A nonlinear dynamic modeling method for primary mirror of Flower-like Deployable Space Telescope(F-DST)undergoing large deployment motion is proposed in this paper.To ensure pointing accuracy and attitude stability of...A nonlinear dynamic modeling method for primary mirror of Flower-like Deployable Space Telescope(F-DST)undergoing large deployment motion is proposed in this paper.To ensure pointing accuracy and attitude stability of the paraboloidal primary mirror,the mirror is discretized into equal thickness shell elements by considering shell curvature.And the material nonlinear constitutive relation of flexible mirror is acquired using Absolute Nodal Coordinate Formulation(ANCF).Furthermore,the primary mirror of F-DST can be regarded as a clustered multi-body system,and its dynamic equations of elastic deformation and deployment motion are established by virtual work principle.Finally,the deployment motion of primary mirror by different driving conditions are simulated,the results show that the vibrations of mirrors that driven by elastic hinge device are more than that driven by servo motor.In addition,single sub-mirror deployment process will perturb the pointing accuracy of primary mirror,and the multiple sub-mirrors simultaneously deploying will seriously affect all the sub-mirrors surface accuracy because of the coupling effect.Thus,there are theoretical value and practical significance for the controlling surface accuracy and attitude accuracy of space telescope.展开更多
Composite Thin-walled Lenticular Tube(CTLT)is increasingly utilized in small satellites missions as a lightweight,foldable,and rollable structural material that facilitates the construction of large deployable systems...Composite Thin-walled Lenticular Tube(CTLT)is increasingly utilized in small satellites missions as a lightweight,foldable,and rollable structural material that facilitates the construction of large deployable systems.The CTLT is initially flattened and coiled around a central hub for storage before launch,during which elastic energy is stored as deformation energy,allowing it to be self-deployed on demand for use in orbit.This work presents a comprehensive investigation into the coiling,storage and deployment behaviors of CTLT that wraps around a central hub.A nonlinear explicit dynamic finite element model was developed with both deformable CTLT and rigidbodies mechanisms including the central hub and guide rollers,as well as the complex interactions among them.The coiling mechanics characteristics such as stored strain energy and rotational moment were presented and validated against experimental data in the literature.Then,the dynamic deployment behaviors were analyzed in terms of two different deployment methods,namely,controlled deployment and free deployment.The effect of material property change during storage was also discussed through numerical experiments.展开更多
The integration of cloud and IoT edge devices is of significance in reducing the latency of IoT stream data processing by moving services closer to the edge-end.In this connection,a key issue is to determine when and ...The integration of cloud and IoT edge devices is of significance in reducing the latency of IoT stream data processing by moving services closer to the edge-end.In this connection,a key issue is to determine when and where services should be deployed.Common service deployment strategies used to be static based on the rules defined at the design time.However,dynamically changing IoT environments bring about unexpected situations such as out-of-range stream fluctuation,where the static service deployment solutions are not efficient.In this paper,we propose a dynamic service deployment mechanism based on the prediction of upcoming stream data.To effectively predict upcoming workloads,we combine the online machine learning methods with an online optimization algorithm for service deployment.A simulation-based evaluation demonstrates that,compared with those state-of-the art approaches,the approach proposed in this paper has a lower latency of stream processing.展开更多
基金based on Project 51575126 the National Natural Science Foundation of ChinaProjects 2013M541358 and 2015T80358 the China Postdoctoral Science Foundation。
文摘A nonlinear dynamic modeling method for primary mirror of Flower-like Deployable Space Telescope(F-DST)undergoing large deployment motion is proposed in this paper.To ensure pointing accuracy and attitude stability of the paraboloidal primary mirror,the mirror is discretized into equal thickness shell elements by considering shell curvature.And the material nonlinear constitutive relation of flexible mirror is acquired using Absolute Nodal Coordinate Formulation(ANCF).Furthermore,the primary mirror of F-DST can be regarded as a clustered multi-body system,and its dynamic equations of elastic deformation and deployment motion are established by virtual work principle.Finally,the deployment motion of primary mirror by different driving conditions are simulated,the results show that the vibrations of mirrors that driven by elastic hinge device are more than that driven by servo motor.In addition,single sub-mirror deployment process will perturb the pointing accuracy of primary mirror,and the multiple sub-mirrors simultaneously deploying will seriously affect all the sub-mirrors surface accuracy because of the coupling effect.Thus,there are theoretical value and practical significance for the controlling surface accuracy and attitude accuracy of space telescope.
基金co-supported by the National Natural Science Foundation of China(No.12202295)the Fundamental Research Funds for the Central Universities,China(No.YJ2021137)+1 种基金the Open Project of State Key Laboratory for Strength and Vibration of Mechanical Structures,Xi’an Jiaotong University,China(No.SV2021-KF-04)the Open Project of State Key Laboratory of Structural Analysis for Industrial Equipment,Dalian University of Technology,China(No.GZ22120)。
文摘Composite Thin-walled Lenticular Tube(CTLT)is increasingly utilized in small satellites missions as a lightweight,foldable,and rollable structural material that facilitates the construction of large deployable systems.The CTLT is initially flattened and coiled around a central hub for storage before launch,during which elastic energy is stored as deformation energy,allowing it to be self-deployed on demand for use in orbit.This work presents a comprehensive investigation into the coiling,storage and deployment behaviors of CTLT that wraps around a central hub.A nonlinear explicit dynamic finite element model was developed with both deformable CTLT and rigidbodies mechanisms including the central hub and guide rollers,as well as the complex interactions among them.The coiling mechanics characteristics such as stored strain energy and rotational moment were presented and validated against experimental data in the literature.Then,the dynamic deployment behaviors were analyzed in terms of two different deployment methods,namely,controlled deployment and free deployment.The effect of material property change during storage was also discussed through numerical experiments.
基金supported by the General Program of National Natural Science Fouddation of China:Analytical Method Reserach of Loop and Recursion(No.61872262/F020106)the Key Project of the National Natural Science Foundation of China:Research on Big Service Theory and Methods in Big Data Environment(No.61832004).
文摘The integration of cloud and IoT edge devices is of significance in reducing the latency of IoT stream data processing by moving services closer to the edge-end.In this connection,a key issue is to determine when and where services should be deployed.Common service deployment strategies used to be static based on the rules defined at the design time.However,dynamically changing IoT environments bring about unexpected situations such as out-of-range stream fluctuation,where the static service deployment solutions are not efficient.In this paper,we propose a dynamic service deployment mechanism based on the prediction of upcoming stream data.To effectively predict upcoming workloads,we combine the online machine learning methods with an online optimization algorithm for service deployment.A simulation-based evaluation demonstrates that,compared with those state-of-the art approaches,the approach proposed in this paper has a lower latency of stream processing.