论文标题
一种基于梯度的框架，用于最大化二元流体中混合
A gradient-based framework for maximizing mixing in binary fluids
论文作者
论文摘要
提出了基于非线性直接聚合循环的计算框架，用于优化二元流体系统的混合策略。管理方程是非线性Navier-Stokes方程，由被动标量的进化方程增强，该方程是通过基于频谱傅立叶方法求解的。通过Brinkman-Penalization技术将搅拌器嵌入计算域中，并根据伴随溶液计算搅拌器的形状和路径梯度。考虑了增加复杂性增加的四种情况，这证明了计算方法和算法的效率和有效性。在所有情况下，在外部强加的界限内都可以实现混合效率的显着提高。
A computational framework based on nonlinear direct-adjoint looping is presented for optimizing mixing strategies for binary fluid systems. The governing equations are the nonlinear Navier-Stokes equations, augmented by an evolution equation for a passive scalar, which are solved by a spectral Fourier-based method. The stirrers are embedded in the computational domain by a Brinkman-penalization technique, and shape and path gradients for the stirrers are computed from the adjoint solution. Four cases of increasing complexity are considered, which demonstrate the efficiency and effectiveness of the computational approach and algorithm. Significant improvements in mixing efficiency, within the externally imposed bounds, are achieved in all cases.
