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Use of electromagnetic induced flow fluctuations in a nanofluidic-assisted cooling device

机译:在纳米流体辅助冷却装置中使用电磁感应流量波动

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In this study, an electromagnetically controlled cooling device (ECCD) is proposed, employing magnetic nanofluid (MNF) and electromagnetohydrodynamics to ameliorate the waste-heat dissipation of mechatronic systems. The MNF was charged in a condenser chamber as an auxiliary coolant, and was prepared from fine ferromagnetic particles of iron ferrite by using a chemical coprecipitation technique. A radial basis function neural network-based training model was employed for examining the electromagnetic resonance of the ECCD at a wide heating range. Satisfactory agreement between the experimental and predicted results indicated the accuracy of the model in capturing the ECCD dynamics. The nonlinearity of ECCD dynamics were also identified as a feature of frequency resonance by mapping a discrete-time model into generalized frequency response functions (GFRFs). Accordingly, the effect of electromagnetic induction relating to ECCD performance can be further confirmed.
机译:在该研究中,提出了一种电磁控制的冷却装置(ECCD),采用磁性纳米流体(MNF)和电磁流体动力学来改善机电系统的废物散热。 将MNF在冷凝器室中加入作为辅助冷却剂,并通过使用化学共沉淀技术从铁铁氧体的细铁磁性颗粒中制备。 采用径向基函数基于神经网络的训练模型来检查ECCD在宽加热范围内的电磁共振。 实验和预测结果之间的令人满意的协议表明了模型在捕获ECCD动力学时的准确性。 通过将离散时间模型映射到广义频率响应函数(GFRFs),还将ECCD动力学的非线性被识别为频率共振的特征。 因此,可以进一步证实与ECCD性能相关的电磁感应的影响。

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