【摘要】 针对火箭贮箱搅拌摩擦焊焊缝区域多裂纹萌生与扩展融合问题,基于有限元联合仿真技术与混合不确定性分析理论,提出了一种虑及多源不确定性的贮箱结构多裂纹扩展融合分析方法.通过采用有限元软件对贮箱结构进行有限元分析并模拟贮箱底部焊缝区域共线多裂纹的扩展融合过程,系统分析了多裂纹动态演化行为及其应力强度因子变化规律,并揭示了多裂纹敏感参数对焊缝疲劳寿命的影响机制.在此基础上,考虑多源不确定性对多裂纹扩展寿命的影响,构建了随机-区间混合分析模型,其中材料参数的不确定性用随机变量表征,多裂纹尺寸参数的不确定性用区间变量进行表征,此时输入混合不确定性对于扩展寿命的影响可以通过响应的区间边界以及区间边界的随机特征进行描述.为进一步提高不确定性分析效率,通过训练BP(back propagation)神经网络模型以代替耗时的有限元仿真模型,实现了多源不确定因素影响下共线多裂纹扩展寿命的高效预测.最终,通过贮箱焊接结构共线多裂纹融合扩展的工程算例验证了所提方法的有效性.更多还原

【Abstract】 Multi-crack initiation and coalescence phenomena in friction stir welding zones of rocket fuel tanks constitute a critical threat to structural integrity, demanding precise analytical approaches. To address this challenge, the present investigation develops a novel methodology for multi-crack propagation and coalescence analysis in tank structures, integrating finite element simulation with hybrid uncertainty quantification theory while incorporating multisource uncertainties. The proposed framework employs commercial finite element analysis software to construct a highfidelity tank model, simulating the complete propagation and coalescence process of collinear multi-cracks within bottom weld zones. This simulation systematically elucidates dynamic crack evolution behaviour, quantifies stress intensity factor variation patterns, and reveals underlying mechanisms through which crack-sensitive parameters govern weld fatigue life. Given the significant impact of multi-source uncertainties on propagation life predictions, a sophisticated hybrid random-interval analysis model is formulated wherein material property uncertainties are characterized probabilistically as random variables, while dimensional uncertainties associated with multi-crack size parameters are represented non-probabilistically as interval variables. This dual-characterization approach rigorously captures hybrid uncertainty effects through computationally derived interval boundaries of structural response and their stochastic features. To overcome computational bottlenecks in uncertainty quantification, an artificial intelligence-based solution is implemented: a BP(back propagation) neural network surrogate model is trained to replace resource-intensive finite element simulations, enabling rapid yet accurate prediction of collinear multi-crack propagation life under multi-source uncertainties. Comprehensive validation via an engineering case study focusing on collinear multi-crack coalescence and propagation in actual welded tank structures conclusively demonstrates the method’s efficacy, establishing it as a reliable and computationally efficient paradigm for analysing complex multi-crack behaviour in aerospace fuel tanks operating under uncertain conditions.更多还原