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Swarm Intelligence Approach for Rational Global Approximation of Characteristic Curves for the Van der Waals Equation of State

机译:范德华状态方程特征曲线的有理全局近似的群智能方法

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The Van der Waals (VdW) equation of state is a popular generalization of the law of ideal gases proposed time ago. In many situations, it is convenient to compute the characteristic curves of the VdW equation of state, called binodal and spinodal curves. Typically, they are constructed through data fitting from a collection of data points represented in the two-dimensional pressure-volume plane. However, the resulting models are still limited and can be further enhanced. In this paper, we propose to extend this polynomial approach by using a rational function as a fitting function. In particular, we consider a rational free-form Bezier curve, which provides a global approximation to the shape of the curve. This rational approach is more flexible than the polynomial one owing to some extra parameters, the weights. Unfortunately, data fitting becomes more difficult as these new parameters have also to be computed. In this paper we address this problem through a powerful nature-inspired swarm intelligence method for continuous optimization called the bat algorithm. Our experimental results show that the method can reconstruct the characteristic curves with very good accuracy.
机译:Van der Waals(VdW)状态方程是前段时间提出的理想气体定律的流行概括。在许多情况下,计算VdW状态方程的特性曲线(称为双曲线和旋节线)非常方便。通常,它们是通过对二维压力-体积平面中表示的数据点集合进行数据拟合来构造的。但是,所得模型仍然有限,可以进一步增强。在本文中,我们建议通过使用有理函数作为拟合函数来扩展此多项式方法。特别是,我们考虑了有理的自由形式贝塞尔曲线,该曲线为曲线的形状提供了一个全局近似值。由于一些额外的参数(权重),这种有理方法比多项式更灵活。不幸的是,由于还必须计算这些新参数,因此数据拟合变得更加困难。在本文中,我们通过一种强大的,受自然启发的群体智能方法(称为蝙蝠算法)来进行连续优化,从而解决了这一问题。我们的实验结果表明,该方法可以很好地重建特征曲线。

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