# A Novel Aerodynamic Modeling Method Based on Data for Tiltrotor evtol
## 1 Introduction  
Urban traffic congestion has worsened due to increasing urbanization. This has led to the development of electric vertical take-off and landing (evtol) aircraft concepts [1, 2]. The tiltrotor evtol configuration, which combines features from traditional fixed-wing and rotor aircraft, shows potential for sustainable transport over medium to long distances, particularly in areas with complex routes and severe congestion [3]. Aerodynamic modeling is critical in determining the dynamic characteristics and simulation fidelity of maneuvering stability for evtol. Furthermore, aerodynamic modeling can offer an affordable and replicable simulation platform for designing flight control systems, assessing maneuvering stability, modifying designs, and other relevant aspects of evtol operations. Concurrently, aerodynamic modeling is the primary method used to describe evtol aerodynamic effects [4]. It reflects the variation characteristics of these effects with factors such as airspeed, configuration, tilt angle, and control surface deflection angle. The aerodynamic characteristics of tiltrotor evtol aircraft vary significantly between different flight modes, particularly during the transition phase caused by nacelle tilting [5, 6]. Due to the influence of flight conditions and rotor tilting motions, tiltrotor evtol aircraft exhibit high complexity, strong coupling, and nonlinearity in their aerodynamic characteristics. These variations in rotor-induced velocities, as well as the effects of rotor downwash and wake, make aerodynamic modeling extremely challenging. Some aerodynamic propulsion modeling methods for lift + cruise evtol aircraft are proposed in references [7, 8, 9, 10]. System identification techniques and computational fluid dynamics (CFD) simulations are used to develop flight dynamic models and validate the predictive capabilities of aerodynamic propulsion models. However, aerodynamic modeling methods for tiltrotor evtol still rely on traditional physical mechanisms for aerodynamic modeling. References [11, 12] suggest that traditional methods are not effective in simulating certain aerodynamic characteristics, such as rotor wake and downwash. Concurrently, there is a certain contradiction between the realism of models established by traditionalmethods and the real-time performance of simulations. Therefore, most aerodynamic modeling methods used in real-time simulation environments are based on simplified physical mechanisms, which limits their ability to perform high-fidelity simulations. The use of algorithms and computational capabilities has led to the widespread adoption of data-driven aerodynamic modeling methods based on neural networks, commonly used neural network methods in aerodynamic prediction include MLP, RBFNNs, CNNs, RNNs, and GANs [13; 14; 15]. These methods do not require the establishment of complex mathematical formulae based on physical mechanisms [16; 17]. Instead, they learn the hidden dynamic characteristics of the system through sample data. Data-driven methods can address high-dimensional, multiscale, and nonlinear problems that are difficult to solve with traditional methods. The aim of this study is to validate the use of data-driven modeling methods in aerodynamic modeling during the transition phase of tiltrotor evtol aircraft. This study analyses the complex relationship between dimensionless aerodynamic force coefficients of tiltrotor aircraft and their influencing factors. A multilayer perceptron (MLP) neural network is selected for multivariate nonlinear regression for data-driven aerodynamic modeling. The data-driven model was constructed from the wind tunnel test data of XV-15 and trained using an MLP neural network. A comparison was conducted between the predictive performance of a data-driven model and a mathematical model based on physical mechanisms. The results demonstrate that the data-driven model can capture aerodynamic characteristics that are challenging to express in mathematical models.