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arXiv 2001.08769physics.flu-dynmath.OCphysics.comp-ph
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二元流体最大混合的梯度基框架

A gradient-based framework for maximizing mixing in binary fluids

Maximilian F. Eggl, Peter J. Schmid

摘要:

提出了一种基于非线性直接伴随循环的二元流体混合策略优化计算框架。控制方程为非线性纳维-斯托克斯方程,通过被动标量的演化方程进行增广,并通过基于谱傅立叶的方法进行求解。搅拌器嵌入在计算域中的Brinkman惩罚技术,和搅拌器的形状和路径梯度计算从伴随的解决方案。四种情况下,增加的复杂性被认为是,这表明计算方法和算法的效率和有效性。在所有情况下,在外部施加的范围内,混合效率都得到了显著的改善。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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