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During an aircraft development, mathematical models are elaborated from its characteristics, physical laws and modeler prior knowledge of the system. Once the aircraft built, those models (mainly linear models) are tuned with flight test recorded data. Regulation authorities define the precision needed for such models. The purpose of this paper is to build an aircraft global model complying with regulation authorities' accuracy requirements with minimal prior knowledge of the system. A professional D level simulator has been used as a flight test aircraft. More than 1,000 experimental flight tests were made with numerous configurations in speed (140 to 240 kt), altitude (10,000 to 46,300 ft), mass (24,000 to 33,000 lb) and the center of gravity position (17 to 34 % of the mean aerodynamic chord). Aircraft's global model is built by identifying linear models at flight points within aircraft flight envelop and the center of gravity limits.

One of the hardest tasks involving wind tunnel characterization is to determine the air-flow condition inside the test section. The Log-Tchebycheff method and the Equal Area method allow calculation of local velocities from measured differential pressures on rectangular and circular ducts. However, these two standard methods for air flow measurement are limited by the number of accurate pressure readings by the Pitot tube. In this paper, a new approach is presented for wind tunnel calibrations. This approach is based on a limited number of dynamic pressure measurements and a predictive technique using Neural Network (NN). To optimize the NN, the extended great deluge (EGD) algorithm is used. Wind tunnel testing involves a large number of variables such as wind direction, velocity, rate flow, turbulence characteristics, temperature variation and pressure distribution on airfoils.

During aircraft development, mathematical models are elaborated from our knowledge of fundamental physical laws. Those models are used to gain knowledge in order to make decisions in all development stages. Since engine model is one of the most important items in aircraft simulation, the aviation industry has recently developed a high interest on them. With the power capacities development in the last years, numerical simulations have been widely used for predicting engine response. In this paper, a methodology to identify an engine model from flight tests is presented. A Cessna Citation X Level D Flight Simulator designed and manufactured by CAE Inc. was used to sample the engine thrust force data. More than 500 flight tests were made for different flight conditions expressed in Mach numbers (M = 0 to M = 0.9), altitudes (h = 0 ft to h = 50,000 ft) and different throttle positions (idle to maximum).

A new approach for the prediction of lift, drag and moment coefficients is presented. This approach is based on the Support Vector Machines methodology, and on a optimization algorithm, the Extended Great Deluge. The novelty of this approach is the combination between the SVM and the EGD algorithm. The EGD is used to optimize the SVM parameters to allow it to predict the aerodynamic coefficients of ATR 42 model. The training and validation of this new combination method is realized using the aerodynamic coefficients of an ATR-42 wing model with Xfoil software and experimental tests using the Price-Païdoussis wind tunnel. The results obtained with our approach are compared with the XFoil results, experimental results and XFLR5 software results for different flight cases, expressed as various combinations of angles of attack and Mach numbers. The main purpose of this methodology is to rapidly predict aircraft aerodynamic coefficients.

An airplane model is usually obtained from preliminary wind tunnel experiments and CFD analysis. These models are then tuned from flight test measurements using system identification, and are used for airplane stability assessment and control design. However, sometimes no or little preliminary data and documentation are available and flight test identification is the main mean to obtain the model needed for control system design. If so, the purpose of this paper is to identify the grey-box model of an airplane without initial data using a combination of the least square and output error estimation methods. A grey-box model identification is preferred because it gives aerodynamic parameter estimations of the airplane. Before flight test data are available, this method was applied to the Cessna Citation X business airplane's high fidelity simulations and carried out with human-in-the-loop on a professional level D flight dynamics simulator designed and manufactured by CAE Inc.