RotorGen2 Minimal-Complexity Helicopter Math Models Home

Robert Heffley Engineering has applied RotorGen2 minimal-complexity models to a variety of helicopters.

• Minimal-complexity air-vehicle modeling is based on a scheme outlined in an Army-sponsored study more than 20 years ago (Manudyne Report 83-2-3).

• The original concept was to devise a helicopter math model that embodies the first-principles of rotary-wing aerodynamics while requiring a minimum of model parameters and computing power.

• The main objectives were to minimize computational delay (frame time), to simplify adaptation to a specific aircraft, and to minimize the engineering labor in implementation and checkout.

• RotorGen was an implementation of the original minimal-complexity model applied to a series of handling-qualities studies by the U.S. Army on the NASA Ames VMS large-amplitude motion simulator facility.

• RotorGen2 is a second-generation development of the original "minimal-complexity" math model form.

• The advantages of RotorGen2 is its implementation in Matlab and Simulink form, its capability to run in the TPV modeling environment, and the array of RHE development tools for adapting RotorGen2 to specific rotary-wing vehicles.

• Because RotorGen2 depends on analytic functions to describe aerodynamics, it can be molded to match a minimal amount of available data, much of which may be basic geometrical and mass data.

• RotorGen2 provides a simulation solution for many applications requiring basic helicopter dynamical modes without higher-order/high-frequency blade dynamics.

• RotorGen2 provides correct non-linear behavior over a large range of airspeeds and wing-body aerodynamics for the full range of angle of attack and sideslip.

• While the basic RotorGen2 model models only the normal working state, it can be augmented to include the autorotation (windmill) state.

• Other effects can be added, including higher-order tip-path-plane dynamics, ground effect, ground contact, blade stall onset, etc.

• RotorGen2 provides the user with control over the tradeoff between complexity and model accuracy, hence, engineering cost, computer performance, and simulator fidelity.