数字孪生驱动的卫星太阳翼展开测试仿真与预测方法

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

摘要/Abstract

摘要：

卫星太阳翼在线展开成功与否是影响其服役性能的关键影响因素，而卫星太阳翼地面展开测试是验证机构展开锁定性能和展开指标符合性的关键研制步骤。为解决卫星太阳翼地面展开测试过程数据监控少、仿真精度低、结果难预测的难题，提出了一种数字孪生驱动的卫星太阳翼地面展开测试仿真与预测方法和体系架构。在典型卫星太阳翼产品、展开地面测试相关工装设备和展开测试单元数字孪生建模的基础上，开展面向卫星太阳翼地面展开测试的多学科联合仿真并输出仿真数据库，结合仿真数据和历史测试数据构建预测模型训练数据集，在此基础上完成卫星太阳翼展开过程预测模型的训练与优化。通过物联网平台实时采集地面展开测试过程关键参数，输入优化后的关键参数预测模型中，实现卫星太阳翼地面展开测试过程中位姿、受力、速度和展开时间等关键参数的快速、准确预测。最终以孪生模型为基础，融合实采、预测数据，实现机构展开过程实时监控和预测结果可视化，支持卫星太阳翼地面展开测试过程智能管控和决策，指导展开工艺优化和现场调整，有效提高展开成功率和效率。通过在卫星展开单元的部署并在典型卫星型号上开展应用验证，典型太阳翼展开时间、位姿等关键参数在线预测准确率大于90%，验证了该方法的有效性和可行性。

关键词: 数字孪生, 展开测试, 多学科仿真, 深度学习, 过程预测

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

中图分类号:

TP391
V416

陈瑞启, 刘晓飞, 万峰, 侯鹏, 沈金屹. 数字孪生驱动的卫星太阳翼展开测试仿真与预测方法[J]. 图学学报, 2025, 46(2): 449-458.

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.

图/表 19

图1 数字孪生驱动的卫星太阳翼展开测试仿真与预测体系架构

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

图2 太阳翼孪生模型结构

Fig. 2 Structure of structure twin model

图3 太阳翼几何模型与数据模型(基本数据)

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

图4 分层次展开测试单元孪生建模

Fig. 4 Hierarchical development of testing unit twin modeling

图5 机构展开过程多学科联合仿真流程图

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

表1 太阳翼地面展开测试过程仿真输入输出参数表

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
……

图6 太阳翼展开过程在线预测

Fig. 6 Online prediction of solar wing deployment process

表2 机构展开过程预测数据集结构

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
……

图7 展开时间特征分析相关性热力图

Fig. 7 Expand time feature analysis correlation heatmap

图8 机构展开过程实时监控与展示

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

图9 卫星太阳翼展开测试单元数字孪生模型((a)太阳翼孪生模型；(b)工装孪生模型；(c)车间环境建模)

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)

图10 卫星太阳翼展开测试多学科联合仿真结果

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

图11 太阳翼展开过程预测输入参数预处理((a)展开时间特征分析；(b)第一铰链(根部)特征分析；(c)第二铰链(板间)特征分析)

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))

图12 太阳翼展开时间预测误差对比((a) LSTM神经网络(预测误差2.076 7)；(b) BP神经网络(预测误差1.230 9)；(c) RBF神经网络(预测误差1.641 7))

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))

表3 太阳翼展开关键参数边界试验参数表

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

图13 太阳翼展开试验星体姿态边界确认

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

图14 太阳翼展开试气浮平台姿态边界确认

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

图15 太阳翼展开试验气浮摩擦力边界确认

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

图16 太阳翼展开试验重力卸载精度边界确认

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

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