Intelligent Agents for Improved Ground-Test Operations, Phase I

The proposal outlines a research program for developing a novel soft-computing technology composed of an Artificial Immune System and Bayesian Belief Networks for monitoring, knowledge and information processing, and decision support infrastructure for testing of rocket engines of future spacecrafts. An engine failure during test operations may cause catastrophic results for the test article, supporting infrastructure, and potentially to supporting resources. The ability to effectively monitor health and status of a testbed and test article during a test firing and to take corrective measures for peak performance and improving reliable operations is of utmost importance. Using an existing SR-30 Jet Engine testbed utilizing artificial intelligence control techniques, novel algorithms are being developed by The University of Alabama that can function under faulty sensor conditions, can work effectively with thousands of sensors at high data rates, are self-checking, and are self-correcting can be of further support. The proposed effort builds on an existing joint MSFC/University of Alabama project where NASA's NSSTC is only funding graduate student research. This proposal will further enhance technology research for realization and synthesis of needed advanced algorithms in larger scale test platforms for propulsion health management, thereby furthering educational research addressing NASA's education missions.

This is a mature technology (TRL 7+) — the realistic path in is usually NASA's Technology Transfer Program: licensing an existing NASA patent, or a Space Act Agreement to use NASA facilities/expertise directly. NASA also runs a startup licensing program with no upfront fee for companies formed to commercialize a specific NASA technology.

None of these are guaranteed paths for this specific project — TechPort itself doesn't have an "apply" button. Reaching out to the contact(s) above with a specific question is usually the fastest way to find out what's actually open.