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Dynamic simulation of concentrated macromolecular solutions with screened long-range hydrodynamic interactions: Algorithm and limitations

机译:具有筛选的远程流体动力相互作用的浓缩大分子溶液的动态模拟:算法和局限性

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Hydrodynamic interactions exert a critical effect on the dynamics of macromolecules. As the concentration of macromolecules increases, by analogy to the behavior of semidilute polymer solutions or the flow in porous media, one might expect hydrodynamic screening to occur. Hydrodynamic screening would have implications both for the understanding of macromolecular dynamics as well as practical implications for the simulation of concentrated macromolecular solutions, e.g., in cells. Stokesian dynamics (SD) is one of the most accurate methods for simulating the motions of N particles suspended in a viscous fluid at low Reynolds number, in that it considers both far-field and near-field hydrodynamic interactions. This algorithm traditionally involves an O(N~3) operation to compute Brownian forces at each time step, although asymptotically faster but more complex SD methods are now available. Motivated by the idea of hydrodynamic screening, the far-field part of the hydrodynamic matrix in SD may be approximated by a diagonal matrix, which is equivalent to assuming that long range hydrodynamic interactions are completely screened. This approximation allows sparse matrix methods to be used, which can reduce the apparent computational scaling to O(N). Previously there were several simulation studies using this approximation for monodisperse suspensions. Here, we employ newly designed preconditioned iterative methods for both the computation of Brownian forces and the solution of linear systems, and consider the validity of this approximation in polydisperse suspensions. We evaluate the accuracy of the diagonal approximation method using an intracellular-like suspension. The diffusivities of particles obtained with this approximation are close to those with the original method. However, this approximation underestimates intermolecular correlated motions, which is a trade-off between accuracy and computing efficiency. The new method makes it possible to perform large-scale and long-time simulation with an approximate accounting of hydrodynamic interactions.
机译:流体动力学相互作用对大分子动力学起关键作用。随着大分子浓度的增加,类似于半稀聚合物溶液的行为或多孔介质中的流动,人们可能会期望进行流体动力学筛选。流体动力学筛选不仅对理解大分子动力学有影响,而且对模拟浓缩的大分子溶液,例如在细胞中具有实际意义。斯托克斯动力学(SD)是模拟悬浮在低雷诺数下的粘性流体中的N粒子运动的最准确方法之一,因为它同时考虑了远场和近场流体动力相互作用。该算法传统上涉及O(N〜3)操作来计算每个时间步长的布朗力,尽管渐近地更快但更复杂的SD方法现在可用。受流体动力筛选的启发,SD中的流体动力矩阵的远场部分可以用对角矩阵近似,这等效于假设对远距离流体动力相互作用进行了完全筛选。这种近似允许使用稀疏矩阵方法,这可以将表观计算比例减小到O(N)。以前,有一些使用这种近似方法对单分散悬浮液进行的模拟研究。在这里,我们采用新设计的预处理迭代方法来计算布朗力和线性系统的解,并考虑了这种近似在多分散悬浮液中的有效性。我们评估使用细胞内样悬浮液的对角线近似方法的准确性。通过这种近似获得的粒子的扩散率与原始方法的扩散率接近。但是,这种近似方法低估了分子间相关的运动,这是精度和计算效率之间的折衷。通过新方法,可以对水动力相互作用进行近似计算,从而进行大规模和长时间的模拟。

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