Digital twin for the structural health management of reusable spacecraft: A case study. (July 2020)

• Record Type: Journal Article
• Authors: Ye, Yumei; Yang, Qiang; Yang, Fan; Huo, Yanyan; Meng, Songhe
• Is Part Of: Engineering fracture mechanics. Volume 234(2020)
• Publication Date: 2020-07
• Subjects: Digital twin -- Reusable spacecraft -- Fatigue crack growth -- Dynamic Bayesian network -- Manufactured solutions; Fracture mechanics -- Periodicals; Rupture, Mécanique de la -- Périodiques; Fracture mechanics; Periodicals; 620.112605
• DOI: 10.1016/j.engfracmech.2020.107076
• Languages: English
• ISSNs: 0013-7944

Abstract

Highlights: A digital twin framework is proposed for the health management of a spacecraft. The framework could improve the prognosis and decision support capabilities. Accumulating inspection data could reduce prognosis uncertainty. Information entropy analysis reveals the reasons for better predictions. Abstract: Reusable spacecraft can significantly reduce the cost of space travel, while evaluation of the structural health of the craft between flights becomes one of the key issues. A digital twin framework is proposed in this paper to track the life of spacecraft structures. A digital twin is a digital representation of an engineering system. It can simulate, monitor, diagnose, predict states and optimize operations of the real engineering system in real time. The proposed framework can be divided into offline and online stages. It has the functions of diagnosis, model updating, performance evaluation and data storage. To demonstrate the prognosis and decision support capabilities of the framework, a numerical example considering fatigue crack growth in a load-bearing frame is carried out. The method of manufactured solutions is employed for validation. Information entropy and relative entropy are used for measuring the uncertainties in crack length prediction. The results show that through the framework, crack growth model can be updated to have a lower uncertainty. Future crack growth and reusable life can be predicted more accurately using the improved model. With the structural life of the spacecraft quantified by the framework, mission success rates for repeated flights can be maximized at a lower cost.