CAREER: Integrated Design of Intelligent Structures with Tailored Distributed Damping

该学院早期职业发展(CALEAR)计划研究项目旨在探索一种全新的方法来设计用于振动和运动控制的智能结构，创建新的数值设计策略，并就如何最好地设计这种新型智能结构产生基础性的理解。现有的智能结构使用集成的传感器和执行器，如压电材料，分布在柔性弹性材料的表面或内部来控制动态行为。在这里，嵌入粘弹性材料(VEM)被引入，以克服目前的性能限制。加入VEM是具有挑战性的，因为1)工程师不能依靠过去的经验来设计这个史无前例的系统，2)准确的VEM模型计算成本高昂。这里将使用新的集成设计优化策略来加速这种新型智能结构的设计知识的生成，并减少计算费用。数值和物理实验将集中在应用于天基望远镜的精确指向上。更精确的指向有可能显著加强科学数据收集，包括搜索系外行星。这些进展还有可能推动超静音结构稳定性或精确运动控制至关重要的其他领域(例如，制造、机器人和国防)。这个职业项目的教育和推广部分包括创建独特的动手活动，让K-12和本科生体验设计自动化工具的价值。通过与Orpheum儿童博物馆的“Girls do Science”计划和NASA喷气推进实验室的CubeSat项目的合作，这些都得到了加强。智能结构(IS)已经得到了广泛的研究，但主要是用于主动减振，而不是运动控制，并且在很大程度上避免了将空间分布的VEM用于减振。包含VEM和扩展到运动控制可以帮助达到新的性能水平，但由于没有设计历史、经过验证的设计指南或专家的直觉，因此带来了深刻的设计挑战。目前的IS技术利用空间分布的控制驱动来定制动态行为。最近的工作是将控制剪裁与分布式几何弹性子结构设计相结合。剩下的一个障碍是实际实现中对高阶结构振型的控制，这里通过战略分布的VEM来被动地抑制高阶结构振型。VEM分布可以在空间上变化，以与弹性材料协同作用并控制分布。这提高了设计的灵活性，但也增加了新的复杂性。提出了一种新的集成动态系统设计优化概念，即构造状态空间导数函数的代理模型并自适应改进。这充分利用了动力系统的固有特性来提高数值效率。利用高阶常微分方程组(ODE)近似可以精确地建立矢量机模型，但计算量较大。提出了一种新的导数函数代理建模(DFSM)策略，利用具有状态相关参数的高效低阶常微分方程组产生高精度的VEM建模。DFSM自适应地调整参数映射以提高精度，支持高效的低阶计算。在深入研究了用于阻尼器设计的DFSM之后，DFSM将被用于快速设计探索和系统地生成设计数据。然后，将使用机器学习策略来识别该数据中的模式和关系。迭代归纳过程将被用来从这些结果中确定可能的设计指南，例如首选的分布式形状关系或传感器/执行器/VEM放置指南。将使用更多的数值设计研究来验证/改进设计指南。

This Faculty Early Career Development (CAREER) Program research project aims to investigate a fundamentally new approach for designing intelligent structures for vibration and motion control, create new numerical design strategies, and to generate a foundational understanding for how best to design this new class of intelligent structures. Existing intelligent structures use integrated sensors and actuators, such as piezoelectric materials, distributed across the surface or interior of a flexible elastic material to control dynamic behavior. Here embedded viscoelastic materials (VEMs) are introduced to overcome current performance limitations. Incorporating VEMs is challenging because 1) engineers cannot rely on past experience to design this unprecedented system, and 2) accurate VEM models are computationally expensive. Here new integrated design optimization strategies will be used to accelerate generation of design knowledge for this new type of intelligent structure, and to reduce computational expense. Numerical and physical experiments will center on application to precision pointing for space-based telescopes. More precise pointing has the potential to enhance significantly scientific data gathering, including search for exoplanets. These advances also have potential to advance other domains where ultra-quiet structural stability or precision motion control is critical (e.g., manufacturing, robotics, and defense). Education and outreach components of this CAREER project involve the creation of unique hands-on activities that allow K-12 and undergraduate students to experience the value of design automation tools. These are enhanced by collaborations with the "Girls do Science" program at the Orpheum Children's Museum and a CubeSat project with NASA's Jet Propulsion Laboratory.Intelligent structures (IS) have been studied extensively, but primarily for active damping as opposed to motion control, and have largely avoided incorporation of spatially distributed VEMs for damping. Inclusion of VEMs and extension to motion control could help achieve new performance levels, but introduces a profound design challenge as no design history, validated design guidelines, or expert intuition exist. Current IS technology utilizes spatially distributed control actuation to tailor dynamic behavior. Recent work has combined control tailoring with distributed geometric elastic substructure design. A remaining obstacle is control of high-order structural modes in practical implementations, and is addressed here via strategically distributed VEMs to damp high-order modes passively. VEM distribution can be varied spatially to synergize with elastic material and control distribution. This enhances design flexibility, but this adds a new level of complexity. A new concept for integrated dynamic system design optimization where surrogate models of the state space derivative function are constructed and improved adaptively is planned. This capitalizes on the intrinsic properties of dynamic systems for numerical efficiency. VEMs can be modeled accurately, but with great computational expense, using high-order ordinary differential equation (ODE) approximations. The new derivative function surrogate modeling (DFSM) strategy is posited to produce high-accuracy VEM modeling using efficient low-order ODEs with state-dependent parameters. DFSM adjusts parameter mappings adaptively to improve accuracy, which supports efficient low-order computation. After thorough study of DFSM for damped IS design, DFSM will then be used for rapid design exploration and systematic generation of design data. Machine learning strategies will then be used to identify patterns and relationships within this data. An iterative inductive process will be used to identify possible design guidelines from these results, such as preferred distributed shape relationships or sensor/actuator/VEM placement guidelines. Additional numerical design studies will be used to validate/refine design guidelines.