MonteCarlo Information-Reuse Approach to Aircraft Conceptual Design Optimization Under Uncertainty

摘要

This paper develops a multi-information source formulation for aerospace design under uncertainty problems. As a specific demonstration of the approach, it presents the optimization under uncertainty of an advanced subsonic transport aircraft developed to meet the NASA N+3 goals and shows how the multi-information source approach enables practical turnaround time for this conceptual aircraft optimization under uncertainty problem. In the conceptual design phase, there are often uncertainties about future developments of the underlying technologies. An aircraft design that is robust to uncertainty is more likely to meet performance requirements as the technologies mature in the intermediate and detailed design phases, reducing the need for expensive redesigns. In the particular example selected here to present the new approach, the multi-information source approach uses an information-reuse estimator that takes advantage of the correlation of the aircraft model in the design space to reduce the number of model evaluations needed to achieve a given standard error in the MonteCarlo estimates of the relevant design statistics (mean and variance). Another contribution of the paper is to extend the approach to reuse information during trade studies that involve solving multiple optimization under uncertainty problems, enabling the analysis of the risk-performance tradeoff in optimal aircraft designs.