相比，新型准静态模型更直接、易懂，与实验数据相比，其阻尼力预测也较为准确。
