IPPW-10

IPPW-10

Royal Observatory of Belgium

18 June 2013

von Karman Institute for Fluid Dynamics

Mars atmosphere reconstructionusing FADS on ExoMars EDM

Bart Van HoveÖzgür Karatekin

Royal Observatory of BelgiumRinglaan 3, Brussel [email protected]

10th International PlanetaryProbe Workshop

San Jose, CA, US18 June 2013



Why is reconstruction important?

Mars is the ultimate test facility:learn from one mission to design the next

Trajectory reconstruction

Vehicle response: deceleration, velocity, attitude

Atmosphere reconstruction

• What environment produces the vehicle response?• Constrain environment to validate ground predictions• Constrain Mars climate models

• ExoMars EDL Demonstrator Module (EDM) will land during the 2016 Mars dust season

Viking

MSL

Pathfinder

IPPW-1018 June 20132



Reconstructing from conventional instrumentation

Inertial Measurement Unit = IMU

Positional measurements by accelerometers and gyroscopes

Estimate atmosphere from trajectory reconstruction:

Density from acceleration and drag coefficient

Pressure from hydrostatic equilibrium

Temperature from ideal gas law

Atmospheric reconstruction starts from density from drag equation:

acceleration aerodynamic drag coefficient true air speed




Reconstructing from heat shield instrumentation

Flush Air Data System = FADS

Installed on MSL and ExoMars EDM 2016:heat shield surface

pressure sensorsFlow field measurements include atmospheric motion

FADSsolver

IMU trajectory

FADS pressure signals

Surface pressure model

Relation to free stream

True air speed

Atmospheric density

Atmospheric winds


Does not require aerodynamic drag model: could instead be validated



Presentation overview

1. Toolkit development: EDL simulation and reconstruction tools

2. ExoMars EDM 2016: preliminary study reconstruction uncertainty


Atmospheric reconstruction at high velocities, before parachute opening



Toolkit development

Reconstruction tool + Monte Carlo

• Validated in Mars Phoenix case study(public IMU flight data set)

• Matches independent reconstructions

• Monte Carlo with time varying bias and noise errors

6-DOF entry simulator

• To produce flight data (IMU and FADS)

• No parachute phase, no guidance

• Validated in collaboration NASA Langley


units [Desai et al. 2] This study

Time since entry (s) 227.8 227.8

Geocentric altitude (km) 13.3 13.55

Relative velocity (m/s) 387.6 391

Total angle of attack (deg) 4.73 4.82

Match reconstruction at parachute deployment



ExoMars EDM 2016: preliminary reconstruction study


• Solar longitude: 244.8 ° in dust season

• Unguided ballistic entry

Mars entry interface

Altitude: 120 km Longitude: 344.6 ° Latitude: 4.5 °Speed: 5.9 km/s Flight path: -12.7 ° Azimuth: 124 °

IMU & FADS instrumentation

Sampling frequencies:

• IMU 100 Hz

• FADS 10 Hz

Preliminary uncertainty estimatesExoMars EDM 2016 entry vehicle



ExoMars EDM: 6-DOF simulation


Simulated up to parachute opening at Mach 2

• MCD (Mars Climate Database) dust storm scenario predicted by LMD GCM [Forget et al.]

• Reproduces expected flight behavior

→ Let’s reconstruct from the synthetic flight data

Δt = 2 msESA aero v5.12

MCD 5.0 dust



ExoMars EDM: impact of assumptions in IMU atmosphere reconstruction


Typical error sources in IMU reconstruction

1. Start from “true” MCD density profile provided to 500 Hz simulation (dust storm scenario)

2. Reconstruct at 100 Hz like the ExoMars IMU3. Assume pure CO2 instead of actual gas mixture

4. Neglect winds since IMU can’t resolve those5. Incorrectly model the drag coefficient (+ 3-σ)

ConclusionNeglecting wind can be as detrimentalas mispredicting the drag coefficient!

𝜌∞ (h )=2𝑚 ·|�⃗�|

𝐶𝐷 · 𝐴𝑟𝑒𝑓 ·𝑉 ∞2



Strong winds along EDM trajectory: especially during dust storms

IPPW-1018 June 201310

MCD 5.0 (Mars Climate Database) wind profiles

• EDM will fly along the equator

• Seen from the Mars surface:strong high altitude retrograde wind

• Seen from space: atmosphere lags behind rigid planet rotation

• Caused by migrating thermal tides due to solar heating

Relative velocity and atmospheric density from IMU data are uncorrected for winds

Could we improve density reconstruction by estimating winds from FADS?



