Hierarchical Model Updating Method for Vector Electric-Propulsion Satellites

Wu, Xueqian; Dong, Yunfeng

doi:10.3390/app13084980

Cited by 2 publications

(2 citation statements)

References 20 publications

(20 reference statements)

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“…In contrast, principle modeling refers to build models through the mathematical analysis of the operating principles of the system [15,16]. Computational resource requirements are high, but high-fidelity simulations of modeling can be accomplished [17]. In 1994, Hughes and Loral Company in the United States(US) pioneered the use of dynamic simulation technology in satellite engineering by dividing subsystems into the structure, power, thermal control, Telemetry Tracking and Command(TTC), propulsion, attitude control, and payload.…”

Section: Introductionmentioning

confidence: 99%

Multi-Granularity Modeling Method for Effectiveness Evaluation of Remote Sensing Satellites

Lei,

Dong

2023

Remote Sensing

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The effectiveness indicator system of remote sensing satellites includes various satellites capabilities. Effectiveness evaluation is the process of calculating these indicators in the digital world, involving many different physical parameters of multiple subsystems. Model-based simulation statistics method is the mainstream approach of effectiveness evaluation, and digital twin is currently the most advanced modeling method for simulation. The satellite digital twin model has the characteristics of multi-dynamic, multi-spatial scale and multi-physics field coupling, which gives rise to challenges related to the stiff problem of ordinary differential equations and multi-scale problem of partial differential equations to the calculation process of indicators. It is difficult to solve these problems by breakthroughs in numerical solution methods. This paper uses the sparsity of the satellite system to group each indicator of the effectiveness evaluation indicator system according to the change period. The satellite system model is decomposed into multiple modules according to the composition and structure, and a series of models with different simulation fidelity are established for each module. The optimization schemes for selecting model granularity when calculating indicators by group is given. Simulation results show that this approach considers the coupling between systems, grasps the main contradiction of indicator calculation and overcomes the loss of indicator accuracy caused by the separate calculation of each subsystem under the neglect of coupling in the traditional method. Additionally, it avoids the difficulty in numerical calculation caused by coupling, while simultaneously balancing the accuracy and efficiency of the model simulations.

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Section: Introductionmentioning

confidence: 99%

Multi-Granularity Modeling Method for Effectiveness Evaluation of Remote Sensing Satellites

Lei,

Dong

2023

Remote Sensing

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“…Satellite subsystems are multi-dimensional, multi-physical, and multi-coupled, and their variables are inter-correlated at different degrees. When updating the digital satellite model in the DT of a satellite, adjusting all satellite state variables at once will most probably lead to a divergent solution [13]. Therefore, in order to separately select adjust the state variables according to their degree of importance to the telemetry parameters, it is crucial to sort out and store the relationships between them beforehand.…”

Section: Introductionmentioning

confidence: 99%

Parameter Selection for Digital Satellite Model Updating with Knowledge Graph

Liew,

Dong

2023

Advances in Transdisciplinary Engineering

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Digital twin technology has played an indispensable role in the recent satellite life cycle, specifically in design, testing, and operation monitoring. A digital twin is created according to a physical satellite and can mirror its performance and parameters. The physical satellite is occasionally affected by dynamic disturbances when in orbit, causing inconsistencies with its digital twin, thus requiring the digital twin model to be continuously updated through model parameter adjustments. Nonetheless, the digital twin model involves numerous parameters with coupled relationships; adjusting all simultaneously might be impractical as it requires high computational and time. Since only some of the model parameters have a significant impact on the digital model’s outputs, we propose a parameter selection method that utilizes knowledge graph technology for digital satellite model updating. Firstly, the satellite state variables and their related telemetry parameters of satellite subsystems in the digital satellite model are defined. Secondly, a knowledge graph consisting of the correlation and sensitivity relationships of the state variables and telemetry parameters is built. Thirdly, a clustering algorithm and graph reasoning algorithms are employed on this knowledge graph to select and group the state variables and telemetry parameters required in model updating. The results show that by updating the state variables and telemetry parameters selected from the knowledge graph, not only can the state variables converge to optimal values in fewer iterations, but a more accurate digital satellite model can also be obtained.

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Hierarchical Model Updating Method for Vector Electric-Propulsion Satellites

Cited by 2 publications

References 20 publications

Multi-Granularity Modeling Method for Effectiveness Evaluation of Remote Sensing Satellites

Multi-Granularity Modeling Method for Effectiveness Evaluation of Remote Sensing Satellites

Parameter Selection for Digital Satellite Model Updating with Knowledge Graph

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