Corvid Innovation LLC — Department of Defense SBIR Phase I: AF161-042

Description

ABSTRACT: Shortcomings, including high costs and lack of realism, associated with classical approaches in aircrew training have motivated the desire to develop a virtual aerial refueling training environment.Such an environment has the potential to provide a cost-effective, realistic environment to perform repetition training.A vital facet of such a training tool is the development of a realistic aircraft aerodynamics model for tanker aircraft, drogue, and receiver aircraft.Accurately modeling the aerodynamics of these components is a necessary first step toward a successful simulation environment.As a step toward enhancing the aircrew training simulations Corvid Innovation proposes a unique, innovative, and simple toolset for building aircraft aerodynamics models.Leveraging well established practices at Corvid, high-fidelity computational fluid dynamics, in-house HPC systems, and in-house aerodynamic model development tools Corvid will provide an incremental advance in our current methodologies that will enable the proper characterization of a receiver aircraft in the presence of a tanker wake.Tanker aerodynamics and wake generation will also be demonstrated along with drogue response.These aeromodels will be demonstrated through the use of fast-running simulation tools which will serve as a first step in the development of a simulation environment for aircrew training.; BENEFIT: There are a number of paths for commercializing a high-fidelity, fast-running, capability for predicting aircraft response in a dynamic flowfield.The most notable include:1. Formation Flight2. Aerial UAV Refueling (private sector)3. Small UAV Refueling4. Small UAV Gust Response5. Enhanced Aircraft Spacing ProtocolsThe commonality in each of these examples is a need to accurately model the effects of flowfield disturbances. The methodologies established throughout the Phase I and II efforts will be directly applicable in improving the industries capability of i) predicting aircraft responses in a simulated operational environment and ii) improving control algorithms for aircraft under a complex spatially varying flowfield.