This paper presents a novel Advanced Uncertainty-Driven Integrated Mechanical (AUDIM) framework for the reliability-based optimization of submarine pressure hulls. The methodology integrates multi-source uncertainties material variability, manufacturing tolerances, and operational loading fluctuations into a holistic probabilistic design process. It employs a hierarchical Bayesian framework coupled with adaptive Gaussian process surrogate modeling to directly quantify reliability indices. The implementation follows a four-stage process: (1) uncertainty landscape mapping via global sensitivity analysis; (2) adaptive Kriging (AK-MCS) metamodeling for computationally efficient nonlinear finite element analysis; (3) failure probability estimation using the first-order reliability method (FORM) combined with importance sampling; and (4) multi-objective robust design optimization via the sequential optimization and reliability assessment (SORA) algorithm within an NSGA-II optimizer. When applied to a deep-diving submarine pressure hull with a baseline weight of 51 tons, the AUDIM framework achieved a 4.6% weight reduction while maintaining a system buckling reliability index of β = 4.82, exceeding classification society requirements. Sensitivity analysis revealed that external pressure variability contributes 42.3% to displacement variance, offering clear guidance for targeted quality control. The results demonstrate that probabilistic design enables scientifically balanced trade-offs between weight, cost, and safety, providing naval architects with a transparent, computationally efficient decision-support tool for next-generation submarine design.
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