Simulation and prediction method of satellite solar wing deployment test driven by digital twin

doi:10.11996/JG.j.2095-302X.2025020449

Abstract

Abstract:

The success of on-line deployment of satellite solar wings is a critical factor influencing their operational performance, and the ground deployment test of satellite solar wings serves as a crucial development step to verify the deployment and locking performance of the mechanism and ensure the compliance of deployment indicators. To address the challenges such as limited data monitoring, low simulation accuracy, and difficulties in predicting outcomes in the process of satellite solar wing ground deployment testing, a digital twin-driven simulation and prediction method along with a system architecture for satellite solar wing ground deployment testing was proposed. Based on the digital twin modeling of typical satellite solar wing products, related tooling and equipment for ground deployment testing, and deployment testing units, a multidisciplinary joint simulation for satellite solar wing ground deployment testing was conducted, and a simulation database was output. A prediction model training dataset was constructed by combining the simulation data and historical test data. Subsequently, the training and optimization of the satellite solar wing deployment process prediction model were completed. Through the real-time collection of key parameters in the ground deployment testing process via the Internet of Things platform, inputting these parameters into the optimized key parameter prediction model, the rapid and accurate prediction of key parameters such as pose, force, velocity, and deployment time in the satellite solar wing ground deployment testing process were achieved. Finally, based on the digital twin model, by integrating the actually collected and predicted data, the real-time monitoring of the mechanism deployment process and the visualization of prediction results were realized. This approach supported the intelligent management and control and decision-making in the satellite solar wing ground deployment testing process, guided the optimization of the deployment process and on-site adjustments, and effectively enhanced the deployment success rate and efficiency. Through the deployment in the satellite deployment unit and the application verification on typical satellite models, the proposed method achieved an online prediction accuracy rate of over 90% for key parameters such as the deployment time and pose of typical solar wings, verifying its effectiveness and feasibility.

Key words: digital twin, deployment test, multidisciplinary simulation, deep learning, process prediction

CLC Number:

TP391
V416

CHEN Ruiqi, LIU Xiaofei, WAN Feng, HOU Peng, SHEN Jinyi. Simulation and prediction method of satellite solar wing deployment test driven by digital twin[J]. Journal of Graphics, 2025, 46(2): 449-458.

Figures/Tables 19

Fig. 1 Architecture of testing, simulation, and prediction system for satellite solar wing deployment driven by digital twins

Fig. 2 Structure of structure twin model

Fig. 3 Geometric models and data models of solar wings (basic data)

Fig. 4 Hierarchical development of testing unit twin modeling

Fig. 5 Process flowchart of multidisciplinary joint simulation for institutional deployment

Table 1 Input and output parameter table for simulating the ground deployment test process of solar wings

输入参数			输出参数
参数	单位	范围	参数	单位
星体姿态(X, Y, Z)	mm	0~100	第一(根部)铰链角度	°
星体姿态(RX, RY, RZ)	°	0~5	第二(板间)铰链角度	°
水平度	mm	0~1	关键点位移(X, Y, Z)	mm
平行度	mm	0~1	关键点位移(X, Y, Z)	mm
拼缝高低差	mm	0~0.5	关键点速度(X, Y, Z)	mm/s
摩擦系数		0.001~0.010	关键点速度(X, Y, Z)	mm/s
卸载力	%	50~100	关键点加速度(X, Y, Z)	mm/s²
支撑高度	mm	100~300	关键点加速度(X, Y, Z)	mm/s²
风阻	N	0~1	展开时间	s
……

Fig. 6 Online prediction of solar wing deployment process

Table 2 Structure of institution deployment process prediction dataset

仿真数据			实测数据
名称	参数	单位	名称	参数	单位
第一铰链角度	HINGE_FIRST_SANGLE	°	第i点位移X,Y,Z轴分量	RPi_X_S, RPi_Y_S, RPi_Z_S	mm
第二铰链角度	HINGE_SECOND_SANGLE	°	第i点速度X,Y,Z轴分量	RPi_X_S, RPi_Y_S, RPi_Z_S	mm/s
第i点位移X,Y,Z轴分量	SPi_X_S, SPi_Y_S, SPi_Z_S	mm	第i点加速度X,Y,Z轴分量	RPi_X_S, RPi_Y_S, RPi_Z_S	mm/s²
第i点速度X,Y,Z轴分量	SPi_X_S, SPi_Y_S, SPi_Z_S	mm/s	实测展开时间	RDEPLOYMENT_TIME	s
第i点加速度X,Y,Z轴分量	SPi_X_S, SPi_Y_S, SPi_Z_S	mm/s²	……
仿真展开时间	SDEPLOYMENT_TIME	s
……

Fig. 7 Expand time feature analysis correlation heatmap

Fig. 8 Real time monitoring and display of institutional deployment process

Fig. 9 Satellite solar wing deployment test unit digital twin model ((a) Solar wing twin model; (b) Twin model of tooling; (c) Workshop environment modeling)

Fig. 10 Multi disciplinary joint simulation results of satellite solar wing deployment test

Fig. 11 Preprocessing of input parameters for predicting the expansion process of solar wings ((a) Expand time feature analysis; (b) Analysis of the characteristics of the first hinge (root); (c) Characteristic analysis of the second hinge (between plates))

Fig. 12 Comparison of errors in solar wing deployment time prediction models ((a) LSTM neural network (prediction error 2.076 7); (b) BP neural network (prediction error 1.230 9); (c) RBF neural network (prediction error 1.641 7))

Table 3 Key parameter boundary test parameters table for solar wing deployment

序号	变化参数名称	数值	展开锁定情况	备注
1	星体姿态	水平0.5 mm，俯仰0.5 mm	告警异常	采用控制变量法，除变化参数外均为最优试验参数
2	气浮平台姿态	平面度0.6 mm，水平度0.6 mm	告警异常
3	气压、气膜厚度(气浮摩擦阻力)	气压0.5 MPa，气膜厚度0.04 mm	告警异常
4	卸载力	各点均70%	告警异常

Fig. 13 Confirmation of the attitude boundary of the celestial body in the solar wing deployment test

Fig. 14 Confirmation of attitude boundary of the air floating platform body in the solar wing deployment test

Fig. 15 Confirmation of the boundary of air flotation friction force in the solar wing deployment test

Fig. 16 Confirmation of the gravity unloading accuracy boundary for solar wing unfolding test

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