ExoMars EDM: FADS reconstruction methodology

IPPW-1018 June 2013

Rebuild oncoming flow from heat shield instrumentation

1. Surface pressure distribution modelstagnation pressure and flow angles

2. Shock wave pressure ratio

3. Derive density from dynamic pressureair speed from IMU (neglecting winds)or FADS (corrected with wind estimate)

𝒒∞=𝝆∞𝑽 ∞

𝟐

𝟐

?

angle of attack αside slip angle β

stagnationpressure

pt2

𝑽 ∞𝟐

correctedfor winds?

11

surface pressure




IPPW-1018 June 2013



stagnationpressure

pt2

1. Surface pressure distribution model

• Modified Newtonian flow model:

• Non-linear least-squares solver

• Solve for and flow angles from

• Fix atmospheric pressure from IMU

(errors on have small impact since )

• True flow angles: sensitive to wind velocity

with

12




IPPW-1018 June 2013



stagnationpressure

pt2

2. Shock wave pressure ratio

• Normal shock wave conservation equations

• VKI Mutation library: high temperature gas properties for 5-species CO2 [Magin et al. 2009]

• 1-D flow solver to build a database of

• High temperature effects are main approximation inanalytical cold gas relations – up to 5% difference!

𝒒∞

13




IPPW-1018 June 2013


𝒒∞=𝝆∞𝑽 ∞

𝟐

𝟐

3. Derive density from dynamic pressure

• Air speed from IMU (no winds) or corrected using FADS wind velocity estimates

• Wind estimate from true FADS flow angles

• Non-linear LSQ solver neglecting vertical wind

14



Monte Carlo: FADS reconstruction vs. IMU

IPPW-1018 June 2013

Conclusion

Poor wind estimate increases uncertaintyBoth FADS and IMU over 5% uncertainty on

• IMU density estimate: large bias error (neglects winds)• FADS with IMU air speed: similar to IMU < 80 km• FADS with corrected air speed: wind estimate too noisy• Limited benefit from additional sensors

15

MCD 5.0 dust



ExoMars EDM: horizontal wind estimate from FADS

IPPW-1018 June 2013

Along the entire entry trajectory the wind estimation error ≥ wind amplitude

16



ExoMars EDM: uncertainties for different weather scenarios

IPPW-1018 June 201317

Dust storms strongly affect atmospheric profiles: detectable below 80 km



Hypothetical FADS pressure sensors to resolve winds

IPPW-1018 June 201318

Imagine very accurate pressure sensors over the entire (high velocity) trajectory

Density bias error reduced: especially at high altitudes where winds are strongest



Hypothetical FADS pressure sensors to resolve winds

IPPW-1018 June 201319



Summary & Conclusions

• Approximate general FADS solver: modified Newtonian 1-D flow

• Winds can affect air speed and density estimate

• Currently difficult to resolve from FADS pressure measurements

• Additional low range pressure sensors

• Atmospheric reconstruction sufficiently accurate to detect dust storms

IPPW-1018 June 201320



Future work

IPPW-1018 June 2013

• Coupled FADS-IMU reconstruction using Kalman filtering

• FADS reconstruction based on CFD

• Consider other Mars missions

• Consider Doppler analysis of radio communications

• Simulate parachute phase (multi-body model under development)

21



Acknowledgements

François Forget – Université Paris

Jeffrey Herath / Juan Cruz / Daniel Litton – NASA Langley Research Center

Paul Withers – Boston University

IPPW-1018 June 2013

ESA Education IPPW student support

project support

22



References

IPPW-1018 June 201323



ExoMars EDM: is a dust storm detectable?

IPPW-1018 June 2013

Dust storm impact density profile very strongly: should be detectable above 20 km

24



Impact of approximations on IMU temperature reconstruction

IPPW-1018 June 201325



Aerodynamic drag uncertainty

IPPW-1018 June 201326



FADS normal shock wave pressure ratio

IPPW-1018 June 2013

Discrepancy up to 7% with ideal (cold) gas approximation

27



Toolkit development – 6-DOF entry simulator

Simulate entry to synthesize IMU and FADS flight data

• Impact of atmospheric conditions, wind velocities…• Planet, aerodynamics, gravity, vehicle models• No parachute phase or real-time flight control systems• Validated in collaboration with NASA Langley

IPPW-1018 June 201328



Toolkit development – Reconstruction and Monte Carlo

IPPW-1018 June 2013

Integrate IMU data to reconstruct entry profiles

• Validated in our Mars Phoenix case study(only IMU available)

• Good match with independent reconstructions• Monte Carlo with time varying bias and noise

errors of various types and distributions

units [Desai et al. 2] This study

Time since entry (s) 227.8 227.8

Geocentric altitude (km) 13.3 13.55

Relative velocity (m/s) 387.6 391

Total angle of attack (deg) 4.73 4.82

Match reconstruction at parachute deployment

29

IPPW-10

Documents

Transcript of IPPW-10