Abstract
The design of aircraft engines involves computationally expensive engineering simulations. One way to solve this problem is the use of response surface models to approximate the high-fidelity time-consuming simulations while reducing computational time. For a robust design, sensitivity analysis based on these models allows for the efficient study of uncertain variables’ effect on system performance. The aim of this study is to support sensitivity analysis for a robust design in aerospace engineering. For this, an approach is presented in which random forests (RF) and multivariate adaptive regression splines (MARS) are explored to handle linear and non-linear response types for response surface modelling. Quantitative experiments are conducted to evaluate the predictive performance of these methods with Turbine Rear Structure (a component of aircraft) case study datasets for response surface modelling. Furthermore, to test these models’ applicability to perform sensitivity analysis, experiments are conducted using mathematical test problems (linear and non-linear functions) and their results are presented. From the experimental investigations, it appears that RF fits better on non-linear functions compared with MARS, whereas MARS fits well on linear functions.
Highlights
The performance of a product may vary to a great extent depending on variations in design parameters, work environment, etc
The results show that random forests (RF) yields the lowest error for 12 datasets, and multivariate adaptive regression splines (MARS) yields the lowest error for 7 datasets
We explored the use of response surface models to support sensitivity analysis for robust design of Turbine Rear Structure (TRS)
Summary
The performance of a product may vary to a great extent depending on variations in design parameters, work environment, etc. Given these uncertainties, designers are highly interested in evaluating how to design a product. The aim is to determine a continuous function f (model) of a set of design variables x = x1, x2, ..., xn from a limited amount of available data D. Response surface modelling deals with two problems which are constructing a model ffrom the available data D and evaluating the error ε of the model (Mack et al 2007). The construction of the response surface model involves several steps (shown in Fig. 1) (Queipo et al.2005):
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