Trajectory and performance of launch vehicles are significantly affected by various flight parameters related to propulsion, aerodynamic forces, mass, center of gravity, etc. Precise estimation of these parameters during post-flight analysis is essential for continuous performance improvement of launch vehicles. Since a large number of parameters affect the performance of a launch vehicle, the estimation process becomes computationally complex and time consuming. Estimation of these parameters can be automated by using 6D simulation software which works in tandem with a heuristic method. In this study, an integrated computational procedure has been designed, which employs a 6D simulation software and a genetic algorithm-based optimizer, to estimate the input flight parameters from the actual flight data. The parameters to be estimated, population size, parameter bounds and genetic algorithm parameters such as crossover probability and mutation probability are taken as inputs to the estimation program along with flight trajectory profiles. The objective function to be minimized is derived by computing the squared error between the actual flight trajectory profiles and the simulated trajectory profiles. Since flight measured data contains noise, data smoothing techniques are implemented before usage to avoid trap down in local minima during optimization. Further, to reduce computational time, a relation matrix between input parameters and output flight profiles is devised to intelligently prioritize the parameter estimation. This technique is demonstrated against known input parameter variations and the estimated outputs are found to be very effective.


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    Title :

    An Automated Flight Parameter Estimatıon Technıque Using Genetic Algorithm


    Additional title:

    Lect.Notes Mechanical Engineering


    Contributors:
    Saran, V. H. (editor) / Misra, Rakesh Kumar (editor) / Anand, Nikhil (author) / Sabarinath, A. (author) / Mettu, Ramesh (author) / Mohan, Chindu (author) / Nisha, S. (author) / Geetha, S. (author) / Ayyappan, G. (author)


    Publication date :

    2021-01-24


    Size :

    8 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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