Adaptive data fusion framework for modeling of non-uniform aerodynamic data

Abstract

Multi-fidelity Data Fusion (MDF) frameworks have emerged as a prominent approach to producing economical but accurate surrogate models for aerodynamic data modeling by integrating data with different fidelity levels. However, most existing MDF frameworks assume a uniform data structure between sampling data sources; thus, producing an accurate solution at the required level, for cases of non-uniform data structures is challenging. To address this challenge, an Adaptive Multi-fidelity Data Fusion (AMDF) framework is proposed to produce a composite surrogate model which can efficiently model multi-fidelity data featuring non-uniform structures. Firstly, the design space of the input data with non-uniform data structures is decomposed into subdomains containing simplified structures. Secondly, different MDF frameworks and a rule-based selection process are adopted to construct multiple local models for the subdomain data. On the other hand, the Enhanced Local Fidelity Modeling (ELFM) method is proposed to combine the generated local models into a unique and continuous global model. Finally, the resulting model inherits the features of local models and approximates a complete database for the whole design space. The validation of the proposed framework is performed to demonstrate its approximation capabilities in (A) four multi-dimensional analytical problems and (B) a practical engineering case study of constructing an F16C fighter aircraft’s aerodynamic database. Accuracy comparisons of the generated models using the proposed AMDF framework and conventional MDF approaches using a single global modeling algorithm are performed to reveal the adaptability of the proposed approach for fusing multi-fidelity data featuring non-uniform structures. Indeed, the results indicated that the proposed framework outperforms the state-of-the-art MDF approach in the cases of non-uniform data.