Integrated Airline Network and Aircraft Design Optimization

What is it about?

The determination of optimal aerial transport networks and their associated flight frequencies is crucial for the strategic planning of airlines, as well as for carrying out market research, and for aircraft and crew rostering. In addition, optimum airplane types for the selected networks are crucial to improve revenue and to provide reduced operating costs. The present study proposes an innovative approach to determine the optimal aerial transport network simultaneously with the determination of the optimum fleet for that network, composed of three types of airplanes (network and vehicle integrated design). The network profit is maximized. The passenger’s demands between the airports are determined via a gravitational model. An embedded linear programming solution is responsible for obtaining potential optimal network configurations. The optimum fleet combination is determined from a database of candidate aircraft designs via genetic algorithm. A truly realistic airplane representation is made possible thanks to accurate surrogate models for engine and aerodynamics is adopted. An accurate engine deck encompassing a compression map and an innovative engine weight calculation besides an aerodynamic artificial neural network module enable a high-degree of accuracy for the mission analysis. The proposed methodology is applied to obtain the optimum network comprised of twenty main Brazilian airports and corresponding fleet

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Why is it important?

The major contributions of the present research are outlined as follows: I. Aircraft design integrated to airline network. II. A database of airplanes with the most distinguished characteristics is employed in the process. Optimal fleet is then found from the airplanes from this database, ensuring this way faster convergence of the optimization process. III. Many design parameters are used to represent the airplane in finest detail with accurate aerodynamic, stability and control, and performance calculations, necessary for realistic mission analysis. IV. Aircraft are generated according to the following design features: a. Adherence to FAR 25 requirements: climb rate at 2nd segment, missed approach, takeoff field length, landing field length, climb rate at service ceiling, cruise speed, and adequate fuel storage. b. Calculation of noise signatures at ICAO certification points: sideline, approach, and takeoff [9]. c. Innovative method for turbofan engine weight: coupling with engine deck program guarantees accurate weight calculation. d. Realistic landing gear sizing and integration into the configuration avoiding flaps being affected by wake generated by wheels and hit by engine hot exhaust gases. e. Proper sizing of wheel tires selected from tables containing internal pressure, loads, speed and other parameters. Main landing gear trunnion is positioned between the rear and auxiliary spars of the inner wing. f. Ditching requirements are considered for fuselage cross-section sizing. g. Engines of underwing configurations are positioned in such way to avoid uncontained fan debris to hit fuel tanks. h. An Artificial Neural Network (ANN) system is employed to calculate the aerodynamic characteristics of the airplane configurations, based on full potential formulation with viscous correction [36]. The use of the ANN enabled a high degree of accuracy and fidelity for the aerodynamics of the present work, allowing performance calculations in such a level never achieved in conceptual design before. V. Realistic airline mission performance calculation, which also considers true airline operations parameters, was employed. VI. Optimal airplane fleets are obtained considering maximization of network profit, including the related network DOC. A genetic algorithm is then used, and the results are analyzed in a multi-objective context, presenting a Pareto-type analysis of network profit versus DOC. VII. City pair demands are calculated via gravitational model. VIII. The determination of the optimum network considers a two-stop route model and three airplane types composing the airline fleet. This is solved in a sub-procedure for obtaining the network with maximum profit. IX. A test case considering the twenty major Brazilian airports was run and optimum networks that were obtained from the computations are analyzed and discussed.

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