To improve the safety of commercial air transport, pilots are required to train on simulators to recognize the characteristics of an impeding stall and subsequently correctly recover from it. To prevent negative training, it is important that the accuracy of the used simulation m
...
To improve the safety of commercial air transport, pilots are required to train on simulators to recognize the characteristics of an impeding stall and subsequently correctly recover from it. To prevent negative training, it is important that the accuracy of the used simulation models is high. A manner to model the nonlinear, unsteady aerodynamic effects during the stall is by using Kirchhoff's theory of flow separation. However, widespread difficulties exist in correctly estimating the stall-related parameters in these models. It is not always possible to simply gather more flight data to solve this problem. Therefore, the research in this paper aims to increase model accuracy by making optimal use of already existing flight data via introduction of the slice-based modeling method. This is done by analyzing the change in the parameter estimate values when applying the system identification procedure to sliced partitions of simulated flight data. These partitions incrementally increase in size with time from the stall. The simulation data is generated to be representative of the available flight test data. The change in parameter estimates was analyzed for both the pre-stall and post-stall phase. The estimated value for each partition was compared to the actual parameter value setting in the simulation model used to create the data. Manually, an optimal window was found for each parameter for which the estimated value and actual value were equal. For the stall-related parameters this window is often not more than 10 s wider than the stall. For the linear stability and control derivatives it is found that using more data generally results in a better estimate. These window sizes were used in the estimation for each separate parameter on the real flight test data. Even though this method represents a prototype, in more than half of the validation cases a decrease in MSE of 10 % to 35 % could be achieved. This shows that the new slice-based modeling method is able to improve the accuracy of nonlinear stall models without the need to gather more flight data. Additionally, the parameter estimate behaviour analysis technique and slice-based modeling method may have applications that reach beyond the realm of stall modeling.
