AI-Based Multifidelity Surrogate Models to Develop Next Generation Modular UCAVs

Abstract

The next generation low-cost modular unmanned combat aerial vehicles (UCAVs) provide the opportunity to implement innovative solutions to complex tasks, while also bringing new challenges in design, production, and certification subjects. Solving these problems with tools that provide fast modeling in line with the digital twin concept is possible. In this work, we develop an artificial intelligence (AI) based multifidelity surrogate model to determine performance parameters of innovative modular UCAVs. First, we develop a data generation algorithm that includes a high-fidelity model based on computational fluid dynamics methods and a low-fidelity model based on computational aerodynamic approaches. In the next step, a new transfer learning-based surrogate model is generated using multifidelity data. Thanks to this approach, the developed AI model more accurately predicted the flow conditions that were missing in the high-fidelity data with the data obtained from the low-order model. The performance of the proposed AI-based surrogate model is to be investigated in terms of accuracy, robustness, and computational cost using a generic modular UCAV configuration.