FORT EUSTIS, Va. – U.S. Army aviation experts are asking industry for new enabling technologies in artificial intelligence (AI) and machine learning to monitor, diagnose, and predict the health and performance of engines and propulsion of vertical-takeoff-and-landing (VTOL) aircraft like helicopters.
Officials of the U.S. Army Aviation Applied Technology Directorate at Fort Eustis, Va., issued a broad agency announcement (W911W625R0006) on Thursday for the Aviation and Missile Research and Development project.
Although this initiative primarily concerns VTOL aircraft and helicopter engines and propulsion technologies, the project also concerns flight electronics that involve AI, machine learning, uncrewed helicopters, and new manufacturing processes for aviation components.
The project has a topic called artificial intelligence (AI) and machine learning for drive system prognostics for Army crewed and uncrewed helicopter drive systems.
Cutting maintenance costs
Operation and sustainment accounts for roughly 70 percent of the total life cycle cost of rotorcraft, with maintenance of drive systems being one of the top cost drivers, Army researchers explain. This can involve early removal of components well before failure.
Although condition indicators from today's helicopter health-management systems enable some capability to predict failure, these systems often require decades of usage data to create a useful tool for maintenance forecasting. Future helicopters, moreover, will not have access to past failure data for condition indicators, leading to overly conservative inspection intervals for drive components early in the aircraft life cycle.
Army researchers are looking for AI models to predict helicopter drive system failures and eliminate overly conservative inspection intervals. Army helicopter experts are asking industry to develop AI models that can integrate physics-based models with actual usage data to predict failures for new aircraft.
Challenges include determining appropriate AI algorithms; collecting the right data for model training to determine appropriate sensor and usage data; and validating the accuracy of failure forecasting by combining physics-based and AI models to demonstrate failure forecasting accuracy against information about known failures.
Tell me more about artificial intelligence (AI) in helicopter engine prognostics ...
- Artificial intelligence (AI) in helicopter engine prognostics involves using machine learning algorithms and data analytics to predict engine failures or maintenance needs before they occur. By analyzing vast amounts of sensor data, AI can detect subtle patterns or anomalies that indicate potential issues, such as wear or fatigue in components. These predictive models help optimize maintenance schedules, reducing downtime and costs while enhancing safety. AI can also improve decision-making by providing real-time insights into engine health, identifying critical failures before they cause significant damage. Over time, AI systems learn from accumulated data, improving their predictions and adapting to new conditions, leading to more reliable and efficient helicopter operations.
Goals are to reduce life cycle costs and increase aircraft availability by eliminating conservative maintenance intervals, which can lead to the unnecessary removal and replacement of helicopter engine and drive components that still may have sufficient time before failures.
This solicitation also has a topic named Advanced Manufacturing to Improve Turbine Performance of crewed and uncrewed helicopter engines, which seeks advances in turboshaft engine turbine components, which today are manufactured through a traditional process of investment casting, forging, and computer numerical control (CNC) machining.
These manufacturing processes limit the ability to fabricate new designs that could improve engine efficiency and enhance cooling. Additive manufacturing -- also called 3D printing -- could enable fabrication of complex airfoil shapes, but these manufacturing technologies still need work. Today's turbine cooling circuit design, moreover, has manufacturing limits on complex geometries.
The Army is looking for advanced manufacturing technologies to improve turbine blade aerodynamics and internal cooling circuits by optimizing flow path and cooling flow characteristics, and by developing new manufacturing that blends complex cooling passages.
System-level performance
Work is expected to include component rig and engine demonstration of a notional propulsion system, and the ability to assess system-level performance for future helicopters.
Anticipated benefits could be reduced fuel consumption due to improved turbine power and blade aerodynamics, and improved power-to-weight ratios by reducing secondary cooling.
Otherwise, this project involves topological optimization for additively manufactured corrosion resistant gearbox housings; lightweight emergency loss of lubrication system; non-press fit or splined inner/outer race fully ceramic bearings in gearboxes; topological optimization of dynamic drive system components; lightweight and high strength compressor materials; optimized compressor aerodynamics; advanced combustion systems; manned and unmanned army rotorcraft engine systems; high temperature tolerant turbine materials and durability improvements; advanced manufacturing to improve turbine performance; and hybrid VTOL.
Companies interested should email white papers no later than 3 June 2027 to the Army's Devon Wolfe at [email protected]. Those submitting promising white papers may be invited to submit full proposals.
Email questions or concerns to the Army's Laurie Pierce at [email protected] or Devon Wolfe at [email protected]. More information is online at https://sam.gov/workspace/contract/opp/4c8bb6759e8144238ba3d006717ee103/view.
