Volume 42 Issue 2
Mar.  2022
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LIU Jinghong, XU Jin, DU Jianli, PAN Jianping. Application of Atmospheric Drag Model Based on Reynolds Number in Reentry Prediction of Rocket Bodies (in Chinese). Chinese Journal of Space Science, 2022, 42(2): 277-283. DOI: 10.11728/cjss2022.02.210222020
Citation: LIU Jinghong, XU Jin, DU Jianli, PAN Jianping. Application of Atmospheric Drag Model Based on Reynolds Number in Reentry Prediction of Rocket Bodies (in Chinese). Chinese Journal of Space Science, 2022, 42(2): 277-283. DOI: 10.11728/cjss2022.02.210222020

Application of Atmospheric Drag Model Based on Reynolds Number in Reentry Prediction of Rocket Bodies

doi: 10.11728/cjss2022.02.210222020 cstr: 32142.14.cjss2022.02.210222020
  • Received Date: 2021-02-22
  • Accepted Date: 2021-10-08
  • Rev Recd Date: 2021-10-08
  • Available Online: 2022-05-25
  • With the increasing demand for space technology services and the realization of active space debris removal technology, space debris will frequently re-enter the atmosphere in the future with the characteristics of large quantity, high quality and difficult decomposition, causing more threats to the safety of people and property. Therefore, there is an urgent need for early warning of atmospheric reentry of large spacecraft such as rocket body. However, due to the lack of appropriate atmospheric drag coefficient model, it is difficult to achieve high-precision atmospheric reentry prediction. Therefore, an atmospheric dynamic model based on Reynolds number is introduced for simplified spacecraft model, and the prediction results are obtained by numerical integration of the differential equation of motion through RK6(7), which are compared with the prediction results of high-precision numerical orbital propagator HPOP and semi analytical orbital propagator WHU-SST. The experimental results show that using the differential equation of motion with the atmospheric dynamic model based on Reynolds number to predict the reentry of the rocket body 30 days in advance, the accuracy of prediction is significantly improved, and the prediction error of an object is reduced from 96% to 7.8%; Using the new model for ground risk assessment with TLE data only, the real decayed location can be located in the predicted statistical decayed location.

     

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