• Reliability-Based Design Optimization (RBDO)
• Reliability-Based Robust Design Optimization (RBRDO)
• System Reliability Analysis and Design Optimization
• Design under Uncertainties with Lack of Information
• Design under Uncertainties with Correlated Input Variables
• Sampling-Based RBDO with Parallel Computing
• Surrogate Model Generation (Meta-modeling)
• Statistical Model Calibration and Validation
• Multidisciplinary Design Optimization
• Topology Optimization

Reliability Analysis

As reliability of engineering systems under uncertainty becomes more important in various industries due to global competitive market situation, a safer and more reliable product design to satisfy consumers’ needs is required. To satisfy these requirements, there have been various attempts to accurately and efficiently compute the product reliability, which is obtained from reliability analysis and used as a probabilistic constraint of reliability-based design optimization. Reliability analysis methods are classified into (1) analytical methods and (2) simulation or sampling methods. The analytical methods include the first-order reliability method (FORM), the second-order reliability method (SORM), and most probable point (MPP)-based methods. Sampling or simulation methods such as the Monte Carlo simulation (MCS), importance sampling method, and Latin hypercube sampling method can be easily used for calculating the probability of failure because these methods do not require any analytical formulation. Recently, new algorithms that improve the efficiency and accuracy of the existing analytical methods, SORM, dimension reduction method (DRM), have been studied in our laboratory.