Statistical Methods for Helicopter Preliminary Design and Sizing

Abstract

The present paper focuses on the applicability of statistical methods to helicopter design. Firstly the structure and statistical dependencies of a database of 150 existing helicopters are investigated by means of principal component and correlation analysis. The multivariate regression method presented is capable of automated computation of regression functions for a variety of input and output parameters. Additionally a minimum degree of complexity of the regression function is estimated by hypothesis testing. In contrast to most of the approaches used in literature a polynomial regression model was chosen in this paper. The regression result can be improved by using a partial data set, which can be extracted using manually defined criteria or – statistically motivated and unsupervised – by clustering. Subject to the underlying database relative errors of less than 10% for certain design parameters are possible – allowing for a suitable application in helicopter preliminary design.