Mars Lander Airbag System Study 2006

Analysis, Optimization and Probabilistic Assessment of an
Airbag Landing System for the ExoMars Space Mission


Richard Slade, Astrium UK Ltd, Earth Observation and Science Division, Stevenage, Hertfordshire, SG1 2A, UK
Paul Sharp2 and Royston Jones3, Altair Engineering Ltd, Vanguard Centre, Sir William Lyons Road, Coventry, CV4 7EZ, UK
Vassili Toropov4, Schools of Civil and Mechanical Engineering, University of Leeds, Leeds, LS2 9JT, UK

AIAA Papermarslander

MAO Presentation PPT

Astrium1 Video

Astrium2 Video

RI Video

Run2_s video

Abstract: Vented airbag systems offer an attractive means of cushioning the landing impact of
robotic planetary spacecraft. This type of airbag absorbs the impact kinetic energy by
exhausting the inflation gas through vent patches in a controlled way that aims to bring the
lander to rest with minimum rebound, limited deceleration and in an upright attitude. Such
systems are characterised by highly non-linear behaviour. This, coupled with the difficulty
of adequate terrestrial testing results in an analytical approach to design that relies on
explicit finite element (FE) analysis. However, the simulation of an impact of a few tenths of
a second duration typically requires tens of hours of CPU time, making it impractical to
optimise a design using a trial end error approach and to perform the large number of
analysis runs necessary for a probabilistic assessment of varied landing conditions. This
paper presents a methodology for overcoming these problems with reference to a vented
airbag design for the ESA ExoMars mission. The approach utilises the Moving Least
Squares Method (MLSM) to fit high quality approximations to multi-dimensional response
surfaces from a relatively small number of FE analysis runs. This method is well-adapted to
highly non-linear and noisy response surfaces that are typical for this problem. The
surrogate response surfaces were used to locate an optimum in the design parameter space
and to perform 10,000 sample point Monte Carlo runs in a probabilistic assessment of
reliability due to varying landing conditions.

About Rahul Ponginan

Technical Manager - Academic Program

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