SIMOC

How do we build a Scalable Interactive Model of an Off-World Community?

We will, in the coming decades, establish ourselves in orbit around the Moon, on the surface of Mars, and in a more distant future, on the moons of Jupiter and Saturn. To get there we must learn how to sustain human life in hostile environments, with limited resupply. What balance of mechanical and biological systems will be required to sustain human life in a growing, off-world habitat?  

SIMOC is a novel integration of an agent-based software model, data from real-world plant physiology studies at NASA, and closed ecosystem studies at universities world-wide. It is a platform for research and citizen science, providing a robust Python server and web-based, educational interface. The objective was to find the minimum complexity required to sustain human life through a combination of physico-chemical (machine) and bioregenerative (plant) systems for long duration, off-world missions.

Key accomplishments

• SIMOC went live at the National Geographic Education Resource Library. Learn more.
• SIMOC completed development, from prototype to viable, scalable, reliable platform for world-wide citizen scientist engagement.
• Launched the SIMOC website, with on-line tutorials that prepare users for a first use of SIMOC.

• Hired NASA veteran curricula developer and built NGSS aligned teacher/student guide for grades 5-8 and 9-14, freely available from the SIMOC website.

• Conducted plant biology experiments at the University of Arizona Biosphere 2 to augment 30 years of NASA data on which SIMOC was built. Designed a physical Mars habitat analog built around the 1987 prototype for the Biosphere 2.

• With support from UA, Paragon, NASA, and Nat Geo, SIMOC raised its first funding for construction of the world’s highest fidelity Mars habitat analog.

• SIMOC is now in its fifth stage of development with an ever improving, dynamic model. The team will launch the next version with an ASU CS Capstone team to enable the display and capture of real-time data from environmental sensors.

Team members