基于神经网络的径向活塞发动机输出特性预测与动态模拟验证

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2024-07-20 DOI:10.3390/machines12070491
Chunjin Li, Zhengwen Xia, Yongjie Tang
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引用次数: 0

摘要

径向活塞马达是液压系统中的执行元件,其任务是在实际应用中根据负载要求提供适当的扭矩和速度。本研究的目的是预测径向活塞液压马达的输出扭矩,并确认其合适的工作条件。在物理模型上进行了效率测定实验,得出了三十组性能数据。从实验数据中选取扭矩(输出扭矩)和机械效率作为预测目标,并采用多元线性回归和神经网络两种方法进行拟合。使用 Adams2020 软件建立了一个动态模拟模型,以获得理论扭矩值,从而验证了预测值与模拟结果之间的一致性。结果表明,动态模型的理论扭矩与物理实验之间的误差为 1.9%,神经网络预测的误差在 2% 以内。动态仿真模型可以获得高精度的理论扭矩值,为液压马达的外部负载提供参考;此外,神经网络还能准确预测输出扭矩,从而降低实验测试成本。
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Prediction and Dynamic Simulation Verification of Output Characteristics of Radial Piston Motors Based on Neural Networks
Radial piston motors are executive components in hydraulic systems, tasked with providing appropriate torque and speed according to load requirements in practical applications. The purpose of this study is to predict the output torque of radial piston hydraulic motors and confirm their suitable operating conditions. Efficiency determination experiments were conducted on physical models, yielding thirty sets of performance data. Torque (output torque) and mechanical efficiency from the experimental data were selected as prediction targets and fitted using two methods: multiple linear regression and neural networks. A dynamic simulation model was built using Adams2020 software to obtain theoretical torque values, enabling the verification of the alignment between the predicted values and simulation results. The results indicate that the error between the theoretical torque of the dynamic model and the physical experiments is 1.9%, with the error of the neural network predictions being within 2%. The dynamic simulation model can yield highly accurate theoretical torque values, providing a reference for the external load of hydraulic motors; additionally, neural networks offer accurate predictions of output torque, thus reducing experimental testing costs.
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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