Local reliability sensitivity and global reliability sensitivity are required in reliability-based design optimization, since they can provide rich information including variable importance ranking and gradient information. However, traditional Monte Carlo simulation is inefficient for engineering application. A novel numerical simulation method based on Monte Carlo simulation and directional sampling is proposed to simultaneously estimate local reliability sensitivity and global reliability sensitivity. By suitable transformation, local reliability sensitivity and global reliability sensitivity can be estimated simultaneously as by-products of reliability analysis for Monte Carlo simulation method. The key is how to efficiently classify Monte Carlo simulation samples into two categories: failure samples and safety samples. Directional sampling method, a classical reliability analysis method, is more efficient than Monte Carlo simulation for reliability analysis. A novel strategy based on nearest Euclidean distance is proposed to approximately screen out failure samples from Monte Carlo simulation samples using directional sampling samples. In the proposed method, local reliability sensitivity and global reliability sensitivity are by-products of reliability analysis using the directional sampling method. Different from existing methods, the proposed method does not introduce hypotheses and does not require additional gradient information. The advantages of the Monte Carlo simulation and directional sampling are well integrated in the proposed method. The accuracy and the efficiency of the proposed method for local reliability sensitivity and global reliability sensitivity are demonstrated by four numerical examples and two engineering examples including the headless rivet and the wing box structure.

基于可靠性的设计优化需要局部可靠性敏感性和全局可靠性敏感性，因为它们可以提供丰富的信息，包括可变重要性等级和梯度信息。但是，传统的蒙特卡洛模拟在工程应用中效率低下。提出了一种基于蒙特卡罗模拟和定向采样的数值模拟方法，可以同时估计局部可靠性和全局可靠性。通过适当的变换，可以同时估计局部可靠性灵敏度和全局可靠性灵敏度，作为蒙特卡洛模拟方法可靠性分析的副产品。关键是如何有效地将蒙特卡洛模拟样本分为两类：故障样本和安全样本。定向采样方法是一种经典的可靠性分析方法，它比蒙特卡洛模拟进行可靠性分析的效率更高。提出了一种基于最近欧几里得距离的新策略，可以使用定向采样从蒙特卡洛模拟采样中近似筛选出失效样本。在该方法中，局部可靠性敏感性和全局可靠性敏感性是使用定向采样方法进行可靠性分析的副产品。与现有方法不同，所提出的方法不引入假设，并且不需要其他梯度信息。所提出的方法很好地整合了蒙特卡罗模拟和定向采样的优点。