## 1 Introduction  
Aircraft performance data for aircraft autopilots and some training-level flight simulators design are generally obtained from flight testing under normal flight conditions. It is impossible to take all flight conditions into account in the design process of transport aircraft. Some flight conditions, e.g., in-flight abrupt descent in clear-air turbulence (CAT), are not easy to test in airworthiness certification, which may lead to the aircraft loss of control in commercial service operation. The flight mechanism of off-nominal conditions cannot easily be simulated by wind tunnel tests, and it is not possible to directly perform flight tests with real aircraft. In other words, the design value of aerodynamic performance is inconsistent with the actual aerodynamic performance during the flight operations. It is appropriate that off-nominal flight conditions cannot be combined with aircraft design and flight simulators [1]. More importantly, the pilots cannot gain much experience in these flight conditions. The adverse weather related to off-nominal flight conditions includes (but is not limited to) heavy rain, convective storms [2], turbulence [3, 4], crosswind [5, 6], wind shear [7, 8], and in-flight icing [9, 10]. Adverse weather impacts the flight safety of transport aircraft and the operational efficiency of air traffic management. An innovative commercial flight route planning tool is developed to avoid the deep convective storms [2]. This program provides an overview to establish a better air route based on original trajectory operations for safe, efficient, and comfortable operating flights to the airlines. Among the different adverse weathers, clear-air turbulence is important in flight safety since it is hard to detect and predict. Clear-air turbulence is the leading cause of serious personal injuries in non-fatal accidents of commercial aircraft [11]. One main type of motion that causes flight injuries in clear-air turbulence is the sudden plunging motion with abrupt changes in attitude and gravitational acceleration (i.e., the normal load factor) [12]. The assessment of flying qualities during sudden plunging motion was presented for an aged twin-jet transport aircraft encountering severe clear-air turbulence through digital six degree-of-freedom (6-DOF) flight simulations. The important process of the method is to numerically integrate the 6-DOF equations of motion and simultaneously determine the eigenmodes of motion at every instant. The longitudinal equations are coupled with the lateral-directional equations, and some nonlinear effects are incorporated, which is an advantage of 6-DOF flight simulation [13]. Unfortunately, with that approach, it was difficult to identify the individual modes of motion from these eigenvalues from one instant to another because of the rapid changes in aerodynamic forces and moments in turbulence. In Ref. [11], an aged twin-jet transport aircraft was tested. The static aeroelastic effect was significant when the transport aircraft sustained instantaneous high g-loads in severe clear-air turbulence. Any structural deformation with static aeroelastic effects caused by flight loads would change certain aerodynamic parameter values. However, that article did not provide the corresponding relationship between the static aeroelastic effect and eigenvalue motion modes. This article presents flight data mining and the fuzzy logic modeling of an artificial intelligence technique to establish nonlinear and unsteady aerodynamic models for six aerodynamic coefficients based on the flight data of a new twin-jet transport aircraft with the service year of only 11 months. The new aircraft has fewer static aeroelastic effects in comparison with the aged one, and even those of the new one can be negligible [14]. This new twin-jet encountered severe clear-air turbulence twice during the descending phase. The oscillatory derivatives extracted from the aerodynamic models were used to study the dynamic stability characteristics during the sudden plunging motion.