Di Ding

The correlation between aerodynamic data obtained from ground and flight tests is crucial in developing aerospace vehicles. This paper proposes methods for modelling this correlation that combine feature extraction and symbolic regression. The neighborhood component analysis (NCA) method is utilized to extract features from the high-dimensional state space and then symbolic regression (SR) is applied to find the concise optimal expression. First, a simulation example of the NASA Twin Otter aircraft is used to validate the NCA and the SR tool developed by the research team in modeling the aerodynamic coefficient deviation between ground and flight due to an unpredictable inflight icing failure. Then, the method and tool are applied to real flight tests of two types of aerospace vehicles with different configurations. The final optimized mathematical models show that the two vehicles’ pitching moment coefficient deviations are related to the angle of attack (AOA) only. The mathematical model built using NCA and the SR tool demonstrates higher fitting accuracy and better generalization performance for flight test data than other typical data-driven methods. The mathematical model delivers a multi-fold enhancement in fitting accuracy over data-driven methods for all fight cases. For UAV flight test data, the average root mean square error (RMSE) of the mathematical model demonstrates a maximum improvement of 37% in accuracy compared to three data-driven methods. For XRLV flight test data, the prediction accuracy of the mathematical model shows an enhancement exceeding 80% relative to Gaussian kernel SVM and Gaussian process data-driven models. The research verifies the feasibility and effectiveness of the data feature extraction combined with the symbolic regression method in mining the correlation law between ground and flight deviations of aerodynamic characteristics. This study provides valuable insight for modeling problems with finite data samples and explicit physical meanings.

Direct measurement of engine thrust during aircraft flight remains challenging. Currently, engineering methods yield only coarse approximations of thrust during flight. This limitation significantly impedes aerodynamic parameter identification from powered flight data, particularly undermining the credibility and accuracy of the identification results of aerodynamic force coefficients. To address these challenges inherent in aerodynamic parameter identification from powered flight data, this study proposes a novel joint online estimation method capable of simultaneously estimating system states, unknown aerodynamic parameters, and engine thrust. The algorithm integrates Kalman filters with computationally efficient recursive least squares (RLS) estimators to perform sequential estimation of flight data. This structure provides real‐time access to unmeasurable engine thrust and enhancement of the estimation precision of aerodynamic parameters. The effectiveness of the proposed algorithm was rigorously validated and assessed using both simulation and flight test data from the CAE‐AVM benchmark aircraft model. The method successfully generated valid estimates of engine thrust and aerodynamic parameters from both datasets and exhibited superiority over the EKF and MMAE algorithms. Specifically, for flight test phases including climb, cruise, and descent, the maximum root mean square relative error (RMSE) for thrust estimates was found to be only 17.36%. These results demonstrate the high estimation accuracy of the proposed joint estimation method for both simulation and flight test data and validate its high effectiveness for the identification and processing of aircraft‐powered flight data.

This paper proposes a new algorithm for the aerodynamic parameter and noise estimation for aircraft dynamical systems. The Bayesian inference method is combined with an unscented Kalman filter to estimate the augmented states and the unknown noise covariance parameters jointly. A Gauss‐Newton method is utilized to sequentially maximize the posterior likelihood function for the noise unknown parameter estimation. The performance of the proposed algorithm is evaluated and compared with two other UKFs via a flight scenario of a given aircraft. The results indicate that the proposed algorithm has equivalent performance to the simplified UKF with prior noise information and slightly outperforms the parallel UKF on precision and efficiency in this flight scenario assessment. Then, the consistency and accuracy of the algorithm are further validated by a Monte Carlo simulation with random process noise covariance. This adaptive algorithm provides another feasible and effective way for estimating aerodynamic parameters from the aircraft real flight data with unknown noise characteristics.

Aircraft inflight icing detection is very important for aviation flight safety. The ice alarm signal given by detectors can help the pilot to actuate the ice protection system. The application of a Neyman Pearson (NP) based statistical decision approach in icing detection is discussed in this paper. First, the flight dynamics for icing aircraft is modelled, the dynamic behaviour of an airbus influenced by ice accreting on the leading edge of wings is analysed. Then the residuals which are directly affected by the aerodynamic parameters are presented and their generation way is given. The Generalized Likelihood Ratio Test (GLRT) method is utilized in detecting the changes of residuals inflight, its performance is assessed by a flight scenario with wing ice. The results show that the fault of wing icing can be detected by every residual under different window sizes. By comparing the results of the residuals, it can be concluded that the small window size will degrade the detection performance with the longer alarming time delay and the lower probability of detection; appropriately increasing the window size can reduce the alarming time delay; diagnosing by residual of normal overload is more reliable than by the other two residuals.