Abstract:
The advent of electric propulsion technology has led to a paradigm shift in aircraft design over the past few decades. This shift has expanded the possibilities for design and optimization processes more than at any previous time. To support these advancements, efficient flight dynamics simulation models that can be employed in iterative optimization and design processes are essential. Among the modules of a typical flight dynamics framework—namely, control, flight dynamics, and aerodynamics—the aerodynamics module, which includes the rotor performance model, generally demands the most computational effort, thereby limiting simulation efficiency. In this study, a novel machine learning (ML)-assisted flight dynamics framework is developed, incorporating a Neural Network Blade Element Theory (NN-BET) model as the rotor performance module. The results show a 7- to 8-fold reduction in computational time compared to fast, physics-based frameworks utilizing efficient Blade Element Momentum Theory (BEMT) models, without compromising predictive accuracy. Furthermore, the modular architecture of the framework allows for easy adaptation to a wide range of practical applications by replacing modules with functionally equivalent alternatives. The demonstrated accuracy and computational efficiency of the proposed flight dynamics framework make it a highly promising candidate for optimization and design applications.