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arXiv 2408.14596cond-mat.soft
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利用最优控制理论在体相活性向列液晶中实现设计的纹理与流

Achieving designed texture and flows in bulk active nematics using optimal control theory

Saptorshi Ghosh, Aparna Baskaran, Michael F. Hagan

AI总结:

本文提出一种最优控制理论框架,通过计算光生活性的时空序列,驱动活性向列系统突破无控时的混沌不稳定状态,实现预设的动力学稳态重构与功能设计。

摘要:

由于本质上处于非平衡态,活性材料可能执行在被动材料中受热力学禁止的功能。然而,活性系统具有对应于不同动力学状态的多种局部吸引子,其中许多表现出混沌的湍流状动力学,因此无法做功或执行有用功能。设计此类系统以选择特定的动力学状态是一项艰巨的挑战。受近期实现实验活性材料光遗传学控制的进展启发,我们描述了一种最优控制理论框架,该框架能识别光生活性的时空序列,从而驱动活性向列系统趋向预设的动力学稳态。在缺乏控制时,活性向列系统对自发缺陷增殖和混沌流动动力学是不稳定的。我们证明,最优控制理论能够计算出将动力学重定向至各种替代动力学程序与功能的活动场。这包括在状态之间进行动态重构,以及选择并稳定那些不对应于吸引子、因此在无控制系统内不稳定的涌现行为。我们的结果提供了一份路线图,以利用光学控制方法在广泛的活性材料中合理设计结构、动力学与功能。

英文摘要:

Being intrinsically nonequilibrium, active materials can potentially perform functions that would be thermodynamically forbidden in passive materials. However, active systems have diverse local attractors that correspond to distinct dynamical states, many of which exhibit chaotic turbulent-like dynamics and thus cannot perform work or useful functions. Designing such a system to choose a specific dynamical state is a formidable challenge. Motivated by recent advances enabling opto-genetic control of experimental active materials, we describe an optimal control theory framework that identifies a spatiotemporal sequence of light-generated activity that drives an active nematic system toward a prescribed dynamical steady-state. Active nematics are unstable to spontaneous defect proliferation and chaotic streaming dynamics in the absence of control. We demonstrate that optimal control theory can compute activity fields that redirect the dynamics into a variety of alternative dynamical programs and functions. This includes dynamically reconfiguring between states, and selecting and stabilizing emergent behaviors that do not correspond to attractors, and are hence unstable in the uncontrolled system. Our results provide a roadmap to leverage optical control methods to rationally design structure, dynamics, and function in a wide variety of active materials.

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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