CRII:OAC:A Data-Driven Closed-Loop Platform for Optimal Design of Deployable Pin-Jointed Structures

可展开铰接（DPJ）结构由于其重量轻、可折叠、刚度大等优点，在航空航天、土木工程、机器人、生物医学材料等领域有着广泛的应用前景。DPJ结构由受压构件（通常为杆或支柱）和受拉构件（通常为缆索或钢筋束）组成，通过销接头连接。在操作中，这些DPJ结构具有各种性能要求，例如保持高水平的表面精度，以及实现期望的刚度和可调谐的自然频率。失去理想的表现往往会导致日本民主党结构的失灵甚至崩溃。然而，DPJ结构的优化设计与期望的性能是很难获得，由于本构建模和缺乏设计工具的问题。该项目通过创建数据驱动的闭环平台来解决这些问题，用于DPJ结构的优化设计。一系列的数据驱动的工具，创建拟议的闭环平台，将被设计和建造：（1）一种新的随机方法，用于确定一个DJP结构的初始平衡配置将被创建;和，（2）一个新的计算建模技术DPJ结构，基于机器学习和先进的无损检测，将被开发。该项目将解决结构工程的迫切需要，并提供DPJ结构的设计和计算建模的更深入的了解。该项目获得的结果可以帮助提高各个领域一类结构的性能、安全性和寿命，包括建筑、航天器、军事装备和高科技设备。该项目将通过开发一系列数据驱动工具来促进数值和高性能科学计算，并扩展固体和结构力学的建模和仿真能力，从而补充建立下一代先进网络基础设施生态系统的努力。该项目还将帮助更新结构计算建模课程。工程专业的学生将被招募和指导在这个项目。学生的培训将包括结构设计、计算建模、算法开发和实验测试。传统的结构设计是一个开环协议，其中遵循设计-建模-验证程序。该项目的主要目标是创建一个数据驱动的闭环平台，用于DPJ结构的优化设计。这个框架不变的平台是新的，提供了一个闭环结构设计协议。在该平台中，实验结果不仅用于模型验证，还可以提供训练和测试数据，以进一步提高数据驱动计算模型的性能。然后，通过使用计算模型来指导初始结构设计来闭合回路。 为实现这一目标，将开展两项工作。第一个任务是发展一个随机的方法来找形。传统的DPJ结构找形方法需要对构件进行分组，这在很大程度上依赖于结构的几何简单性。为了解决这个问题，一个新的方法，称为随机固定节点位置法，将被设计和研究。此方法的主要优点是它不使用成员分组或要求任何几何简单性。这些特点将使该方法作为一个强大的工具，在设计大规模，复杂的，不规则的DPJ结构。第二个任务是开发数据驱动的计算建模技术。本构建模技术往往过于简化DPJ结构，这导致不能反映结构的重要力学性能。由于DPJ结构的特殊性质，很少有最近开发的计算建模技术适用于DPJ结构，这在其他固体或结构中是不常见的。本项目将开发一种基于机器学习和DPJ结构无损检测的新型计算建模技术。这项技术将绕过传统的本构建模，并在处理DPJ结构时提供良好的性能。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Deployable pin-jointed (DPJ) structures, due to their being lightweight, foldable, and having high stiffness, have shown high research and development interest in state-of-the-art applications in many fields, such as aerospace and mechanical engineering, civil engineering, robotics and bio-medical materials. A DPJ structure is composed of members in compression (usually bars or struts) and tension (usually cables or tendons), connected by pin joints. In operation, these DPJ structures have various performance requirements, such as maintaining a high level of surface accuracy, and achieving desired stiffness and tunable natural frequencies. Loss of desired performance often yields malfunction or even breakdown of a DPJ structure. However, optimal design of DPJ structures with the desired performance is hard to obtain, due to issues related to constitutive modeling and lack of design tools. This project addresses these problems by creating a data-driven closed-loop platform for optimal design of DPJ structures. A series of data-driven tools, that create the proposed closed-loop platform, will be designed and built: (1) a novel stochastic method for determining an initial equilibrium configuration of a DJP structure will be created; and, (2) a new computational modeling technique for DPJ structures, based on machine learning and advanced nondestructive testing, will be developed. The project will address an urgent need in structural engineering, and provide a deeper understanding of the design and computational modeling of DPJ structures. The results obtained from this project can help enhance the performance, safety and longevity of a class of structures in various areas, including architectures, spacecraft, military equipment and high-tech devices. The project will complement efforts to build the next-generation advanced cyberinfrastructure ecosystem by developing a series of data-driven tools to facilitate numerical and high-performance scientific computing, and expand modeling and simulation capabilities for mechanics of solid and structures. The project will also help upgrade the curriculum on computational modeling of structures. Engineering students will be recruited and mentored in this project. The training for students will include structural design, computational modeling, algorithm development and experimental testing.Traditional structural design is an open-loop protocol, in which a design-modeling-validation procedure is followed. The main objective of this project is to create a data-driven closed-loop platform for optimal design of DPJ structures. This frame-invariant platform is new in providing a closed-loop structural design protocol. In this platform, experimental results will not only be used for model validation, but also in turn serve to provide training and testing data to further improve performance of a data-driven computational model. The loop will then be closed by using the computational model to guide initial structural design. Toward this goal, two tasks will be carried out. The first task is to develop a stochastic approach to form finding. Traditional methods for form finding of DPJ structures require member grouping, which relies highly on the geometric simplicity of the structure. To resolve this issue, a new method, called the stochastic fixed nodal position method, will be designed and investigated. The key benefit of this method is that it does not use member grouping or require any geometric simplicity. These features will allow the method to serve as a powerful tool in design of large-scale, complex, and irregular DPJ structures. The second task is to develop a data-driven computational modeling technique. Constitutive modeling techniques often over-simplify a DPJ structure, which results in the failure to reflect important mechanical properties of the structure. Very few recently developed techniques for computational modeling are suitable to DPJ structures, due to their special characteristics that are not commonly seen in other solids or structures. This project will develop a novel computational modeling technique based on machine learning and non-destructive testing for DPJ structures. This technique will bypass traditional constitutive modeling and provide good performance in handling DPJ structures.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.