Comparison of Data-Driven Approaches to Rotorcraft Store Separation Modeling

Abstract

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 computational fluid dynamics (CFD) simulations were completed while varying the propulsive force assigned to the store. Once the surrogate models were generated, an additional CFD simulation with a new propulsive force was assigned to the store. The results of this validation indicate that although the proper orthogonal decomposition (POD) surrogate model struggled to provide detailed predictions of store-distributed loads, mean load variations could be modeled well. The results further indicated that by leveraging the convolutional neural network (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 central processing unit cores. While using the surrogate model, comparable predictions could be produced in under 3 min on a single core.