• Journal of the Korean Society of Propulsion Engineers
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• v.12 no.4
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• pp.42-47
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• 2008

The power control system of Smart UAV is similar to the propeller pitch governing concept of turboprop aircraft. The pilot adjusts the engine power directly and the pitch governor controls the propeller pitch to maintain the propeller rotational speed. The electronic engine controller(EEC) of PW206C engine developed for helicopter is not fit for the power control concept of Smart UAV, and therefore the manual back-up system of PW206C engine is used for the engine power control of Smart UAV. Engine performance estimation program is used to predict the control range of power lever angle(PLA) according to the variation of engine output shaft speed, flight altitude and flight speed. These data provide a guide for the PLA control in manual mode operation.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2007.11a
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• pp.339-342
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• 2007

The power control system of Smart UAV is similar to the propeller pitch governing concept of turboprop aircraft. The pilot inputs the engine power directly and the pitch governor controls the propeller pitch to maintain the propeller RPM. The manual back-up system of PW206C engine is used for the engine power control of Smart UAV. Engine performance estimation program is used to predict the control range of power lever arm(PLA) angle according to the variation of flight altitude and speed. These data provide a guide for the engine control in manual mode operation.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.05a
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• pp.222-226
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• 2006

The purpose of this study is to analyze both the design and off design performance simulation of the PW206C turbo shaft engine used in the development of the smart UAV (Unmanned Ariel Vehicle) by KARI(Korean Aerospace Research Institute). Its mainly aims to investigate performance behavior at the un-installed and installed conditions. The ways employed to be able to analyze the performance extensively were mainly carried out by comparison of performance simulation results from both the commercial program 'GASTURB 9' using compressor maps generated by Genetic algorithms (GAs) or Scaling Method, and the engine manufacturer's program 'EEPP'. Off-design performance analysis was performed through matching of both mass flow and work between engine components. The set of performance simulations of the developed analytical models was performed by a commercial program package (GASTURB 9) that provides great flexibility in the choice of independent variables of the overall system. The results from the simulations are used to compare turbo shaft engine (PW206C) performance data obtained by the EEPP. At un-installed condition, it was found that the results with the compressor map generated by GAs were relatively agreed well than those with the compressor map generated by the Scaling Method. The performance calculation results using the compressor map generated by GAs were compared at un-installed condition and installed conditions with ECS-off and ECS-Max in variation of altitude, gas generator speed and flight speed.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.11a
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• pp.403-406
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• 2006

An ejector is designed for the purpose of engine bay cooling. The primary flow of the ejector is the exhaust gas of the PW206C turboshaft engine. The mass flow of secondary flow is calculated by using the approximate analytic equation. For the purpose of verification of approximate analytic method, comparison is made with the results of Navier-Stokes turbulent flow solution. According to the results of CFD, the mixing of two flows is incomplete due to the short length of mixing duct.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.05a
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• pp.213-217
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV (Unmanned Aerial Vehicle) which has been developed by KARI (Korea Aerospace Research Institute). For teaming the NN, a BPN with one hidden, one input and one output layer was used. The input layer had seven neurons of variations of measurement parameters such as SHP, MF, P2, T2, P4, T4 and T5, and the output layer used 6 neurons of degradation ratios of flow capacities and efficiencies for compressor, compressor turbine and power turbine. Database for network teaming and test was constructed using a gas turbine performance simulation program. From application results for diagnostics of the PW206C turboshaft engine using the learned networks, it was confirmed that the proposed diagnostics algorithm could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• Journal of the Korean Society of Propulsion Engineers
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• v.10 no.2
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• pp.15-22
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV(Unmanned Aerial Vehicle) which is being developed by KARI (Korea Aerospace Research Institute). For teeming the NN(Neural Network), a BPN(Back Propagation Network) with one hidden, one input and one output layer was used. The input layer has seven neurons: variations of measurement parameters such as SHP, MF, P2, T2, P4, T4 and T5, and the output layer uses 6 neurons: degradation ratios of flow capacities and efficiencies for compressor, compressor turbine and power turbine, respectively, Database for network teaming and test was constructed using a gas turbine performance simulation program. From application of the learned networks to diagnostics of the PW206C turboshaft engine, it was confirmed that the proposed diagnostics algorithm could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.11a
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• pp.281-284
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV (Unmanned Aerial Vehicle) which has been developed by KARI (Korea Aerospace Research Institute). The measurement parameters of Smart UAV propulsion system are gas generator rotational speed, power turbine rotational speed, exhaust gas temperature and torque. But two measurement such as compressor exit pressure and compressor turbine exit temperature were added because they were difficult each component diagnostics using the default measurement parameter. The performance parameters for the estimate of component performance degradation degree are flow capacities and efficiencies for compressor, compressor turbine and power turbine. Database for network learning and test was constructed using a gas turbine performance simulation program. From application results for diagnostics of the PW206C turboshaft engine using the learned networks, it was confirmed that the proposed diagnostics could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• International Journal of Aeronautical and Space Sciences
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• v.9 no.1
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• pp.100-110
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• 2008

It is not easy to monitor and identify all engine faults and conditions using conventional fault detection approaches like the GPA (Gas Path Analysis) method due to the nature and complexity of the faults. This study therefore focuses on a model based diagnostic method using Neural Network algorithms proposed for fault detection on a turbo shaft engine (PW 206C) selected as the power plant for a tilt rotor type unmanned aerial vehicle (Smart UAV). The model based diagnosis should be performed by a precise performance model. However component maps for the performance model were not provided by the engine manufacturer. Therefore they were generated by a new component map generation method, namely hybrid method using system identification and genetic algorithms that identifies inversely component characteristics from limited performance deck data provided by the engine manufacturer. Performance simulations at different operating conditions were performed on the PW206C turbo shaft engine using SIMULINK. In order to train the proposed BPNN (Back Propagation Neural Network), performance data sets obtained from performance analysis results using various implanted component degradations were used. The trained NN system could reasonably detect the faulted components including the fault pattern and quantity of the study engine at various operating conditions.

• Journal of the Korean Society of Propulsion Engineers
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• v.15 no.6
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• pp.63-69
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• 2011

The Smart UAV must have the control characteristics of propulsion system necessary for both rotary aircraft and fixed wing aircraft though it equips turbo-shaft engine. To develop an electronic engine controller in the future, it is necessary to accumulate the experience of engine operation and data of tilt rotor aircraft. For this purpose, the computer programs which predict engine performance in the steady state and transient state can be utilized for the supplementation of flight test data. In this work, we developed a dynamic analysis program using engine performance data gathered during the flight tests. In addition the accuracy of the program was verified through comparison with flight test data and the results of steady-state performance analysis program.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2005.11a
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• pp.149-153
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• 2005

In order to estimate the gas turbine engine performance precisely, the component maps containing their own performance characteristics should be needed. In this study a component map generation method which may identify compressor map conversely from a performance deck provided by engine manufacturer using genetic algorithms was newly proposed. As a demonstration example for this study, the PW 206C turbo shaft engine for the tilt rotor type Smart UAV (Unmanned Aerial Vehicle). In ordo to verify the proposed method, steady-state performance analysis results using the newly generated compressor map was compared with them performed by EEPP(Estimated Engine Performance Program) deck provided by engine manufacturer. And also the performance results using the identified maps were compared with them using the traditional scaling method. In this investigation, it was found that the newly proposed map generation method would be more effective than the traditional scaling method.