The Lockheed Martin F-35 Lightning II Sustainment program reduces life-cycle costs and increases the mission readiness of the F-35 fleet by minimizing downtime, supporting pilot training, and ensuring the availability of parts while avoiding unnecessary stockpiling. To achieve these goals, the program depends on accurate predictions of fleet performance, including projections of how long the aircraft will be grounded for service.
Lockheed Martin engineers used Simulink®, SimEvents®, Deep Learning Toolbox™, and MATLAB Parallel Server™ to model fleet performance and make predictions based on tens of thousands of simulations on a 256-worker computing cluster.
“With Simulink and SimEvents we created a model that incorporates data from the entire F-35 program and simulates thousands of aircraft operating every day, each with thousands of parts, at hundreds of locations over a span of many years,” says Justin Beales, project engineer at Lockheed Martin. “Accelerating thousands of Monte Carlo simulations on our cluster and then interpolating the results with Deep Learning Toolbox will save us years of processing time.”