Geometric design and optimization of scissor-type deployable structures. (15th April 2023)

• Record Type: Journal Article
• Authors: Li, Yaxin; Krishnan, Sudarshan
• Is Part Of: Journal of building engineering. Volume 65(2023)
• Publication Date: 2023-04-15
• Subjects: Geometric design -- Deployable structures -- Domes -- Structural behavior -- Optimization -- Genetic algorithm; Building -- Periodicals; 690.05
• DOI: 10.1016/j.jobe.2022.105724
• Languages: English
• ISSNs: 2352-7102

Abstract

Abstract: Geometric design is the essential first step to ensure transformation of a structure from an undeployed to a deployed state and vice-versa. Typically, this is achieved using motion criteria to prevent interference of parts. This paper describes the geometric design procedure of axisymmetric deployable structures made of planar angulated scissor units. The geometric design parameters are optimized not just using motion criteria but also structural performance measures. The first part of this paper discusses the refinement of geometric parameters, namely, shape equation, number of vertical layers, number of sectors in plan, and rise-to-span ratio, to ensure minimum change in span during deployment. Analysis results showed that more number of vertical layers, more sectors, and a lower rise-to-span ratio led to minimal changes in base radius during deployment. A finite element analysis of a simplified slice model was used to validate the results by comparing with the full three-dimensional model results. Genetic Algorithm was used to find the optimum number of vertical layers and sectors in plan based on structural performance. This paper presents results of concurrent optimization of layout, elevation, and member sizes of deployable domes to meet stiffness criteria and to reduce self-weight. Highlights: Structural behavior along with motion criteria are used for geometric design. Planar model is developed for efficient parametric analysis under balanced loads. Geometric and structural parameters are optimized using Genetic Algorithm. Insights given on structural behavior in intermediate and fully deployed states. Selection of design parameters is based on motion and stiffness criteria.