Application of particle swarm optimization in gas turbine engine fuel controller gain tuning

Montazeri-Gh, Morteza; Jafari, Soheil; Ilkhani, Mohammad Reza

doi:10.1080/0305215x.2011.576760

Cited by 34 publications

(18 citation statements)

References 14 publications

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“…However, there is no definite technique for determining the order of dynamic functions. The selection of the model order is referred to the experimental results and the equations used in the ICV model [38,39].…”

Section: Implementation and Resultsmentioning

confidence: 99%

Advanced Constraints Management Strategy for Real-Time Optimization of Gas Turbine Engine Transient Performance

Nikolaidis¹,

Li²,

Jafari³

2019

Applied Sciences

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Motivated by the growing technology of control and data processing as well as the increasingly complex designs of the new generation of gas turbine engines, a fully automatic control strategy that is capable of dealing with different aspects of operational and safety considerations is required to be implemented on gas turbine engines. An advanced practical control mode satisfaction method for the entire operating envelope of gas turbine engines is proposed in this paper to achieve the optimal transient performance for the engine. A constraint management strategy is developed to generate different controller settings for short-range fighters as well as long-range intercontinental aircraft engines at different operating conditions by utilizing a model predictive control approach. Then, the designed controller is tuned and modified with respect to different realistic considerations including the practicality, physical limitations, system dynamics, and computational efforts. The simulation results from a verified two-spool turbofan engine model and controller show that the proposed method is capable of maneuverability and/or fuel economy optimization indices while satisfying all the predefined constraints successfully. Based on the parameters, natural frequencies, and dynamic behavior of the system, a set of optimized weighting factors for different engine parameters is also proposed to achieve the optimal and safe operation for the engine at different flight conditions. The paper demonstrates the effects of the prediction length and control horizon; adding new constraints on the computational effort and the controller performance are also discussed in detail to confirm the effectiveness and practicality of the proposed approach in developing a fully automatic optimized real-time controller for gas turbine engines.

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Section: Implementation and Resultsmentioning

confidence: 99%

Advanced Constraints Management Strategy for Real-Time Optimization of Gas Turbine Engine Transient Performance

Nikolaidis¹,

Li²,

Jafari³

2019

Applied Sciences

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“…A reduced order Wiener model based on experimental results is generated to predict the dynamic behavior of the engine [35]. The engine is modelled and analyzed with details by the authors in [12], and [22]. The results of the modelling are also validated against experimental data.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…where W f ,PLA , W f ,dec , W f ,acc and W f ,Nmax are the transient fuel flow rates calculated by the PLA, maximum deceleration, maximum acceleration and maximum speed loops, respectively. All details about design, development, and implementation of Min-Max control strategies could be found in [2,11,12]. The schematic of Min-Max selection strategy in the controller structure is shown in Figure 2.…”

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confidence: 99%

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Turbojet Engine Industrial Min–Max Controller Performance Improvement Using Fuzzy Norms

Jafari

Nikolaidis

2018

Electronics

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The Min–Max control strategy is the most widely used control algorithm for gas turbine engines. This strategy uses minimum and maximum mathematical functions to select the winner of different transient engine control loops at any instantaneous time. This paper examines the potential of using fuzzy T and S norms in Min–Max selection strategy to improve the performance of the controller and the gas turbine engine dynamic behavior. For this purpose, different union and intersection fuzzy norms are used in control strategy instead of using minimum and maximum functions to investigate the impact of this idea in gas turbine engines controller design and optimization. A turbojet engine with an industrial Min–Max control strategy including steady-state and transient control loops is selected as the case study. Different T and S norms including standard, bounded, Einstein, algebraic, and Hamacher norms are considered to be used in control strategy to select the best transient control loop for the engine. Performance indices are defined as pilot command tracking as well as the engine response time. The simulation results confirm that using Einstein and Hamacher norms in the Min–Max selection strategy could enhance the tracking capability and the response time to the pilot command respectively. The limitations of the proposed method are also discussed and potential solutions for dealing with these challenges are proposed. The methodological approach presented in this research could be considered for enhancement of control systems in different types of gas turbine engines from practical point of view.

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“…The block structure model has been developed by Kulikov et al [18]. One of the block structure models appropriates for simulation of gas turbine engines is Wiener model [19]. This model has two parts: A linear dynamic part and a static nonlinear part.…”

Section: Introductionmentioning

confidence: 99%

Static and Dynamic Mathematical Modeling of a Micro Gas Turbine

2013

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This paper presents a static and linear dynamic model to simulate the performance of a Micro Gas Turbine (MGT). The static model is obtained using thermodynamic equations and maps of the components to determine off design performance of the MGT with constant output power. The linear dynamic model is developed using linearized static and dynamic nonlinear equations around an operating point as state-space model to predict the behaviour of the MGT in transient conditions. To validate the results of the static model, important information such as fuel flow, fuel to air ratio, and turbine inlet temperature are compared with the results of Aspen-HYSYS software during an increase in ambient temperature. In addition, static model results are compared with experimental data of the MGT test by previous studies. Also, the compressor equilibrium running line of the MGT is derived in constant ambient condition. In order to evaluate the linear dynamic model, the verification is performed using the results of nonlinear model of previous studies. The comparison between the results confirms the ability of proposed dynamic model to simulate a MGT while decreasing the computational effort and complexity of equations. Moreover, a comparison is carried out between static model results and steady state values predicted by dynamic model.

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Application of particle swarm optimization in gas turbine engine fuel controller gain tuning

Cited by 34 publications

References 14 publications

Advanced Constraints Management Strategy for Real-Time Optimization of Gas Turbine Engine Transient Performance

Advanced Constraints Management Strategy for Real-Time Optimization of Gas Turbine Engine Transient Performance

Turbojet Engine Industrial Min–Max Controller Performance Improvement Using Fuzzy Norms

Static and Dynamic Mathematical Modeling of a Micro Gas Turbine

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