In order to estimate the precise performance of the existing gas turbine engine, the component maps with more realistic performance characteristics are needed. Because the component maps are the engine manufacturer’s propriety obtained from very expensive experimental tests, they are not provided to the customers, generally. Therefore, because the engineers, who are working the performance simulation, have been mostly relying on component maps scaled from the similar existing maps, the accuracy of the performance analysis using the scaled maps may be relatively lower than that using the real component maps. Therefore, a component map generation method using experimental data and the genetic algorithms are newly proposed in this study. The engine test unit to be used for map generation has a free power turbine type small turboshaft engine. In order to generate the performance map for compressor of this engine, after obtaining engine performance data through experimental tests, and then the third order equations, which have relationships with the mass flow function, the pressure ratio, and the isentropic efficiency as to the engine rotational speed, were derived by using the genetic algorithms. A steady-state performance analysis was performed with the generated maps of the compressor by the commercial gas turbine performance analysis program GASTURB (Kurzke, 2001). In order to verify the proposed scheme, the experimental data for verification were compared with performance analysis results using traditional scaled component maps and performance analysis results using a generated compressor map by genetic algorithms (GAs). In comparison, it was found that the analysis results using the generated map by GAs were well agreed with experimental data. Therefore, it was confirmed that the component maps can be generated from the experimental data by using GAs and it may be considered that the more realistic component maps can be obtained if more various conditions and accurate sensors would be used.
In order to estimate the precise performance of the existing gas turbine engine, the component maps with more realistic performance characteristics are needed. Because the components maps are engine manufacturer’s propriety obtained from very expensive experimental tests, they are not provided to the customers, generally. Therefore, because the engineers, who are working the performance simulation, have been mostly relying on component maps scaled from the similar existing maps, the accuracy of the performance analysis using the scaled maps may be relatively lower than that using the real component maps. Therefore, a component map generation method using experimental data and the genetic algorithms are newly proposed in this study. The engine test unit to be used for map generation has a free power turbine type small turboshaft engine. In order to generate the performance map for components of this engine, after obtaining engine performance data through many experimental tests, and then the third order equations, which have relationships the mass flow function, the pressure ratio and the isentropic efficiency as to the engine rotational speed were derived by using the genetic algorithm. A steady-state performance analysis was performed with the generated maps of the compressor by the commercial gas turbine performance analysis program GASTURB (Kruzke, 2001). In order to verify predominance of the proposed scheme, the performance analysis results using the maps obtained by this study were compared with those using the compressor map provided by the engine manufacturer and the scaled turbine maps obtained from the GASTURB, as well as experimental results. In comparison, it was found that the component maps can be generated from the experimental test data by using the genetic algorithms, and it was confirmed that the analysis results using the generated maps were very similar to those using the scaled maps from the GASTURB.
In this study, in order to facilitate application of the NNs as well as to provide user‐friendly conditions, a performance diagnostic computer code using MATLAB® was newly proposed. As a result, not only more precise and prompt analysis results can be obtained due to use of the toolbox in MATLAB® on diagnosis and numerical analysis, but also the graphical user interface platform can be realized. The proposed engine diagnostics system is able to train the BPN with each fault pattern and then construct the total training network by assembling the trained BPNs. The database for network learning and test was constructed using a gas turbine performance simulation program. In order to investigate reliability on construction of the database for diagnostic results, an analysis is performed with five combination cases of 40 fault patterns. Finally, a diagnostic application example for the PT6A‐62 turboprop engine is performed using the trained network with the database, which represents the best diagnostic results among test sets.
Because aircraft gas turbine operates under various flight conditions that changes with altitude, flight velocity and ambient temperature, performance estimation that considers the flight conditions must be known before developing or operating the gas turbine. More so, for the UAV (Unmanned Aerial Vehicle) where the engine is activated by an onboard engine controller in emergency, the precise performance model including the estimated steady-state and transient performance data should be provided to the engine control system and the engine health monitoring system. In this study, a GUI (Graphic User Interface) type steady-state and transient performance simulation model of the PW206C turbo shaft engine that was adopted for use on the Smart UAV was developed using SIMULINK for performance analysis. For the simulation model, firstly the component maps including compressor, gas generator turbine and power turbine were inversely generated from manufacturer’s limited performance deck data by Hybrid Method. For the work and mass flow matching between components of the steady-state simulation, the state-flow library of SIMULINK was applied. The proposed steady-state performance model can simulate off-design point performance at various flight conditions and part loads, and in order to evaluate the steady-state performance model their simulation results were compared with manufacturer’s performance deck data. According to comparison results, it was confirm that the steady-state model well agreed with the deck data within 3% in all flight envelop. In the transient performance simulation model, the CMF (Continuity of Mass Flow) method was used and the rotational speed change was calculated by integrating the excess torque due to the transient fuel flow change using Runge-Kutta method. In this transient performance simulation, the turbine overshoot was predicted.
Because an aircraft gas turbine operates under various flight conditions that change with altitude, flight velocity, and ambient temperature, the performance estimation that considers the flight conditions must be known before developing or operating the gas turbine. More so, for the unmanned aerial vehicle (UAV) where the engine is activated by an onboard engine controller in emergencies, the precise performance model including the estimated steady-state and transient performance data should be provided to the engine control system and the engine health monitoring system. In this study, a graphic user interface (GUI) type steady-state and transient performance simulation model of the PW206C turboshaft engine that was adopted for use in the Smart UAV was developed using SIMULINK for the performance analysis. For the simulation model, first the component maps including the compressor, gas generator turbine, and power turbine were inversely generated from the manufacturer’s limited performance deck data by the hybrid method. For the work and mass flow matching between components of the steady-state simulation, the state-flow library of SIMULINK was applied. The proposed steady-state performance model can simulate off-design point performance at various flight conditions and part loads, and in order to evaluate the steady-state performance model their simulation results were compared with the manufacturer’s performance deck data. According to comparison results, it was confirmed that the steady-state model agreed well with the deck data within 3% in all flight envelopes. In the transient performance simulation model, the continuity of mass flow (CMF) method was used, and the rotational speed change was calculated by integrating the excess torque due to the transient fuel flow change using the Runge–Kutta method. In this transient performance simulation, the turbine overshoot was predicted.
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