Jayoung Ki

Component Map Generation of a Gas Turbine Using Genetic Algorithms

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 manufacturers 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.

Components map generation of gas turbine engine using genetic algorithms and engine performance deck data

In order to estimate the gas turbine engine performance precisely, the component maps containing their own performance characteristics should be used. Because the components map is an engine manufacturer’s propriety obtained from many experimental tests with high cost, they are not provided to the customer generally. Some scaling methods for gas turbine component maps using experimental data or data partially given by engine manufacturers had been proposed in a previous study. Among them the map generation method using experimental data and genetic algorithms had showed the possibility of composing the component maps from some random test data. However not only does this method need more experimental data to obtain more realistic component maps but it also requires some more calculation time to treat the additional random test data by the component map generation program. Moreover some unnecessary test data may introduced to generate inaccuracy in component maps. The map generation method called the system identification method using partially given data from the engine manufacturer (Kong and Ki, 2003, ASME J. Eng. Gas Turbines Power, 125, 958–979) can improve the traditional scaling methods by multiplying the scaling factors at design point to off-design point data of the original performance maps, but some reference map data at off-design points should be needed. In this study a component map generation method, which may identify the component map conversely from some calculation results of a performance deck provided by the engine manufacturer using the genetic algorithms, was newly proposed to overcome the previous difficulties. As a demonstration example for this study, the PW206C turbo shaft engine for the tilt rotor type smart unmanned aerial vehicle which has been developed by Korea Aerospace Research Institute was used. In order to verify the proposed method, steady-state performance analysis results using the newly generated component maps were compared with them performed by the Estimated Engine Performance Program deck provided by the engine manufacturer. The performance results using the identified maps were also 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 and the methods explained above.

Performance simulation of a turboprop engine for basic trainer

A performance simulation program for the turboprop engine (PT6A-62), which is the power plant of the first Korean indigenous basic trainer KT-1, was developed for performance prediction, development of an EHMS (Engine Health Monitoring System) and the flight simulator. Characteristics of components including compressors, turbines, power turbines and the constant speed propeller were required for the steady state and transient performance analysis with on and off design point analysis. In most cases, these were substituted for what scaled from similar engine components’ characteristics with the scaling law. The developed program was evaluated with the performance data provided by the engine manufacturer and with analysis results of GASTURB program, which is well known for the performance simulation of gas turbines. Performance parameters such as mass flow rate, compressor pressure ratio, fuel flow rate, specific fuel consumption and turbine inlet temperature were discussed to evaluate validity of the developed program at various cases. The first case was the sea level static standard condition and other cases were considered with various altitudes, flight velocities and part loads with the range between idle and 105% rotational speed of the gas generator. In the transient analysis, the Continuity of Mass Flow Method was utilized under the condition that mass stored between components is ignored and the flow compatibility is satisfied, and the Modified Euler Method was used for integration of the surplus torque. The transient performance analysis for various fuel schedules was performed. When the fuel step increase was considered, the overshoot of the turbine inlet temperature occurred. However, in case of ramp increase of the fuel longer than step increase of the fuel, the overshoot of the turbine inlet temperature was effectively reduced.

A Study on Fault Detection of a Turboshaft Engine Using Neural Network Method

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.

Intelligent performance diagnostics of a gas turbine engine using user‐friendly interface neural networks

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.

Steady-State and Transient Performance Modeling of Smart UAV Propulsion System Using SIMULINK

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.

Development of On-Line Performance Diagnostics Program of a Helicopter Propulsion System

The engine health monitoring system has been generally applied to the aircraft system to improve reliability and durability of the aircraft propulsion system and to minimize its operational cost. The helicopter flies at low altitude level flight mode in its own operational range comparing to other aircraft categories. The low level flight means that the engine operates at variable atmospheric condition such as hot and cold temperature, snow, heavy rain, etc. Furthermore, it may increase the possibility of foreign object ingestion, such as sand, dust, etc., i.e. this operating condition gives rise to damages of engine gas path components. Because types and severities of most helicopter engine faults are very complicate, the conventional model based fault diagnostic approach like the GPA (Gas Path Analysis) method is not adequate to monitor such a complex engine fault condition. An on-line diagnostic program was developed by using SIMULINK, where measurement signals were simulated by an input module. This study proposes a neural network algorithm for calculating variation of mass flow and efficiency in each engine component from measuring data. The neural network was trained by damages at each component such as compressor, compressor turbine or power turbine. The used database for training the neural network was obtained from simulation under various flight conditions. Reliability and capability of the developed on-line diagnostics program were evaluated through application to a helicopter engine health monitoring.Copyright

Performance Simulation of Turboprop Engine for Basic Trainer

A performance simulation program for the turboprop engine (PT6A-62), which is the power plant of the first Korean indigenous basic trainer KT-1, was developed for performance prediction, development of an EHMS (Engine Health Monitoring System) and the flight simulator. Characteristics of components including compressors, turbines, power turbines and the constant speed propeller were required for the steady state and transient performance analysis with on and off design point analysis. In most cases, these were substituted for what scaled from similar engine components’ characteristics with the scaling law. The developed program was evaluated with the performance data provided by the engine manufacturer and with analysis results of GASTURB program, which is well known for the performance simulation of gas turbines. Performance parameters such as mass flow rate, compressor pressure ratio, fuel flow rate, specific fuel consumption and turbine inlet temperature were discussed to evaluate validity of the developed program at various cases. The first case was the sea level static standard condition and other cases were considered with various altitudes, flight velocities and part loads with the range between idle and 105% rotational speed of the gas generator. In the transient analysis, the Continuity of Mass Flow Method was utilized under the condition that mass stored between components is ignored and the flow compatibility is satisfied, and the Modified Euler Method was used for integration of the surplus torque. The transient performance analysis for various fuel schedules was performed. When the fuel step increase was considered, the overshoot of the turbine inlet temperature occurred. However, in case of ramp increase of the fuel longer than step increase of the fuel, the overshoot of the turbine inlet temperature was effectively reduced.

Development of Condition Monitoring Test Cell Using Micro Gas Turbine Engine

This study is aim to be programmed the simulation which is available for real-time performance analysis so that is to be developed gas turbine engine’s condition monitoring system with analyzing difference between performance analysis results and measuring data from test cell. In addition, test cell created by this study have been developed to use following applications: to use for learning principals and mechanism of gas turbine engine in school, and to use performance test and its further research for variable operating conditions in associated institutes. The maximum thrust of the micro turbojet engine is 137 N (14 kgf) at 126,000 rpm of rotor rotational speed if the Jet A1 kerosene fuel is used. The air flow rate is measured by the inflow air speed of duct, and the fuel flow is measured by a volumetric fuel flowmeter. Temperatures and pressures are measured at the atmosphere, the compressor inlet and outlet and the turbine outlet. The thrust stand was designed and manufactured to measure accurately the thrust by the load cell. All measuring sensors are connected to a DAQ (Data Acquisition) device, and the logging data are used as function parameters of the program, LabVIEW. The LabVIEW is used to develop the engine condition monitoring program. The proposed program can perform both the reference engine model performance analysis at an input condition and the real-time performance analysis with real-time variables. By comparing two analysis results the engine condition can be monitored. Both engine performance analysis data and monitoring results are displayed by the GUI (Graphic User Interface) platform.Copyright

Trend Monitoring of A Turbofan Engine for Long Endurance UAV Using Fuzzy Logic

The UAV propulsion system that will be operated for long time at more than 40,000ft altitude should have not only fuel flow minimization but also high reliability and durability. If this UAV propulsion system may have faults, it is not easy to recover the system from the abnormal, and hence an accurate diagnostic technology must be needed to keep the operational reliability. For this purpose, the development of the health monitoring system which can monitor remotely the engine condition should be required. In this study, a fuzzy trend monitoring method for detecting the engine faults including mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration and etc. Using engine condition database as an input to be generated by linear regression analysis of real engine instrument data, an application of the fuzzy logic in diagnostics estimated the cause of fault in each component. According to study results. it was confirmed that the proposed trend monitoring method can improve reliability and durability of the propulsion system for a long endurance UAV to be operated at medium altitude