NSF Award
2104237
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.<br/><br/>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.<br/><br/>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.
