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Author Rajkumar, T. ♦ Bardina, Jorge
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Pitching Moment ♦ Mach Number ♦ Training Data ♦ Numerical Simulation ♦ Wind-tunnel Experiment ♦ Flow Characteristic ♦ Excellent Potential ♦ Sparse Data ♦ Flight Simulation ♦ Complex Aerodynamic Coefficient ♦ Aerodynamic Coefficient ♦ Fast Method ♦ Neural Network ♦ Control Surface ♦ Basic Coefficient ♦ Aerodynamic Model ♦ Aerodynamic Coefficient Using Neural Network ♦ Rapid Development ♦ Good Agreement
Description A reliable and fast method of predicting complex aerodynamic coefficients for flight simulation is presented using neural networks. The training data for the neural network is derived from numerical simulations and wind-tunnel experiments. The aerodynamic coefficients are modeled as functions of the flow characteristics and the control surfaces of the vehicle. The basic coefficients of lift, drag and pitching moment are expressed as function of angles of attack and Mach number. The modeled and training aerodynamic coefficients show good agreement. This method shows excellent potential for rapid development of aerodynamic models for flight simulation.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2002-01-01
Publisher Institution Proc. of FLAIRS 2002