A stochastic six-degree-of-freedom flight simulator for passively controlled high power rockets

Box, Simon, Bishop, Christopher M. and Hunt, Hugh (2010) A stochastic six-degree-of-freedom flight simulator for passively controlled high power rockets Journal of Aerospace Engineering, 24, (1), pp. 31-45. (doi:10.1061/(ASCE)AS.1943-5525.0000051).


[img] PDF boxetal11 (2).pdf - Author's Original
Download (638kB)


This paper presents a method for simulating the flight of a passively controlled rocket in six degrees of freedom, and the descent under parachute in three degrees of freedom, Also presented is a method for modelling the uncertainty in both the rocket dynamics and the atmospheric conditions using stochastic parameters and the Monte-Carlo method. Included within this we present a method for quantifying the uncertainty in the atmospheric conditions using historical atmospheric data. The core simulation algorithm is a numerical integration of the rocket's equations of motion using the Runge-Kutta-Fehlberg method. The position of the rocket's centre of mass is described using three dimensional Cartesian coordinates and the rocket's orientation is described using quaternions. Input parameters to the simulator are made stochastic by adding Gaussian noise. In the case of atmospheric parameters the variance of the noise is a function of altitude and noise at adjacent altitudes is correlated. The core simulation algorithm, with stochastic parameters, is run within a Monte Carlo wrapper to evaluate the overall uncertainty in the rocket's flight path. The results of a demonstration of the simulator, where it was used to predict the flight of real rocket, show the rocket landing within the 1 area predicted by the simulation. Also lateral acceleration during weather cocking, which was measured in the test, shows a strong correlation with simulated values.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1061/(ASCE)AS.1943-5525.0000051
ISSNs: 0893-1321 (print)
Keywords: Rocket, Stochastic, Simulation, Flight, Unguided, Machine learning, Monte Carlo, 6DOF, Parachute, HPR rocket, stochastic, simulation, flight, unguided, machine learning, monte carlo, 6dof, parachute, hpr
Subjects: T Technology > TL Motor vehicles. Aeronautics. Astronautics
ePrint ID: 73938
Date :
Date Event
3 March 2010e-pub ahead of print
January 2011Published
Date Deposited: 11 Mar 2010
Last Modified: 18 Apr 2017 20:48
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/73938

Actions (login required)

View Item View Item