Tecplot Simulation Software Feature Image

Surrogate model showing the difference in drag values between two different system models created using Tecplot Chorus. Image: Ryan Starkey, University of Colorado, Boulder

An unmanned aerial vehicle (UAV) that will fly at speeds approaching Mach 1.4—faster than anything in the sub-50-kg vehicle category today—using an engine two to four times more efficient than any other in its class is under development by researchers at the University of Colorado, Boulder, through a university startup Starcor. The prototype is expected to be ready within a year.

Potential applications are wide ranging, such as flying into storms or hurricanes (subsonic); reaching dangerous, long-distance locations more quickly on military missions; and flight test beds for testing aircraft technology. When it goes into commercial production, the vehicle is expected to cost between $50,000 and $100,000, which is relatively low compared to the test vehicles in use today.

At the heart of the UAV is the smallest supersonic jet engine developed to date. The goal is to deliver a high-efficiency, lubrication-free turbojet engine that weighs about 22 lbs and uses a custom-designed afterburner and an innovative fluidic thrust vectoring control system. The UAV frame will be compact, as well, measuring seven or eight feet long and about six feet wide.

Achieving Mach 1.4 flight requires intensive computational fluid dynamics (CFD) simulation, optimization, and analysis.

The research group used several tools to design and optimize the airframe, made of a lightweight composite skin and bulkhead reinforcement. Specifically, they used the Ansys finite element method (FEM) to predict maximum loading conditions; Ansys Fluent and Zona Technology’s Zeus CFD solvers to validate the aerodynamics; and Tecplot Chorus to visualize, analyze, and optimize the results.

In addition to executing the traditional post-processing tasks offered by most CFD visualization tools, Tecplot Chorus also allowed the team to effectively manage the massive amounts of data required to optimize the airframe for supersonic flight. They were able quickly generate optimal designs from multifidelity runs using Chorus to analyze the results using surrogate modeling for sensitivity analysis, problem space understanding, and reduction of optimization computational expenses.

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