论文标题

使用本构模型和广义回归神经网络预测磁性记忆合金(MSMA)的电动力的预测

Predictions of Electromotive Force of Magnetic Shape Memory Alloy (MSMA) Using Constitutive Model and Generalized Regression Neural Network

论文作者

Emu, Md Esharuzzaman

论文摘要

铁磁形状的记忆合金(MSMAS),例如Ni-MN-GA单晶,由于在室温下施加的磁场而引起的形状记忆效应。在可变的磁场和恒定偏置应力载荷下,MSMA已用于致动应用。这项工作为Ni-MN-GA单晶的现有宏观磁机械模型引入了新功能。该模型包括以下事实:D Silva等人观察到的两个变体中的磁易于轴并非完全垂直。这种偏移有助于解释MSMA的一些功率收集能力。将模型预测与在Ni-MN-GA单晶上收集的实验数据进行比较。实验包括具有恒定偏置磁场载荷(模拟功率收获或感测)和具有恒定偏置压缩应力(模拟驱动)的现场控制负载。每种类型的测试均在几个不同的负载水平下进行,并且在没有MSMA样本的情况下测量了施加的场,因此消除电磁不会影响Eberle等人建议的实验测量场。结果显示模型预测与实验数据之间的一致性。尽管该模型可以很好地预测实验结果,但并未捕获实验数据的所有特征。为了捕获所有实验特征,最终,使用广义回归神经网络(GRNN)来训练实验数据(应力,应变,磁场和EMF),以便可以做出相当更好的预测。

Ferromagnetic shape memory alloys (MSMAs), such as Ni-Mn-Ga single crystals, can exhibit the shape memory effect due to an applied magnetic field at room temperature. Under a variable magnetic field and a constant bias stress loading, MSMAs have been used for actuation applications. This work introduced a new feature to the existing macroscale magneto-mechanical model for Ni-Mn-Ga single crystal. This model includes the fact that the magnetic easy axis in the two variants is not exactly perpendicular as observed by D silva et al. This offset helps explain some of the power harvesting capabilities of MSMAs. Model predictions are compared to experimental data collected on a Ni-Mn-Ga single crystal. The experiments include both stress-controlled loading with constant bias magnetic field load (which mimics power harvesting or sensing) and fieldcontrolled loading with constant bias compressive stress (which mimics actuation). Each type of test was performed at several different load levels, and the applied field was measured without the MSMA specimen present so that demagnetization does not affect the experimentally measured field as suggested by Eberle et al. Results show decent agreement between model predictions and experimental data. Although the model predicts experimental results decently, it does not capture all the features of the experimental data. In order to capture all the experimental features, finally, a generalized regression neural network (GRNN) was used to train the experimental data (stress, strain, magnetic field, and emf) so that it can make a reasonably better prediction.

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