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Volume-based phase stability testing at pressure and temperature specifications

机译:压力和温度规格基于体积的相位稳定性测试

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AbstractConventional phase equilibrium calculations at temperature and pressure specifications require the resolution of the equation of state (EoS). In volume-based calculations, the EoS must not be solved for volume, which is a primary variable. The tangent plane distance (TPD) function can be expressed in terms of mole numbers and volume or of component molar densities. The stationary points of the TPD function, as well as the location of the stability test limit locus (STLL) are different for the two formulations. A modified TPD function in terms of mole numbers and volume is proposed here. Using the block structure of the Hessian matrix, it is shown that Newton iterations in volume-based stability are inherently slower than those in the conventional PT stability, since the Hessian matrix is evaluated at a lower implicitness level. The minimization of several TPD functions (in volume-based and conventional stability testing) is analyzed using various sets of independent variables and scaling procedures. A modified Cholesky factorization and a two-stage line search procedure ensure a sequence of decreasing TPD functions in all cases. The proposed methods are tested for several mixtures, with emphasis on the vicinities of singularities (STLL and spinodal). With proper scaling, the modified Newton iterations are robust and converge very fast for most conditions and reasonably fast for very difficult conditions. The proposed algorithm is not model-dependent; any pressure explicit EoS can be used, provided the required partial derivatives are available.]]>
机译:<![CDATA [ 抽象 温度和压力规范的常规相平衡计算需要解决状态方程(EOS)的分辨率。在基于批量的计算中,EOS不得为卷解决,这是一个主要变量。切线平面距离(TPD)功能可以以摩尔数和体积或组分摩尔密度表示。 TPD功能的静止点以及稳定性测试限制基因座(STLL)的位置对于两种配方不同。在此提出了一种修改的TPD函数在此处提出了摩尔数和体积。使用Hessian矩阵的块结构,显示基于体积的稳定性的牛顿迭代本质上比传统PT稳定性中的速度较慢,因为Hessian矩阵在较低的隐式水平下评估。使用各种独立变量和缩放程序分析几种TPD函数的最小化(基于体积的稳定性稳定性测试)。修改后的Cholesky分解和两级线路搜索过程确保在所有情况下都能减少TPD功能。所提出的方法对几种混合物进行了测试,重点是奇点的型(STLL和Spinodal)。通过适当的缩放,改进的牛顿迭代是强大的,对于大多数条件以及非常困难的条件而言,非常快速地收敛非常快。所提出的算法不是模型依赖性;如果可以使用任何压力显式EOS,所以提供所需的部分衍生物。 ]]>

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