Asymmetric Cessna Citation II Stall Model Identification using a Roll Moment-based Kirchhoff Method

Abstract

Accurate modeling of the unsteady aerodynamics during flow separation is critical for effective pilot stall training in Flight Simulation Training Devices and the development of automatic stall recovery controllers. Kirchhoff’s theory of flow separation has gained popularity due to its relative simplicity and suitability for parameter identification from flight data. The goal of this work is to improve an existing Cessna Citation II dynamic stall model’s 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. 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. Hence, a hybrid approach is proposed that combines separate sets of flow separation parameters for the longitudinal and lateral-directional models.