Moment-based measurement uncertainty evaluation for reliability analysis in design optimization

System uncertainties play a major role in reliability analysis performed in design optimization. According to the Guide to the expression of Uncertainty in Measurement, linear approximation or Monte Carlo simulation can be used to perform the reliability analysis. Unfortunately the linear approximation is unreliable for non-linear problems and the Monte Carlo approach is computationally expensive for the iterative process used in design optimization. The current state-of-the-art techniques in design optimization bypass the direct uncertainty evaluation of the system outputs by finding the first point of failure known as the most probable point. Such an approach introduces additional iterations into the optimization framework and hence leads to high computational time in complex reliability-based design optimization problems. To address the shortcoming, this paper uses a novel moment-based approach to evaluate the measurement uncertainty, and then to perform the reliability analysis. This shortens the computational time significantly while allowing for better quality in the final design. The proposed approach was implemented on a real-world problem of designing an aerospike nozzle. The results show that the proposed method achieves the expected high quality of final design with up to 7-fold shorter computational time compared to the current state-of-the-art techniques.