Reliability design of structures - Huang, Weiwen (2017)
Reliability-based design methods also known as the probabilistic design has been increasingly popular in engineering. Reliability based methods allows engineers and designers to determine a structure or product’s failure probability using statistical methods, allowing improved design and reduced production costs. In reliability-based analysis, it is often easy to analyse a simple structure like a cantilever or even a truss structure with known shape and form. However, in reality, many structures are composed of many components with different shapes and forms and it is extremely hard or impossible to analyse using analytical methods. Therefore, in cases where structures are too complex, computer aided engineering tools are used. Using a software can help reduce the computational time and allows failure probability estimates to be much more accurate. The main purpose of this report is to serve as a reference and guide to readers that wishes to perform reliability-based analysis using ANSYS, a computer aided simulation tool. It also hopes to illustrate the efficiency of using reliability-based methods. ANSYS uses Finite Element Analysis to analyse and determine a structure’s stress regions, strains, and deformations under various loading conditions. Based on a single analysis, the probability of failure of a structure can be determined by modeling each random input variables as a probability distribution and repeating analysis over a number of iterations. By doing so, it simulates the randomness in the operating conditions. Reliability-based analysis will be used on three examples where the structure model, loading as well as boundary conditions are provided. Different probability distributions and sampling methods will be used to illustrate the effects in using different parameters for the analysis. By repeating over a number of iterations, a failure probability estimate is obtained. By illustrating the methodologies of using reliability-based analysis, it will demonstrate the effectiveness, precision, and efficiency in using the reliability-based or probabilistic methods.