Search Results

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).

Flight trajectory optimization algorithms reduce flight cost and fuel consumption, thereby reducing the polluting emissions released to the atmosphere. Ground teams and avionics equipment such as the Flight Management System evaluate different routes to minimize flight costs. The optimal trajectory represents the flight plan given to the crew. The resulting flight plan contains waypoints and weather information such as the wind speed and direction and the temperature for each waypoint. The flight plan is normally introduced manually into the Flight Management System. In this paper, genetic algorithms were applied to the waypoints available in a flight plan to find the altitudes that minimize total fuel consumption, taking into account the cruise-climb and cruise-descent steps' costs. The genetic algorithms emulate the evolution process through a predefined number of generations.

This paper describes an optimization algorithm that provides an economical Vertical Navigation profile plan by finding the combinations of climb, cruise and descent speeds, as well as the altitudes for an aircraft to minimize flight costs. The computational algorithm profits from a space search reduction algorithm to reduce the initial number of speed and altitude combinations. Additional search space reductions were performed with the implementation of the branch and cut algorithm. A bounding function that correctly estimates the flight cost considering step climbs was developed to reduce the number of calculations. The full flight fuel burn cost was obtained using a performance database- based method. The fuel flight cost was computed using the cost index. This algorithm used a performance database instead of equations of motion to compute fuel burn. This database was developed and validated by our industrial partner using real flight experimental data.

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 the best decisions at all development stages. Depending on the application, different models can be used to describe, in one way or another, the aircraft behavior. The goal of this paper is to develop a high-fidelity aircraft simulation model that is exceptionally capable, flexible and responsive to the needs of the researchers. The proposed model includes nonlinear aerodynamic coefficients, a generic engine model and a complete autopilot with auto-landing. The simulation model has been designed to help researchers develop and validate new algorithms for trajectory optimization, control design, stability analysis and parameter estimation. To make it easy to use, the simulation model also includes algorithms for stability and control analysis.

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.

This article presents a structural analysis of the Unmanned Aerial System UAS-S4 ETHECATL. Mass, center of gravity position and mass moment of inertia are numerically determined and experimentally attested using the pendulum method. To determine the mass moment of inertia, a bifilar torsion-type pendulum is used for the Z-axis and a simple pendulum for the X and Y axes [14]. A nonlinear dynamic model is developed for the rotational motion about the center of gravity (Gs) of the tested system, including the effects of large-angle oscillations, aerodynamic drag, viscous damping and additional mass effects. MATLAB genetic algorithms are then used to obtain the values of mass moment of inertia that would validate the experimental data with the numerical results. The experiment used data gathered by three sensors: an accelerometer, a gyroscope and a magnetometer. Therefore, a method is used to calibrate these three sensors.

The main goal of this flight control system is to achieve good performance with acceptable flying quality within the specified flight envelope while ensuring robustness for model variations, such as mass variation due to fuel burn. The Cessna Citation X aircraft linear model is presented for different flight conditions to cover the aircraft's flight envelope, on which a robust controller is designed using the H-infinity method optimized by two heuristic algorithms. The optimal controller was used to achieve satisfactory dynamic characteristics for the longitudinal and lateral stability control augmentation systems with respect to this aircraft's flying quality requirements. The weighting functions of the H-infinity method were optimised by using both genetic and differential evolution algorithms. The evolutionary algorithms gave very good results.

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.

In this paper, we describe a practically efficient methodology of improving the aerodynamic characteristics of an UAS's wing using a morphing approach. We have replaced a part of the original wings' upper and lower surfaces with a flexible, composite material skin whose shape can be modified, according to the variable airflow conditions, using internally placed actuators. The optimal displacements of the actuators, as functions of the external flow characteristics, are determined using a genetic algorithm based optimizer, coupled with a three - dimensional numerical extension of the classical lifting line model for estimating the modified wing aerodynamic coefficients. We have used the optimization tool to decrease the overall drag coefficient of a military grade UAS' wing equipped with the flexible skin. We have obtained good quality solutions for only a fraction of the computational cost needed when performing viscous flow field calculations.

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.

In this work, the Hydra Technologies Unmanned Aerial System (UAS) wing was simulated in several flight conditions. The simulations were carried out with the FLUENT Computational Fluid Dynamics code in 2D, and the grid was drawn with ICEM. Simulations were conducted using the turbulent models Spalart-Allmaras, SST k-ω and transition SST k-ω. The calculated results were compared with Xfoil. This comparative study allowed increasing the accuracy of the results as for the aerodynamic parameters of the wing CL, CD, CM and CP. The parallel objective was to determine the transition point. All the turbulent models were able to accurately simulate the correct behavior of the CP but transition SST k-ω appeared to be the only one able to find the transition point. In this study, we simulate, in 2D, a specific section of the wing located at the wing tip.