Asymmetric Cessna Citation II Stall Model Identification using a Roll moment-based Kirchhoff method

Abstract

Adequate modeling of the unsteady aerodynamics during flow separation is critical for effective pilot training in Flight Simulation Training Devices. Over the years, a stall modeling method rooted in Kirchhoff's theory of flow separation has gained popularity due to its relative simplicity and suitability for parameter identification from flight data. This method describes the lift using a single internal flow separation point variable. A major drawback of Kirchhoff's method comes from the one-dimensionality of the flow separation point, limiting the representation of asymmetric flow separation. The goal of this work is to improve the existing Cessna Citation II dynamic stall model fidelity by applying Kirchhoff's method for each wing surface, separately. The main contribution is the identification of asymmetric flow separation development, using the flight-derived roll moment and a roll moment model based on the differential flow separation between the wing surfaces. Transformations of the flow separation variables were chosen by a Multivariate Orthogonal Functions selection algorithm to capture the stall-related nonlinearities of the roll moment, yaw moment, and lateral force. The longitudinal model structures are adopted from the existing, validated baseline stall model. The lateral-directional model outputs are in good agreement with the validation flight data, showing an average reduction of 48\% in Mean Squared Error (MSE) compared to the baseline stall model. In contrast, the longitudinal model output results in an average MSE increase of 88\%, suggesting that the estimated asymmetric flow separation parameters are unsuitable for longitudinal stall modeling. A promising way to incorporate the benefits of the proposed method is suggested, by adopting a hybrid approach that combines separate sets of flow separation parameters --- symmetric and asymmetric variants --- for the longitudinal and lateral-directional models, respectively.