An Efficient Transient Simulation Method for a Volume Dynamics Model

Kurosaki, Masahiro; Sasamoto, Minoru; Asaka, Kentaro; Nakamura, Keiko; Kakiuchi, Daiki

doi:10.1115/gt2018-75353

Cited by 5 publications

(2 citation statements)

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“…Apart from this it proved to be useful for dynamic simulation back in the days when the computers were not so fast. Recently, Kurosaki et al [ 148 ] have proposed a new volume dynamics model in order to enhance the computational fidelity by using implicit Euler method for numerical solution of the dynamic system. However, the suggested method takes larger time steps during simulation process.…”

Section: Transient Model Development Portfolio Of Igtsmentioning

confidence: 99%

Transient Behavior in Variable Geometry Industrial Gas Turbines: A Comprehensive Overview of Pertinent Modeling Techniques

Hashmi

Lemma

Ahsan

et al. 2021

Entropy

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Generally, industrial gas turbines (IGT) face transient behavior during start-up, load change, shutdown and variations in ambient conditions. These transient conditions shift engine thermal equilibrium from one steady state to another steady state. In turn, various aero-thermal and mechanical stresses are developed that are adverse for engine’s reliability, availability, and overall health. The transient behavior needs to be accurately predicted since it is highly related to low cycle fatigue and early failures, especially in the hot regions of the gas turbine. In the present paper, several critical aspects related to transient behavior and its modeling are reviewed and studied from the point of view of identifying potential research gaps within the context of fault detection and diagnostics (FDD) under dynamic conditions. Among the considered topics are, (i) general transient regimes and pertinent model formulation techniques, (ii) control mechanism for part-load operation, (iii) developing a database of variable geometry inlet guide vanes (VIGVs) and variable bleed valves (VBVs) schedules along with selection framework, and (iv) data compilation of shaft’s polar moment of inertia for different types of engine’s configurations. This comprehensive literature document, considering all the aspects of transient behavior and its associated modeling techniques will serve as an anchor point for the future researchers, gas turbine operators and design engineers for effective prognostics, FDD and predictive condition monitoring for variable geometry IGT.

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Section: Transient Model Development Portfolio Of Igtsmentioning

confidence: 99%

Transient Behavior in Variable Geometry Industrial Gas Turbines: A Comprehensive Overview of Pertinent Modeling Techniques

Hashmi

Lemma

Ahsan

et al. 2021

Entropy

View full text Add to dashboard Cite

show abstract

“…In recent years, numerous studies show that these dynamic models allow for simulating and predicting gas turbines behavior by making approximations around transient behaviors. Kurosaki et al (2018) proposed an efficient numerical integration method for a volume dynamics model in gas turbine transient simulations. The proposed model was applied to transient simulations of a compressor rig test model composed of a compressor, a nozzle with variable geometry and a volume placed between them.…”

Section: Introductionmentioning

confidence: 99%

Physics Informed Machine Learning Approach for Performance Degradation Monitoring of Gas Turbine

Liu,

Jiang,

et al. 2023

PHMAP_CONF

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The heavy-duty gas turbine is playing an increasingly significant role on power generation due to its lower-emission, higher flexibility and thermo-efficiency. Main subsystems of the gas turbine like compressor, combustor and turbine degrade over the operating time under the harsh environmental conditions, which largely impacts the efficiency and productivity of the system. Therefore, it is critical to develop effective approaches to monitor performance degradation of a heavy-duty gas turbine for system predictive maintenance thus improving the efficiency and productivity of the machine. This paper presents a new physics informed machine learning methodology to predict the degradation of gas turbine by seamlessly integrating thermodynamic heat balancing mechanism, component characteristics, multi-source data and artificial neural network model. The mechanism-based thermodynamic model is established for multiple subsystems considering the balance of flow, mass and energy, and then integrated to a system level for performance simulation of the gas turbine under different conditions. The system model is able to effectively simulate values for those parameters that are not measurable (e.g. GT exhaust flow) or inaccurately measured (e.g. fuel flow). Machine learning based data cleaning approach is employed to preprocess the multivariate raw data of the gas turbine. The difference between design performance data and corrected value obtained from the physics-informed model under ISO conditions is utilized to assess the performance degradation. A Long Short-Term Memory (LSTM) model is established from the fusion of the actual and simulation data to predict the performance degradation of the gas turbine. A comparison study with the classical Nonlinear Autoregressive Network with External Input (NARX) neural network is conducted to demonstrate the advantage of the proposed method. Key Word: Gas Turbine, Thermodynamic Balance, Performance Degradation Predict, Machine Learning, LSTM

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Dynamic simulation of a gas turbine for heat recovery at varying load and environment conditions

Gou

Zhang

et al. 2021

Applied Thermal Engineering

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An Efficient Transient Simulation Method for a Volume Dynamics Model

Cited by 5 publications

References 0 publications

Transient Behavior in Variable Geometry Industrial Gas Turbines: A Comprehensive Overview of Pertinent Modeling Techniques

Transient Behavior in Variable Geometry Industrial Gas Turbines: A Comprehensive Overview of Pertinent Modeling Techniques

Physics Informed Machine Learning Approach for Performance Degradation Monitoring of Gas Turbine

Dynamic simulation of a gas turbine for heat recovery at varying load and environment conditions

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