Abstract

The Mk44 chain gun utilizes a custom-shaped spring, known as the Rounds Positioner Spring (RPS), to quickly translate rounds from its dual feed paths onto the bolt face. This component of the feed system is subject to two primary modes of failure: feed jam and spring fatigue. Both failures are heavily influenced by the spring’s shape. Optimization of the spring geometry is challenging because the system response is highly nonlinear and sensitive to the numerous parameters needed to describe the irregular spring geometry. Northrop Grumman has historically engineered system improvements using a traditional simulation-based trial-and-error approach. In this approach, engineers combine their judgment and experience with simulation results to iterate on potential design improvements. Despite this manual iteration approach’s tangible benefits, it is unlikely to achieve a true global optimization when applied to a system with multiple design parameters, competing constraints, and objectives. It is simply too complex for engineers to efficiently assimilate the nuanced relationships between the numerous variables for such systems. This presentation will discuss Northrop Grumman’s shift toward a more systematic approach to optimizing the Mk44’s RPS. In this approach, the engineering team fully automated the process of building models of the Mk44 feeder assembly in MSC Adams. They then used SmartUQ’s design of experiments (DOE) tools to prescribe the simulation runs needed for training an emulator (aka predictive model) of the physics-based Adams simulation. The emulator is shown to effectively predict system behavior for eight input variables and two critical analysis scenarios. Finally, the engineering team used the emulator in a nonlinear optimization algorithm to determine a spring shape optimized to reduce spring stress and propensity for feeder jams for multiple boundary conditions. The optimal design was then modeled in MSC Adams for validation and additional analysis. The emulator was also used in SmartUQ for further Uncertainty Quantification (UQ) analysis including sensitivity analysis and propagation of input uncertainties.