Numerical simulation method of surge experiments on gas turbine engines

Zheng, Xinqian; Zeng, Hanxuan; WANG, Baotong; WEN, Mengyang; Yang, Heli; Sun, Zhenzhong

doi:10.1016/j.cja.2022.08.007

Cited by 10 publications

(3 citation statements)

References 16 publications

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“…Numerical zooming is a computational approach used in aero engine design and analysis to study the flow characteristics in specific regions of interest within the engine (Jia et al, 2022;Xu et al, 2022;Yang et al, 2022;Zheng et al, 2023). The Cycle with CFD in it (CWCFD) method in numerical zooming involves integrating multiple models with varying fidelity and complexity into a full engine simulation.…”

Section: Introductionmentioning

confidence: 99%

Direct multi-fidelity integration of 3D CFD models in a gas turbine with numerical zooming method

Deng,

Wei,

et al. 2024

J. Glob. Power Propuls. Soc.

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Multi-fidelity simulation improves the simulation accuracy and captures more detailed information about aero engines under limited computing resources, which is implemented by coupling different levels of models using numerical zooming methods. However, there is an obvious problem in traditional zooming methods such as the iterative coupled zooming method or mini-map method: both the convergence and accuracy depend highly on the component general characteristic maps. Based on the investigation of a micro gas turbine, a direct zooming method (Cycle with CFD in it, CWCFD) is developed. It directly embeds the 3D CFD compressor and turbine model into a 0D component-level model without component general characteristic maps. Then, the CWCFD zooming method is compared with the traditional 0D component-level model in terms of the throttle characteristics of the micro gas turbine, and the experimental data of the ground test is performed to verify the effectiveness of the CWCFD zooming methods. The results indicate that the CWCFD zooming method matches well with the test data better than the traditional 0D component-level model.

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Section: Introductionmentioning

confidence: 99%

Direct multi-fidelity integration of 3D CFD models in a gas turbine with numerical zooming method

Deng,

Wei,

et al. 2024

J. Glob. Power Propuls. Soc.

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show abstract

“…This optimization not only boosts engine performance and lifespan but also slashes maintenance costs and extends maintenance intervals [2,3]. Mathematical modelling is pivotal in analyzing and optimizing engine processes across various modes of helicopter TE operation, such as low gas mode, cruise modes, and emergency mode [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…Justification for the choice of neural network architecture and its training algorithm for helicopter TE model neural network approximation. 4.…”

mentioning

confidence: 99%

Neural Network Approximation of Helicopter Turboshaft Engine Parameters for Improved Efficiency

Vladov,

Yakovliev,

Bulakh

et al. 2024

Energies

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The work is devoted to the development of a method for neural network approximation of helicopter turboshaft engine parameters, which is the basis for researching engine energy characteristics to improve efficiency, reliability, and flight safety. It is proposed to use a three-layer direct propagation neural network with linear neurons in the output layer for training in which the scale conjugate gradient algorithm is modified by introducing a moment coefficient into the analytical expression. This modification helps in calculating new model parameters to avoid falling into a local minimum. The dependence of the energy released during helicopter turboshaft engine compressor rotation on the gas-generator rotor r.p.m. was obtained. This enables the determination of the optimal gas-generator rotor r.p.m. region for a specific type of helicopter turboshaft engine. The optimal ratio of energy consumption and compressor operating efficiency is achieved, thereby ensuring helicopter turboshaft engines’ optimal performance and reliability. Experimental data support the high efficiency of using a three-layer feed-forward neural network with linear neurons in the output layer, trained using a modified scale conjugate gradient algorithm, for approximating parameters of helicopter turboshaft engines compared to the analogues. Specifically, this method better predicts the relations between the energy release during compressor rotation and gas-generator rotor r.p.m. The efficiency coefficient of the proposed method was 0.994, which exceeded that of the closest analogue (0.914) by 1.09 times.

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Multi-Fidelity Simulation of Gas Turbine Overall Performance by Directly Coupling High-Fidelity Models of Multiple Rotating Components

Deng,

Xu,

et al. 2024

J. Therm. Sci.

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Numerical simulation method of surge experiments on gas turbine engines

Cited by 10 publications

References 16 publications

Direct multi-fidelity integration of 3D CFD models in a gas turbine with numerical zooming method

Direct multi-fidelity integration of 3D CFD models in a gas turbine with numerical zooming method

Neural Network Approximation of Helicopter Turboshaft Engine Parameters for Improved Efficiency

Multi-Fidelity Simulation of Gas Turbine Overall Performance by Directly Coupling High-Fidelity Models of Multiple Rotating Components

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