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Probabilistic Design Method for Aircraft Thermal Protective Layers Based on Surrogate Models

Author

Listed:
  • Zhongcan Chen

    (China Academy of Launch Vehicle Technology, Beijing 100076, China
    School of Aerospace Engineering, Tsinghua University, Beijing 100084, China)

  • Kai Zhang

    (China Academy of Launch Vehicle Technology, Beijing 100076, China)

  • Shanshan Zhao

    (China Academy of Launch Vehicle Technology, Beijing 100076, China)

  • Feng Li

    (China Academy of Launch Vehicle Technology, Beijing 100076, China)

  • Fengtao Xu

    (China Academy of Launch Vehicle Technology, Beijing 100076, China)

  • Min Chen

    (School of Aerospace Engineering, Tsinghua University, Beijing 100084, China)

Abstract

In this study, a probabilistic method was proposed for an aircraft’s thermal protective layers. The uncertainties of material properties, geometric dimensions, and incoming flow environments were considered for the design inputs. To accelerate the design efficiency, Latin hypercube sampling and surrogate models were built based on finite element method calculations to enhance the simulation efficiency. Thus, the Monte Carlo method can be implemented with such a fast simulation method and produce a massive number of samples for the uncertainty quantification and sensitivity analysis, exploring their impact on the back temperature of the thermal protection layer. Compared to the deterministic method with the extreme deviation design, the probabilistic design yields a weight reduction of 15.61%. This indicates that probabilistic design is an efficient approach to enhance the performance of aircraft and reduce the overall weight of the aircraft. The general goal of this study is to provide a new design method for the coating film of thermal protection systems by considering multiple sources of uncertainties.

Suggested Citation

  • Zhongcan Chen & Kai Zhang & Shanshan Zhao & Feng Li & Fengtao Xu & Min Chen, 2024. "Probabilistic Design Method for Aircraft Thermal Protective Layers Based on Surrogate Models," Energies, MDPI, vol. 17(5), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:1051-:d:1344309
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