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

A stochastic six-degree-of-freedom flight simulator for passively controlled high power rockets
A stochastic six-degree-of-freedom flight simulator for passively controlled high power rockets
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.
Rocket, Stochastic, Simulation, Flight, Unguided, Machine learning, Monte Carlo, 6DOF, Parachute, HPR rocket, stochastic, simulation, flight, unguided, machine learning, monte carlo, 6dof, parachute, hpr
0893-1321
31-45
Box, Simon
2bc3f3c9-514a-41b8-bd55-a8b34fd11113
Bishop, Christopher M.
75494b30-e719-4be9-84a2-f30752e1b72d
Hunt, Hugh
1bc35072-298e-4176-a922-151f4a71f5e4
Box, Simon
2bc3f3c9-514a-41b8-bd55-a8b34fd11113
Bishop, Christopher M.
75494b30-e719-4be9-84a2-f30752e1b72d
Hunt, Hugh
1bc35072-298e-4176-a922-151f4a71f5e4

Box, Simon, Bishop, Christopher M. and Hunt, Hugh (2011) 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).

Record type: Article

Abstract

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.

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e-pub ahead of print date: 3 March 2010
Published date: January 2011
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
Organisations: Civil Engineering & the Environment

Identifiers

Local EPrints ID: 73938
URI: http://eprints.soton.ac.uk/id/eprint/73938
ISSN: 0893-1321
PURE UUID: 5e4cca43-ea86-46d0-966c-11dd0b3145c5

Catalogue record

Date deposited: 11 Mar 2010
Last modified: 18 Jul 2017 23:48

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Contributors

Author: Simon Box
Author: Christopher M. Bishop
Author: Hugh Hunt

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