ZrB2-SiC 基热防护系统的机器学习驱动应力分布分析

Aug 5, 2026

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Data-to-knowledge has accelerated the shift to a machine learning and big-data paradigm for solving complex thermal-structural problems. This study evaluates the non-discrete stress distribution behavior of a ZrB2/SiC composite thermal protection system using a machine-learning driven workflow. Thermo-fluid FEM under re-entry conditions predicts a maximum external temperature of about 5000 K and boundary-layer pressure up to 205 MPa. Clustering reveals high thermal stress near boundary-layer separation points with oxygen ionization, yielding peak stress around 400 MPa and improved simulation efficiency and accuracy through deep learning.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Abstract

Keywords

Introduction

Background and challenges in hypersonic TPS evaluation

Role of CFD/FEM and limitations

Data-to-knowledge and ML as an alternative

Purpose and scope

Stress distribution goal for a TPS-coated semi-cone leading edge

Crimson Publishers Wings to the Research Research Article ISSN: 2576-8840 *Corresponding author: Carmine Zuccarini, Department of Aerospace and Aircraft Engineering, Kingston University, Roehampton Vale Campus, London, United Kingdom Submission: July 26, 2024 Published: August 30, 2024 Volume 20-Issue 5 How to cite this article: Carmine Zuccarini*, Karthikeyan Ramachandranand Doni Daniel Jayaseelan. Stress Distribution Analysis in ZrB2-SiC Based Thermal Protection System Using Machine Learning Driven Approach. Res Dev Material Sci. 20(5). RDMS. 000999. 2024. DOI: 10.31031/RDMS.2024.20.000999 Copyright@ Carmine Zuccarini, This article is distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use and redistribution provided that the original author and source are credited. Stress Distribution Analysis in ZrB2-SiC Based Thermal Protection System Using Machine Learning Driven Approach Carmine Zuccarini*, Karthikeyan Ramachandran and Doni Daniel Jayaseelan Department of Aerospace and Aircraft Engineering, Kingston University, Roehampton Vale Campus, London, United Kingdom Abstract Data-to-knowledge has significantly increased over the last decades, and this led to a new paradigm of science where Machine Learning (ML) and big data are utilised to optimize and solve complex problems. This study is the first of its kind to evaluate a non-discrete stress distribution behaviour of ZrB2/SiC composite based thermal protection system (TPS) using ML. Thermo-fluid analysis using FEM at re-entry conditions indicated maximum external temperature of 5000 K with boundary layer pressure up to 205 MPa. The ML driven approach through Keras library with combination of clustering indicated that high thermal stress occurs within approximately on three quarters of boundary layer separation points where flow is highly stochastic, and oxygen ionization takes place. A maximum stress of ~400 MPa obtained from stress distribution supports the ablative behaviour of TPS. The use of deep learning has significantly reduced the computational time needed for simulation and showed improved accuracy. Keywords: Thermal protection system; Hypersonic conditions; machine learning; ZrB2-SiC composites, Keras library Introduction Established methods and extensive previous research have proved that it could be possible to fly outside the earth’s atmosphere and re-enter reaching speeds that could go above the speed of sound by 5 folds [1] . However, sharp leading edges, combustors and thermal protection systems of such hypersonic vehicles require materials that can withstand extreme thermal, mechanical and shock-wave loadings. Ultra-High Temperature Ceramics (UHTCs) are potential candidates that can be utilised as non-ablative TPS for a space vehicle due to their low volatility, high melting point and high heat-flux resistance [1-4] . The current trend in barrier coatings utilised by designers and engineers involves randomised choice of material combinations based upon historical experiments and extensive testing to ensure safety and vehicle manoeuvrability [5]. However, experimental preparation of UHTCs and the testing for harsh environmental conditions require hypersonic wind tunnel facility and the availability of such facilities is scarce. Further, using such exotic techniques, materials can only get evaluated for limited time leading to lack of information over time. Computer-based simulation, based upon the Finite Element Modelling (FEM) and Computational Fluid Dynamics (CFD) have been utilised to estimate the rate of change of fluid field across the vehicle to determine safety parameters, such as heat flux and shock wave resistance, which could be provided by understanding structural integrity and thermal efficiency [6]. However, computer-based simulations do not bear any environmental and physical properties of wind tunnel in terms of accuracy and requires high pe

FAQs

这项研究要分析什么问题？

研究面向ZrB2/SiC复合材料热防护系统，评估其在再入工况下的非离散应力分布行为，并探讨机器学习模型的作用与效率。

论文采用了哪些分析与建模方法？

研究使用在再入条件下的热流体有限元分析（FEM）提供边界层与温度/压力信息，并通过Keras实现的机器学习流程（含聚类组合）来刻画应力分布。

结果表明应力峰值与哪些流动特征相关？

聚类结果显示，高热应力主要出现在边界层分离点附近约四分之三区域，且与流动强随机性以及发生氧离子电离的情形相关；应力分布给出峰值约400 MPa。

Aug 5, 2026

Upload by Miles