Simulation of an INVERSE JET ENGINE MODEL for performance prediction and fault diagnosis in transient operation Project by Inbal Srebro Levin Dmitry Control & Robotics Lab Supervisor Dr. Michael Lichtsinder

Motivation finding a new and cheaper way to constructing engine maps Finding a faster algorithm to simulate engine – as a first step towards real time simulation Proving the model is sensitive to error, as a first step towards error detection algorithm

Objectives Development of the Inverse Engine Model (IEM) Our goal is to evaluate the engine maps when not all the effective (realistic) map components are known: Compressor-Nozzle IEM, Turbine-Nozzle IEM Evaluation of Turbine/Compressor Map Each turbine/compressor in an engine has an operating map. Complete maps are based on experimental data or predicted by special computer programs. Our goal is to write the program which will be able to predict operating maps. Fast simulation (and control) Using IEM algorithm to produce MATLAB simulation instrument for map evaluation

Single Spool engine station

Turbine map Each turbine/compressor in an engine has an operating map. Complete maps are based on experimental data or predicted by special computer programs The typical turbine map is shown in the picture below. The x-axis of the turbine map is pressure ratio(PR). The other axis is the corrected flow and the z-axis is efficiency : Evaluated points from data Lines of

Compressor map

Inverse Engine Model (IEM) Inverse Engine Model Command: Program Output:

CN-Shortened Inverse Engine Model (without Turbine Map) Closed Loop Engine CN-Inverse Model Maps: Compressor+Nozzle

Conditions for construction (short Inverse Model) Inverse jet Engine Model VALin nVALout MAPin nMAPout For CN short Inverse Model:

Advantages of IEM May be built when not all engine components can be measured CPU time decrease compare to conventional model May be used for fast calculation of steady-state and transient performance of single spool engine, and is a step towards real time simulation

MATLAB algorithm (flowchart)

Simulation results: Input Input to engine (command) Measure data from transducer

Simulation result: Turbine map parameters

Simulation result: Turbine map Two dimensional plot of turbine map, with original contour lines (got from the manufactory) Evaluated data Data from full conventional model

3D-graphs: Turbine map The slices

Evaluation of Turbine Map (Using Compressor and Nozzle maps)

mdot4cor Pi Evaluated data by Inverse engine model

Evaluation of Turbine Map (Using Compressor and Nozzle maps)

known data from full turbine map Evaluated data by Inverse engine model

Compressor Map Data Matrix (1500X4): Evaluation of Compressor Map

Sensitivity to bias error In future development, this model will be used for engine error diagnosis – and for that purpose it has to be sensitive to error: a small error in input should produce larger error in output. Assuming that single transducer faults could be present in the engine at any instance, let’s insert the following fault into the model: measurement bias of +1% for the fault transducer

Evaluation of Turbine parameters: bias error: +1% Error in sensor T05

Conclusions Shortened Inverse Engine Models can be used for engine component map evaluation using data acquisition during transient (or steady state) operation. The investigation shows that relatively small measured bias in the inverse model input leads to relatively significant output errors - Sensitivity. Only one strategy of evaluation is described. However, alternative shortened inverse engine models may be developed for different engine component map detection. Limitations: 1. Accuracy of the engine model 2. Measurement error (random and bias) problem

Conclusions (Evaluation of Turbine Map) The idea of evaluating compressor/turbine map which was described above is claimed. By evaluating the compressor or/and turbine map from inverse engine model(IEM) the data which before can be only given by the producer (got from long-lasting and very expensive experiments) of the engine can now be evaluated by just one computer and impressively short time.

Future Tasks Flexibility One flexible program for all Partly map simulations Noise in the system – modeling Updating system for being Noise Resistant