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神经网络模型在不同类型内燃机悬置中的应用及动态分析。

Neural Network Modeling and Dynamic Analysis of Different Types of Engine Mounts for Internal Combustion Engines.

机构信息

Federal University of Technology-Paraná, Ponta Grossa 84017-220, Brazil.

出版信息

Sensors (Basel). 2022 Feb 25;22(5):1821. doi: 10.3390/s22051821.

DOI:10.3390/s22051821
PMID:35270964
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8914709/
Abstract

This paper presents the results of studies on reducing the amount of vibrations in different frequency ranges generated by a combustion engine through the use of different types of engine mounts. Three different types of engine supports are experimentally and numerically analyzed, namely an elastomeric engine mount, an elastomeric engine mount with a hydraulic component and standard decoupling, and an elastomeric engine mount with a hydraulic component and a modified decoupler-with this engineering design being a novelty in the literature. Experimental tests that considered different excitation frequencies were performed for the three types of engine mounts. Experimental data for stiffness and damping were used to obtain nonlinear mathematical models of the two systems with hydraulic components through the use of an Artificial Neural Network (ANN). For the results, all of the mathematical models presented coefficients of determination, R, greater than 0.985 for both stiffness and damping, showing an excellent fit for the nonlinear experimental data. Numerical results using a quarter-car suspension model showed a large reduction in vibration amplitudes for the first vibration model when using the hydraulic systems, with values ranging between 48.58% and 66.47%, depending on the tests. The modified system presented smaller amplitudes and smoother behavior when compared to the standard hydraulic model.

摘要

本文介绍了通过使用不同类型的发动机支架来减少发动机在不同频率范围内产生的振动的研究结果。对三种不同类型的发动机支架进行了实验和数值分析,分别是弹性发动机支架、带有液压部件和标准解耦的弹性发动机支架以及带有液压部件和改进解耦器的弹性发动机支架——这种工程设计在文献中是新颖的。对三种类型的发动机支架进行了考虑不同激励频率的实验测试。实验数据用于通过人工神经网络(ANN)获得带有液压部件的两个系统的非线性数学模型的刚度和阻尼。对于结果,所有数学模型的决定系数 R 对于刚度和阻尼都大于 0.985,表明对非线性实验数据有很好的拟合。使用四分之一车悬架模型的数值结果表明,在使用液压系统时,第一种振动模型的振动幅度大大降低,在 48.58%和 66.47%之间变化,具体取决于测试。与标准液压模型相比,改进后的系统具有更小的振幅和更平滑的行为。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8803/8914709/64e4c7299b7e/sensors-22-01821-g008a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8803/8914709/62524acd5946/sensors-22-01821-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8803/8914709/cde0b8408cf6/sensors-22-01821-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8803/8914709/e59ed4a54c3d/sensors-22-01821-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8803/8914709/eecddab5b78f/sensors-22-01821-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8803/8914709/4601cde86a84/sensors-22-01821-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8803/8914709/24148afae60a/sensors-22-01821-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8803/8914709/ba63be4e1ddb/sensors-22-01821-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8803/8914709/64e4c7299b7e/sensors-22-01821-g008a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8803/8914709/62524acd5946/sensors-22-01821-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8803/8914709/cde0b8408cf6/sensors-22-01821-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8803/8914709/e59ed4a54c3d/sensors-22-01821-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8803/8914709/eecddab5b78f/sensors-22-01821-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8803/8914709/4601cde86a84/sensors-22-01821-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8803/8914709/24148afae60a/sensors-22-01821-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8803/8914709/ba63be4e1ddb/sensors-22-01821-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8803/8914709/64e4c7299b7e/sensors-22-01821-g008a.jpg

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本文引用的文献

1
Feedforward backpropagation artificial neural networks for predicting mechanical responses in complex nonlinear structures: A study on a long bone.用于预测复杂非线性结构力学响应的前馈反向传播人工神经网络:对长骨的一项研究
J Mech Behav Biomed Mater. 2022 Apr;128:105079. doi: 10.1016/j.jmbbm.2022.105079. Epub 2022 Jan 11.