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A robust Bezier based solution for nonlinear vibration and post-buckling of random checkerboard graphene nano-platelets reinforced composite beams

机译:基于鲁棒贝塞尔曲线的随机棋盘石墨烯纳米片增强复合梁非线性振动和后屈曲解决方案

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摘要

In the present study, an accurate Bezier based multi-step method is developed and implemented to find the nonlinear vibration and post-buckling configurations of Euler-Bernoulli composite beams reinforced with graphene nano-platelets (GnP). The GnP is assumed to be randomly and uniformly dispersed in the composite mix-proportion, with a random checkerboard configuration. Therefore, a probabilistic model together with an efficient simulation technique is proposed to find the effective moduli of a matrix reinforced GnP. It is worth noting that the presented micro-mechanics model found by the employed Monte-Carlo simulation matches exactly the experimental data and predicts the composite elastic constants more accurate than that found from other common methods, including the Halpin-Tsai theory. Also, for mathematical simplification, the composite beam in-plane inertia is neglected. The presented multi-step method is based on Burnstein polynomial basis functions while shows interesting potential to provide robust solutions for various initial and boundary value problems. It is found that adding a relatively low content of GnP would drastically increase the composite elastic constants, particularly in the transverse direction to fiber. In addition, the numerical results are compared with those provided by exact analytical solutions, where the stability of results suggests the effectiveness of the presented methodology.
机译:在本研究中,开发并实施了一种基于Bezier的精确多步方法,以发现石墨烯纳米薄片(GnP)增强的Euler-Bernoulli复合梁的非线性振动和后屈曲构型。假设GnP随机且均匀地分散在复合混合比例中,并具有随机棋盘格配置。因此,提出了一种概率模型以及一种有效的仿真技术来寻找矩阵增强GnP的有效模量。值得注意的是,所采用的蒙特卡洛模拟所找到的微力学模型与实验数据完全匹配,并且比包括Halpin-Tsai理论在内的其他常用方法所预测的更精确地预测了复合弹性常数。同样,为了简化数学,忽略了合成光束的平面惯性。提出的多步方法基于伯恩斯坦多项式基函数,同时显示出有趣的潜力,可以为各种初始值和边值问题提供鲁棒的解决方案。已经发现,添加相对低含量的GnP会大大增加复合弹性常数,特别是在纤维横向上。此外,将数值结果与精确分析解决方案提供的结果进行比较,结果的稳定性表明了所提出方法的有效性。

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