Components Map Generation of Gas Turbine Engine Using Genetic Algorithms and Engine Performance Deck Data

Abstract

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.

Detailed study of the impact of co-utilization of biomass in a natural gas combined cycle power plant through perturbation analysis

“…Water is drawn from the deaerator drum and passed through the HP economizer (6) before entering the HP drum (7). Steam is produced in the HP drum and superheated through the HP superheater (8) before being expanded in the HP steam turbine for power generation (9). The LP steam is superheated (5) and added to the expanded HP steam to generate additional power in the LP steam turbine (10).…”

“…Several detailed approaches to setting up an accurate gas turbine model can be found in [8][9][10][11]. Under different circumstances, simplified linearized techno-economic models were developed to have a quick but fairly accurate tool to estimate performance and electricity cost [12][13][14][15][16][17].…”

Review on Advanced Health Monitoring Methods for Aero Gas Turbines using Model Based Methods and Artificial Intelligent Methods

“…In addition, Kong et al recently proposed a new map generation method obtained by applying genetic algorithms to random test data or performance deck data. They therefore showed the possibility of composing the component maps from some random performance data [22]. The performance simulation is classified into the steady state performance simulation and the dynamic performance simulation.…”

“…However, the scaling method is only available if it uses very similar maps to the real engine. If similar maps were not used to simulate the engine, the simulated performance may differ considerably from the real engine performance at off-design conditions [20][21][22][23]. In order to overcome the above mentioned difficulties, Kong et al proposed a map generation method called the system identification method using partially given operation performance data from the engine manufacturer; they could improve the traditional scaling methods by multiplying the scaling factors at the design point to off-design point data of the original performance maps [20].…”

Aero Gas Turbine Flight Performance Estimation Using Engine Gas Path Measurements

“…Roth et al [7] introduced an optimization concept for engine cycle model matching and a minimum variance estimator algorithm [8] for performance matching of a turbofan engine. Kong et al proposed map scaling methods using genetic algorithms to improve the accuracy of performance models [9,10]. Li et al developed an influence coefficient matrix-based adaptation method for gas turbine design point performance adaptation [11] and different nonlinear adaptation methods using genetic algorithms to improve the accuracy of off-design performance modeling [12][13][14].…”