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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2017, Vol. 38 ›› Issue (S1): 721512-721512.doi: 10.7527/S1000-6893.2017.721512

• Material Engineering and Machine Manufacturing • Previous Articles     Next Articles

Prediction of meso-heat transfer characteristics of resin-based ablative materials

GAO Junjie, YU Jijun, HAN Haitao, DENG Daiying   

  1. China Academy of Aerospace Aerodynamics, Beijing 100074, China
  • Received:2017-05-25 Revised:2017-07-07 Online:2017-11-30 Published:2017-07-07
  • Supported by:

    National Natural Science Foundation of China (11372297,11402252)

Abstract:

Low-density resin-based ablative materials have been widely used in spacecraft for deep space exploration in recent years. To improve their thermal insulation capacity, we need to conduct an analysis of the mechanism of heat transfer, especially meso-heat transfer, of the material. Based on microscopic observation and statistical analysis of the material, a theoretical model for the unit cells of different scales of the material was established. The calculated results were compared with the experimental values, and the influence of the parameters was given. A finite element stochastic model was also established. The calculated results were compared with the experimental values, and the influence of parameterization based on finite element was given. The results show that the error between the theoretical model and the experimental value is 12% and 7.6% for the two resin-based ablation materials respectively, and the correctness of the theoretical model is thus verified. The difference between the thermal conductivity values obtained with the stochastic model and the experimental values is less than 10%, verifying the correctness of the stochastic model. The model proposed and parameter analysis is of great significance for improvement of the thermal insulation performance and processing technology of the resin-based ablative material.

Key words: resin-based, ablative material, heat conductivity coefficient, mesoscopic observation, unit cell model, finite element, random model

CLC Number: