• 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.

• Journal of the Korean Society of Propulsion Engineers
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• v.9 no.4
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• pp.81-88
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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 order 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. When the performance analysis is performed at far away operation conditions from the design point, in case of use of e component map by the traditional scaling method, the error of the performance analysis results is greatly increasing. In the other hand, if in case of use of the compressor map generated by the proposed GAs scheme, the performance analysis results are closely met with those by the performance deck, EEPP.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.187-194
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• 2008

Because the types and severities of most engine faults are various and complex, it is not easy that the conventional model based fault detection approach like the GPA(Gas Path Analysis) method can monitor all engine fault conditions. Therefore this study proposed newly a diagnostic algorithm for isolating and diagnosing effectively the faulted components of the smart UAV propulsion system, which has been developed by KARI(Korea Aerospace Research Institute), using the fuzzy logic and the neural network algorithms. A precise performance model should be needed to perform the model-based diagnostics. The based engine performance model was developed using SIMULINK. For the work and mass flow matching between components of the steady-state simulation, the state-flow library 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. The diagnosis procedure of the proposed diagnostic system has the following steps. Firstly after obtaining database of fault patterns through performance simulation, then secondly the diagnostic system was trained by the FFBP networks. Thirdly after analyzing the trend of the measuring parameters due to fault patterns, then fourthly faulted components were isolated using the fuzzy logic. Finally magnitudes of the detected faults were obtained by the trained neural networks. Because the detected faults have almost same as degradation values of the implanted fault pattern, it was confirmed that the proposed diagnostic system can detect well the engine faults.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.39 no.4
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• pp.371-378
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• 2015

Range extenders, which are power generation systems driven by small engines, extend the driving distance and time of electric vehicles (EVs) through continuous charging of batteries. The currently used range extenders with gasoline engines pose limitations with regard to the realization of high-power compact systems, owing to their complex structure and low energy density. In contrast, micro gas turbine (MGT) range extenders (MGT power packs) possess high power and low weight, and can therefore be significantly reduced in size despite increase in speed. In this study, an MGT power pack for the range extenders of EVs was developed using a turbo-prop micro turbine, an alternator for passenger vehicles and electric batteries. The operating characteristics of the MGT power pack were measured through a series of experiments conducted under electrical no-load and load conditions. Their power generation performance and efficiency were measured under various electrical loads and power transmission gear ratios. From the results, electrical load was found to have no influence on power generation performance. The maximum electrical power output was 0.8 kW at a core turbine speed of 150 krpm, and the application of 3:1 reduction gear to the turbine output shaft increased the power to 1.5 kW by 88%. This implies that the test results demonstrated stable power generation performance of the MGT power pack regardless of vehicle load changes, thus revealing its feasibility for use with the range extenders of EVs.