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Multi-Disciplinary Optimisation for Planetary Entry, Descent and Landing Systems

Multi-Disciplinary Optimisation for Planetary Entry, Descent and Landing Systems. David Riley Deimos Space Ltd., UK Davide Bonetti Deimos Space S.L.U., Spain. Table of Contents. Introduction: The Problem Domain The Optimisation Process Tools for Numerical Trade-offs

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Multi-Disciplinary Optimisation for Planetary Entry, Descent and Landing Systems

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  1. Multi-Disciplinary Optimisation for Planetary Entry, Descent and Landing Systems David Riley Deimos Space Ltd., UK Davide Bonetti Deimos Space S.L.U., Spain

  2. Table of Contents • Introduction: The Problem Domain • The Optimisation Process • Tools for Numerical Trade-offs • Optimization results • Conclusion

  3. INTRODUCTION: THE PROBLEM DOMAIN

  4. The problem • Top-level requirement • Bring a spacecraft safely to rest on the surface of another planet • Challenges: Mars • Entry velocity ~ 3 km/s • Very thin atmosphere • equivalent to 35 km altitude on Earth • Gravity around 1/3 of Earth’s • Want to maximise the payload delivered to the surface • Subject to constraints in cost, risk, geographical sourcing, development timescale… Image: NASA/JPL

  5. Entry, Descent and Landing Systems • Entry: aerodynamic deceleration using a heatshield • Deploy a parachute to slow down faster • Retro-rockets for final slowdown and fine control • Airbags for touchdown

  6. Entry, Descent and Landing Systems • Some parts are common: • Heatshield for entry • Parachute for deceleration • Others vary: • One vs two parachute stages • Whether to have a retro stage • Touchdown on airbags,landing legs, crushables…

  7. Driving requirements • Mass • Height loss and verticalisation • Thermo-mechanical loads • Landing site accuracy • Volume • Reliability / robustness • Cost • Development timescale

  8. Trade-offs Smaller parachute / bigger retros: • Lighter parachute system • Final speed is higher • More retro fuel required Single-stage vs two-stage parachute: • For low final speeds, two-stage generally is lower mass • Two-stage may take morealtitude to reach final speed • Single-stage is simpler

  9. Optimisation problem • Problem includes discrete choices, integer values and continuous values • Multi-disciplinary approach required • Parachute design, retro system design, airbag design, trajectory, … • Can’t split the problem into separate pieces - different parts of the design affect each other • Speed reached under heatshield must be safe for parachute deployment • Each stage must carry the subsequent stages • e.g. parachute must slow down the retro fuel and airbags as well as the payload • Need the trajectory to be self-consistent • Test through simulation: enough time and altitude to stop before hitting ground

  10. High Precision Landers: overview • Mission concept • Technology Research Study within the ESA MREP (Mars Robotic Exploration Preparatory) Programme, preliminary to Mars Sample Return • Rover and return vehicle must land near each other • Target is launch in late 2020’s • Main study objective • Design an optimum and robust EDL/GNC configuration for MSR lander • Project led by Airbus Defence & Space (Les Mureaux) • Main requirements • Achieve landing accuracy of at least 10 km, ideally high precision (3 km) or even pinpoint (100 m) • Mass at entry ~ 800 kg • Use European technology as far as possible Images: NASA/JPL

  11. Small Mars Landers: overview • Mission concept • Technology Research Study within the ESA MREP (Mars Robotic Exploration Preparatory) Programme, preliminary to INSPIRE • Target is launch in 2026 or 2028 • Main study objective • Design an optimum and robust EDL/GNC configuration for a Mars Network Science mission • Project led by Deimos Space (Spain) • Main requirements • Three identical probes separating from the same carrier and landing in different sites • Each probe mass at entry ~350 kg • Payload: 130 kg, 1150x355 mm (cylinder) • Use European technology as far as possible Image: ESA

  12. Small Mars Landers: overview • Challenges of a multi-probe mission • Several studies, only one launched (crashed) • Target entry mass quite different to previous single or multiple landers solutions • Wide environment variability • Triple landing site, identical probes • Altitude below 0 MOLA, -15º< Latitude < 30º • Global atmosphere models based on statistics of European Mars Climate Database calls Global model One site

  13. THE OPTIMISATION PROCESS

  14. Overall Process

  15. Overall Process

  16. Preliminary EDL & GNC Trade-offs

  17. 3 Configurations selected for ESAT numerical trade-offs ROBUST TYPE 1 MER-LIKE TYPE 2 MPF-LIKE TYPE3 SIMPLE Beagle2-LIKE (no lowering)

