Modeling, Simulation and Validation of Mini SR-30 Gas Turbine Engine

Singh, Richa; Maity, Arnab; Nataraj, P. S. V.

doi:10.1016/j.ifacol.2018.05.093

Cited by 12 publications

(7 citation statements)

References 6 publications

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“…The model is found to deliver good shaft speed prediction with an accuracy of 88.2%. The detailed description of this model and its validation is given in [12]. This virtual model is utilized to simulate different fault scenarios in the current work as it is not feasible to create faults in the laboratory engine.…”

Section: Modeling Of the Laboratory Sr-30 Enginementioning

confidence: 99%

“…shows that the prediction result obtained from the NARX model follows the experimental data. The model output is also verified against a first order with dead time (FOPDT) model as given in [12]. The NARX model of the fuel supply system predicts the fuel flow rate with an 88.9 % accuracy.…”

Section: Modeling Of the Fuel Supply Systemmentioning

confidence: 99%

“…The NARX model of the fuel supply system predicts the fuel flow rate with an 88.9 % accuracy. The detailed description of FSS modeling is given in [12].…”

Section: Modeling Of the Fuel Supply Systemmentioning

confidence: 99%

“…An in-house, physics-based model [12] is developed in Simulink/ MATLAB [7] is used to simulate faults in a gas turbine engine. This mathematical model of laboratory GTE also allows identifying the characteristic parameters of the different components of the gas turbine, as listed in Table 1.…”

Section: Benchmarking Datasets and Preprocessingmentioning

confidence: 99%

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On-board Fault Diagnosis of a Laboratory Mini SR-30 Gas Turbine Engine

Singh¹

2021

Preprint

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Inspired by recent progress in machine learning, a data-driven fault diagnosis and isolation (FDI) scheme is explicitly developed for failure in the fuel supply system and sensor measurements of the laboratory gas turbine system. A passive approach of fault diagnosis is implemented where a model is trained using machine learning classifiers to detect a given set of fault scenarios in real-time on which it is trained. Towards the end, a comparative study is presented for wellknown classification techniques, namely Support vector classifier, linear discriminant analysis, K-neighbor, and decision trees. Several simulation studies were carried out to demonstrate and illustrate the proposed fault diagnosis scheme's advantages, capabilities, and performance.

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Section: Modeling Of the Laboratory Sr-30 Enginementioning

confidence: 99%

Section: Modeling Of the Fuel Supply Systemmentioning

confidence: 99%

“…The NARX model of the fuel supply system predicts the fuel flow rate with an 88.9 % accuracy. The detailed description of FSS modeling is given in [12].…”

Section: Modeling Of the Fuel Supply Systemmentioning

confidence: 99%

Section: Benchmarking Datasets and Preprocessingmentioning

confidence: 99%

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On-board Fault Diagnosis of a Laboratory Mini SR-30 Gas Turbine Engine

Singh¹

2021

Preprint

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“…They combined the advantages of the previously described tools as their proposed approach was based on commercially available simulation tools and allowed mathematical modeling of gas turbine components for all engine configurations in a flexible and user-friendly interface. Singh et al (2018) provided the modeling of the SR-30 engine developed by turbine technologies, which allowed the monitoring of thermodynamic processes of the engine through pressure and temperature sensors when integrated into the Turbine Technologies MiniLab System and a user interface via Simulink software. After the modeling phase, they compared and discussed the outputs with experimental data.…”

Section: Introductionmentioning

confidence: 99%

Performance of a microjet using component map scaling

Coban¹,

Ekici

Çolpan

et al. 2021

AEAT

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Purpose This paper aims to investigate the cycle performance of a small size turbojet engine used in unmanned aerial vehicles at 0–5,000 m altitude and 0–0.8 Mach flight speeds with real component maps. Design/methodology/approach The engine performance calculations were performed for both on-design and off-design conditions through an in-house code generated for simulating the performance of turbojet engines at different flight regimes. These calculations rely on input parameters in which fuel composition are obtained through laboratory elemental analysis. Findings Exemplarily, according to comparative results between in-house developed performance code and commercially available software, there is 0.25% of the difference in thrust value at on-design conditions. Practical implications Once the on-design performance parameters and fluid properties were determined, the off-design operation calculations were performed based on the compressor and turbine maps and scaling methodology. This method enables predicting component maps and fitting them to real conditions. Originality/value A method to be used easily by researchers on turbojet engine performance calculations which best fits to real conditions.

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