A case study : Influence of Dimension Reduction on regression trees-based Algorithms -Predicting Aeronautics Loads of a Derivative Aircraft

In aircraft industry, market needs evolve quickly in a high competitiveness context. Thisrequires adapting a given aircraft model in minimum time considering for example an increase of range or of the number of passengers such as the A330 family in [1]. In our case study, variants concern the maximum takeoff weight of a given aircraft model. Depending on the configuration, the computation of loads and stress, as defined in [13, 12], to resize the airframe is on the critical path of this aircraft variant definition: this is a time consuming (approximately a year for a new aircraft variant) and costly process, one of the reason being the high dimensionality and the large amount of data. Big Data approaches such as defined by [11] is mandatory to improve the speed, the data value extraction and the responsiveness of the overall process. This study has been realized during aproof of value sprint project within Airbus to demonstrate the usefulness of statistics and machine learning approaches in the Engineering field. In a previous internal project, it has been shown that the family of regression trees [5] works well to predict loads for different aircraft missions in an interpolation context. Thus, we can formulate our problem in this way: is it possible to use dimensional reduction and regression trees-based algorithms to predict loads in an extrapolation context (i.e outside the design space of a certain weight variant) toimprove the actual process?