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A Parametric Physics-Informed Deep Learning Method for Probabilistic Design of Thermal Protection Systems

Author

Listed:
  • Runlin Zhang

    (School of Mathematics and Science, North China Electric Power University, Beijing 102206, China)

  • Nuo Xu

    (School of Mathematics and Science, North China Electric Power University, Beijing 102206, China)

  • Kai Zhang

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

  • Lei Wang

    (School of Mathematics and Science, North China Electric Power University, Beijing 102206, China)

  • Gui Lu

    (School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

Precise and efficient calculations are necessary to accurately assess the effects of thermal protection system (TPS) uncertainties on aerospacecrafts. This paper presents a probabilistic design methodology for TPSs based on physics-informed neural networks (PINNs) with parametric uncertainty. A typical thermal coating system is used to investigate the impact of uncertainty on the thermal properties of insulation materials and to evaluate the resulting temperature distribution. A sensitivity analysis is conducted to identify the influence of the parameters on the thermal response. The results show that PINNs can produce quick and accurate predictions of the temperature of insulation materials. The accuracy of the PINN model is comparable to that of a response surface surrogate model. Still, the computational time required by the PINN model is only a fraction of the latter. Considering both computational efficiency and accuracy, the PINN model can be used as a high-precision surrogate model to guide the TPS design effectively.

Suggested Citation

  • Runlin Zhang & Nuo Xu & Kai Zhang & Lei Wang & Gui Lu, 2023. "A Parametric Physics-Informed Deep Learning Method for Probabilistic Design of Thermal Protection Systems," Energies, MDPI, vol. 16(9), pages 1-20, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3820-:d:1136412
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    References listed on IDEAS

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    1. Abdulwahab Alqahtani & Xupeng He & Bicheng Yan & Hussein Hoteit, 2023. "Uncertainty Analysis of CO 2 Storage in Deep Saline Aquifers Using Machine Learning and Bayesian Optimization," Energies, MDPI, vol. 16(4), pages 1-16, February.
    2. M.-Elisabeth Paté-Cornell & Paul S. Fischbeck, 1994. "Risk Management for the Tiles of the Space Shuttle," Interfaces, INFORMS, vol. 24(1), pages 64-86, February.
    3. Justin Sirignano & Konstantinos Spiliopoulos, 2017. "DGM: A deep learning algorithm for solving partial differential equations," Papers 1708.07469, arXiv.org, revised Sep 2018.
    4. Yiwei Dong & Ertai Wang & Yancheng You & Chunping Yin & Zongpu Wu, 2019. "Thermal Protection System and Thermal Management for Combined-Cycle Engine: Review and Prospects," Energies, MDPI, vol. 12(2), pages 1-51, January.
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    Cited by:

    1. Liangcai Zhou & Long Huo & Linlin Liu & Hao Xu & Rui Chen & Xin Chen, 2025. "Optimal Power Flow for High Spatial and Temporal Resolution Power Systems with High Renewable Energy Penetration Using Multi-Agent Deep Reinforcement Learning," Energies, MDPI, vol. 18(7), pages 1-14, April.

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