Flight models are used to accurately predict the aircraft performance and response and are used for aircraft development, engineering analysis, and pilot training. It is common practice to use linearized small perturbation equations of motion for the identification of the stability and control derivatives that form the basis of flight models using system identification. Due in large part to advances in computer technology and optimization techniques, it is feasible to use nonlinear equations of motion for time-domain system identification. This report compares two full flight envelope aircraft models that were developed with identical data: one developed using linearized equations of motion in a state space form, and the other with nonlinear equations. The global flight models were developed for the NRC Bell 412 helicopter in forward flight across its range of speeds, altitudes, and configurations. Use of the nonlinear equations of motion produced a model with less parameter variance and its corresponding global model had improved trim characteristics.

The authors have developed a novel physics-based nonlinear autoregressive exogeneous neural network model architecture for flight modelling across the entire flight envelope, called FlyNet. When using traditional parameter estimation and output-error methods, aircraft models are captured about a single point in the flight envelope using a first-order Taylor series to approximate forces and moments. To enable analysis throughout the entire flight envelope, the traditional models can be extended across the entire flight envelope by interpolating or stitching between a number of these single-condition models. This paper completes the evolutionary next step in aircraft modelling to consider all second-order Taylor series terms instead of a subset of those and by exploiting the ability of neural networks to capture more complex and nonlinear behaviour for the efficient development of a continuous flight simulation model valid across the entire flight envelope. This method is valid for fixed- and rotary-wing aircraft. The behaviour of a conventional model is compared to FlyNet using flight test data collected from the National Research Council of Canada’s Bell 412HP in forward flight.

The authors have developed a novel physics-based nonlinear autoregressive exogeneous neural network model architecture for flight modelling across the entire flight envelope, called FlyNet. When using traditional parameter estimation and output-error methods, aircraft models are captured about a single point in the flight envelope using a first-order Taylor series to approximate forces and moments. To enable analysis throughout the entire operational envelope, the traditional models can be extended by interpolating or stitching between a number of these single-condition models. This paper completes the evolutionary next step in aircraft modelling to consider all second-order Taylor series terms instead of a subset of those and by exploiting the ability of neural networks to capture more complex and nonlinear behaviour for the efficient development of a continuous flight simulation model valid across the entire envelope. This method is valid for fixed- and rotary-wing aircraft. The behaviour of a conventional model is compared to FlyNet using flight test data collected from the National Research Council of Canada’s Bell 412HP in forward flight.

The landing impact case results in the development of significant loads and accelerations within the airframe. Accurate knowledge of the landing loads is not only necessary for the stress analysis and design of the airframe, but also for designing strategies to mitigate the vibratory loads and improve the ride quality. Perceived passenger comfort is dependent both on the magnitude of the acceleration experienced by the passengers and on the frequency content of the vibrations. Using a flexible airframe model of a 150-passenger regional jet with cantilevered landing gear in a tricycle configuration, this study optimizes various single-port (two-terminal) passive mechanical networks that consist of an arrangement of springs, dampers, and inerters to minimize passenger discomfort and peak forces applied to the aircraft. The performance of the mechanical networks is compared to a baseline oleopneumatic shock absorber. First, the importance of including airframe flexibility effects was demonstrated as the peak landing gear loads, the loading regime, and the frequency response of the structure were altered when compared to the equivalent rigid model. Next, eight candidate layouts were optimized, then the observations from this exercise were used to synthesize a mechanical network with a desired frequency response. All considered mechanical networks demonstrated the ability to control the frequency content of the input loading, thus resulting in a reduction in accelerations and an improvement in all comfort parameters used in this study over the oleo-pneumatic baseline.This research was financially supported in part by the Natural Sciences and Engineering Research Council of Canada both by the Canada Graduate Scholarship-Master's and Discovery Grants. In addition, entrance scholarships to the Mechanical and Aerospace Engineering department, and Teaching Assistantships helped fund my research.

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