A Data-Driven Modeling Approach for Rotorcraft Store Separation

Despite advancements made in computational and experimental analysis approaches, achieving successful store separation from rotorcraft remains a highly rigorous task. Wind tunnel-based approaches for store separation modeling largely remain infeasible due to the challenges associated with scaling store wake interactions. Furthermore, while high-fidelity computational fluid dynamics (CFD) approaches are capable of closely matching rotorcraft flight test, the high computational expense of these approaches greatly limits the total number of CFD simulations which can be run. However, even with this sparse sampling obtained through CFD potentially terabytes of high-fidelity data is still generated for the flow field. The objective of this study is to determine the feasibility of leveraging this data for the derivation of meaningful surrogate models to the topic of rotorcraft store separation. In this study, two surrogate modeling approaches will be investigated for their ability to predict store surface pressure distributions as a store is launched from a rotorcraft in hover. To generate these surrogate models, three CFD simulations are completed while varying the propulsive force assigned to the store. The CFD simulations were completed using the High-Performance Computing Modernization Program Computational Research and Engineering Acquisition Tools and Environments Air Vehicles Helios code. Once the surrogate models had been generated, an additional CFD simulation with a new propulsive force was assigned to the store. The results of this validation indicated that while the POD surrogate model struggled to provide detailed predictions of store-distributed loads, mean load variations could be modeled well. Results further indicated that through leveraging the CNN-based surrogate model, a significant improvement in distributed load modeling could be obtained. It was further identified that once distributed loads were integrated both POD and CNN-based surrogate models provided a viable path for the generation of a trajectory prediction-based surrogate model for rotorcraft applications. Both POD and CNN-based surrogate models provided significantly reduced computational costs. For each rotorcraft store separation CFD simulation, the computational cost required 10 days of simulation time across 880 CPU cores. While using the surrogate model, comparable predictions could be produced in under three minutes on a single core.