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Cumulative global metamodels with uncertainty — a tool for aerospace integration

Abstract The integration of multidisciplinary data is key to supporting decisions during the development of aerospace products. Multidimensional metamodels can be automatically constructed using limited experimental or numerical data, including data from heterogeneous sources. Recent progress in multidimensional response surface technology, for example, provides the ability to interpolate between sparse data points in a multidimensional parameter space. These analytical representations act as surrogates that are based on and complement higher fidelity models and/or experiments, and can include technical data from multiple fidelity levels and multiple disciplines. Most importantly, these representations can be constructed on-the-fly and are cumulatively enriched as more data become available. The purpose of the present paper is to highlight applications of these cumulative global metamodels (CGM), their ease of construction, and the role they can play in aerospace integration. A simple metamodel implementation based on a radial basis function network is presented. This model generalises multidimensional data while simultaneously furnishing an estimate of the uncertainty on the prediction. Four examples are discussed. The first two illustrate the efficiency of surrogate-based design/optimisation. The third applies CGM concepts to a data fusion application. The last example is used to visualise extrapolation uncertainty, based on computational fluid dynamics data.