AMAT is an open source collection of Python subroutines for rapid conceptual design of aerocapture and atmospheric Entry, Descent, and Landing (EDL) missions in a Jupyter environment. AMAT comes with a suite of tools to allow end-to-end conceptual design of aerocapture missions: launch vehicle performance calculator, database of interplanetary trajectories, atmosphere models, vehicle control techniques, and aeroheating models. AMAT supports analysis for all atmosphere-bearing Solar System destinations for both lift and drag modulation control techniques.
AMAT has been extensively used in various aerocapture mission studies at the Advanced Astrodynamics Concepts (AAC) research group at Purdue University in collaboration with the NASA Jet Propulsion Laboratory (JPL).
The lack of a rapid mission design tool for aerocapture mission concepts was identified by the NASA Ice Giants Pre Decadal (IGPD) Study led by JPL in 2016. This meant there was no quick way of performing architectural level assessments without resorting to resource intensive, subsystem-level design exercises such as the NASA Aerocapture Systems Analysis Team studies in 2004.
A team of researchers at Purdue University (Saikia et al.) led the aerocapture assessment studies in support of IGPD. Graduate researchers have since then further developed and extended the methods and tools for other atmosphere-bearing Solar System destinations. The focus was on developing an integrated systems engineering framework to allow mission designers to quickly evaluate the feasibility and performance of aerocapture mission concepts. Ye Lu and Athul P. Girija from the AAC research group conceptualized the aerocapture feasibility charts, now a commonly used graphical method for aerocapture mission design. An earlier version of the feasibility charts was presented by Saikia et al. in the IGPD study report.
Athul P. Girija formulated a systems framework for rapid conceptual design of aerocapture missions for his doctoral thesis. Much of the AMAT source code was originally written in support of his Ph.D. dissertation work. AMAT was first publicly released in November 2019, and has since then been maintained by the author at Purdue University. In the spirit of open code for open science, AMAT is free and open-source to foster universal access to the knowledge, and allow reproducibility of results by other researchers. Sugestions for improvement and potential contributions are greatly welcome.
Some things I would like to implement in the future:
Pairing AMAT with Blender and NASA 3D models of planets and spacecraft to produce high resolution renders of aerocapture vehicle trajectories.
Improved guidance schemes for lift and drag modulation aerocapture such as direct force control.
Improved support for EDL mission concepts in the areas of precision landing, parachute dynamics, terminal descent and landing phases.