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基于BART的泡沫铝填充U型梁防护结构抗爆多目标优化设计

MULTI-OBJECTIVE OPTIMAL DESIGN OF ANTI-EXPLOSION PROTECTION STRUCTURE FOR BART-BASED ALUMINUM FOAM-FILLED U-SHAPED BEAMS

  • 摘要: 面向防护型车辆在战场环境下面临的底部爆炸冲击威胁, 本文提出一种泡沫铝填充U型加强梁复合防护组件构型, 该构型以纵横梁及U型梁构成承载骨架, 并将泡沫铝布置在U型梁空腔内, 通过芯材的压溃吸能, 从而提高车辆底部结构的抗爆承载与吸能能力. 通过建立土壤-空气-炸药-结构耦合有限元模型, 并开展爆炸试验对有限元建模方法进行对标验证, 保证后续仿真分析的可靠性. 在此基础上, 采用最优拉丁超立方采样方法获取初始样本数据, 结合Spearman相关性分析和Sobol全局敏感性分析, 识别影响结构响应的关键设计变量, 实现设计参数筛选与降维. 构建了贝叶斯加性回归树(Bayesian additive regression trees, BART)代理模型, 并与克里金模型进行预测精度对比, 分析其对复杂非线性爆炸响应的拟合能力. 进一步结合q-期望超体积改进(q-expected hypervolume improvement, qEHVI)多目标贝叶斯优化策略, 经过迭代在有限的高保真仿真次数下获得Pareto最优解集, 选取其中一组折中优化方案进行仿真验证, 并与优化前结构进行对比. 结果表明, 优化后结构基板中心最大位移降低约60%, 基板残余塑性变形降低约15%, 基板最大动能降低约20%.

     

    Abstract: Protected vehicles remain vulnerable to near-field underbody blasts, which can cause severe floor deformation and transmit high-amplitude transient loads. Under constraints on installation space and structural mass, a protective component must combine load distribution, structural support, and energy dissipation. This study proposes a composite protective component incorporating aluminum-foam-filled U-shaped stiffeners. Longitudinal, transverse, and U-shaped beams form a load-bearing framework that spreads localized blast loads, while aluminum foam inside the U-shaped cavities dissipates energy through progressive crushing. The configuration therefore integrates load-bearing, load-transfer, and energy-absorption functions without requiring a separate continuous sandwich core. A coupled soil-air-explosive-structure finite element model was established to evaluate the blast response. Blast tests were conducted to validate the model using the residual plastic deformation of the base plate; the difference between the experimental and numerical results was approximately 2%. The initial component was also compared with an equal-mass steel plate under identical loading and boundary conditions. Optimal Latin hypercube sampling generated samples across a 17-dimensional design space. Spearman correlation and Sobol global sensitivity analyses identified the dominant variables and reduced the design space to nine variables. A Bayesian additive regression trees (BART) surrogate model was then constructed and benchmarked against a Kriging model. The BART model achieved higher overall prediction accuracy for the nonlinear structural responses considered. A q-expected hypervolume improvement (qEHVI)-based multi-objective Bayesian optimization framework was subsequently developed. The maximum central displacement and maximum kinetic energy of the base plate, as well as the structural mass, were minimized, while structural mass was constrained not to exceed that of the initial design. Starting from 60 high-fidelity samples, ten batch iterations with four candidates per iteration produced a dataset of 100 simulations and an approximation of the Pareto-optimal solution set. A representative compromise solution was selected and re-evaluated using the high-fidelity model. Compared with the initial design, the optimized component reduced the maximum central displacement, residual plastic deformation, and maximum kinetic energy of the base plate by approximately 60%, 15%, and 20%, respectively. These results indicate that the proposed component and optimization framework can improve underbody blast protection under a prescribed mass constraint, providing a reference for the design and multi-objective optimization of vehicle underbody protective structures.

     

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