  18. Overall Process

  19. Overall Process ESAT: EDL&GNC Sizing and Analysis Tool

  20. TOOLS FOR NUMERICAL TRADE-OFFS

  21. ESAT Architecture ESAT relies on a modular and generic infrastructure. Users concentrate efforts on Settings and Wrapper Function to solve a given problem. External module:- multidisciplinary - single discipline EDL&GNC Sizing and AnalysisTool

  22. MDO Architecture Overall it is a complex problem: • Multiple phases • Multiple combinations of worst cases (aerodynamics, atmosphere, events…) => robust solutions SIMULATION CORE C: Coasting E: Entry D: Descent L: Landing P/L: Payload Qdyn: Dynamic pressure FS: Frontshield Vt: terminal V NSP: Network Science Probe Different forType 1, 2 and 3

  23. Bi-level MDO Architecture • Bi-level surrogate models with internal optimization • Level1: MDO (mission & system objectives) • Level2: Coasting, D&L (nested in level 1) • Achieve efficient optimizations PESDO (TESSELLA)

  24. OPTIMIZATION RESULTS

  25. Problem 1: Probeseparationtiming • 3 probes, want to optimise the separation events Coasting and separation overview => simplified ESAT example => ESAT wrapper for Small Mars Landers

  26. Problem 1: Probeseparationtiming • Look at results for sites 1 and 3 Pareto Front: Dominant solutions EIP FPA dispersion Fuel mass for retargeting

  27. Problem 2: Minimisationof EDLS Mass • EDL&GNC Robust Optimization Problem • 7 variable dimensions covering EDL, GNC and environment aspects • Minimize system mass • Maximize altitude at parachute dep.

  28. Problem 2: Minimisation of EDLS Mass • Configurations selection process: Query and Filtering • More than 100 performances have been managed in 7 dimensions with ESAT • 2D slices / N-dim data mining • Statistics of 50000 different samples

  29. Problem 2: Minimisation of EDLS Mass • Pareto Frontier: optimum solutions, = chosen one • Predicted and validated Pareto frontiers match with very good accuracy TYPE 2 (mass includes system margins)

  30. Optimum configurations comparison • Overall summary

  31. Configurations selected • Type 1&2: Mass budget and Sizes (mass includes system margins) Type 1: winds < 21.3 m/s P/L = 32.2% of Total Type 1:BC ~ 73 kg/m2 kg P/L = 29.6% of Total Type 2: BC ~ 79 kg/m2 Type 2: Winds < 13 m/s kg

  32. Configurations selected • Type 1&2: GNC solutions • Type 1: Modes • Events (blue) • Sensors (grey) • State vector (red)

  33. CONCLUSIONS

  34. Conclusions: Mars Landers • Identified optimal landing site sequences, configurations and component trade-offs for the Small Mars Landers project • Robustness is critical for a network Mars landers mission • Higher GNC complexity is the price of adding flexibility to site selection • Vision based navigation • Lateral control • ESAT allows the System, Mission and GNC engineers to perform high-fidelity EDL-GNC architecture trade-offs relaying on high-fidelity and end-to-end approach. It increases the reliability of the selected design solutions with a reduced number of iteration loops (number and extent)

  35. Conclusions: Design Optimisation Tools • We have found the approach adds a lot to EDLS optimisation • Need to choose an appropriate level of optimisation based on the level of fidelity of the model • No point fine-tuning something that exists in the model, not in reality • Surrogate modelling tools like ESAT are valuable when the models are complicated, slow to run • Multi-disciplinary approach is vital – we’ve got as far as we can with tuning each component separately, choosing handover conditions by guesswork • Mathematical optimisation is effectively “yet another discipline” • Important to add appropriate support not just extra complication • ESAT is a useable front-end to a sophisticated optimisation approach • Allows the user to focus on creating the “wrapper function”

  36. Acknowledgements • Deimos Space (Spain) • Gabriele De Zaiacomo, Irene Pontijas Fuentes, Rodrigo Haya Ramos, Gabriele Bellei, Jordi FreixaMallol • Airbus Defence and Space • TimotheeVerwaerde, AurélienPisseloup, Cédric Renault • European Space Agency • Eric Bornschlegl, Kelly Geelen, Alvaro Martinez Barrio, Thomas Voirin

  37. Thank you!

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