Validation of a Modified Parallel Compressor Model for Prediction of the Effects of Inlet Swirl on Compressor Performance and Operability

Engine inlet distortion complications have plagued the turbine engine development community for decades, and engineers have developed countless methods to identify and combat the harmful effects of inlet distortion. One such type of distortion that has gained much attention in recent years is known as inlet swirl, which results in a significant flow angularity at the face of the engine. This flow angularity can affect the pressure rise and flow capacity of the fan or compressor, and subsequently affect compressor and engine performance. Previous modeling and simulation efforts to predict the effect inlet swirl can have on fan and compressor performance have made great strides, yet still leave a lot to be desired. In particular, a one-dimensional parallel compressor model called DYNTECC (Dynamic Turbine Engine Compressor Code) has been used to analyze the effects of inlet swirl on fan and performance operability of the Honeywell F109 turbofan engine. However, when compared to experimental swirl data gathered at the United States Air Force Academy (USAFA), the model predictions were found to be inaccurate. This paper documents work done to compare the initial predictions generated by DYNTECC to the latest set of experimental swirl data, analyze the potential shortcomings of the initial model, and modify the existing model to more accurately reflect test data. Extensive work was completed to create a methodology that can calibrate the model to existing clean inlet fan map data. In addition, an in depth study of fan/compressor stalling criteria was conducted, and the model was modified to use an alternate stalling criteria that more accurately predicted the point of stall for various swirl inlet conditions. The prediction of the fan stall pressure ratio for all inlet swirl conditions tested is within 2% of the ground test stall point at the same referred fan speed and referred mass flow.