Model-Based Dynamic Performance Simulation of a Microturbine Using Flight Test Data

Erario, Mario Leonardo; Giorgi, Maria Grazia De; Przysowa, Radosław

doi:10.3390/aerospace9020060

Cited by 13 publications

(10 citation statements)

References 63 publications

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“…All the components were configured to accurately model the engine behavior by setting their design parameters, such as the maximum speed, pressure ratio, mass flow rate, fuel flow rate, efficiencies, and so forth. The model was used to simulate the design point, steady states at various engine speeds and transient operation, at sea level and in flight conditions [33]. The engine model was validated with data gathered from bench tests and the flight missions of the twin-engine aerial target.…”

Section: Micro Turbojetmentioning

confidence: 99%

“…The rig and flight data acquired from a real micro turbojet were used to validate the GSP model [33]. The validated model was subsequently used to simulate selected operating conditions in order to obtain the values of the virtual engine sensors, necessary to train and test the predictive techniques.…”

Section: Prediction Of Component Degradationmentioning

confidence: 99%

“…Traditional engine models rely on the thermodynamic description of the engine, so they are called white-box or physics-based approaches. Such a model of a micro turbojet [33] was recently developed and fine-tuned in GSP (Gas turbine Simulation Program) [34,35], and validated with experimental flight data. This model was reused here for generating training datasets for an artificial neural network (ANN), for a planned engine health management system.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine

Menga¹,

Mothakani²,

Giorgi³

et al. 2022

Preprint

Self Cite

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Micro turbojets are used for propelling radio-controlled aircraft, aerial targets and personal air vehicles. When compared to full-scale engines, they are characterized by relatively low efficiency and durability. In this context, the degraded performance of gas path components could lead to an unacceptable reduction in the overall engine performance. In this work, a data-driven model based on a conventional Artificial Neural Network (ANN) and an extreme learning machine (ELM) was used for estimating the performance degradation of the micro turbojet. The training datasets containing the performance data of the engine with degraded components were generated using the validated GSP model and the Monte Carlo approach. In particular, compressor and turbine performance degradation were simulated for three different flight regimes. It was confirmed that component degradation has a higher impact in flight than at sea level. Finally, the datasets were used in the training and testing process of the ELM algorithm with four different input vectors. Two vectors had an extensive number of virtual sensors, and the other two were reduced to just fuel flow and Exhaust Gas Temperature. Even with the small number of sensors, the high prediction accuracy of ELM was maintained for takeoff and cruise but was slightly worse for variable flight conditions.

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Section: Micro Turbojetmentioning

confidence: 99%

Section: Prediction Of Component Degradationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine

Menga¹,

Mothakani²,

Giorgi³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Using an approach that requires less testing on a real engine to produce valid and accurate data for different engine conditions can help in designing a reliable and efficient control system. The use of optimization-and model-based controls will be more applicable for future engine control systems [18][19][20][21]. In this paper, the current FADEC logic of a real engine is redesigned and optimized with the fewest changes for a practical application.…”

Section: Introductionmentioning

confidence: 99%

Advanced Control Algorithm for FADEC Systems in the Next Generation of Turbofan Engines to Minimize Emission Levels

et al. 2022

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New propulsion systems in aircrafts must meet strict regulations and emission limitations. The Flightpath 2050 goals set by the Advisory Council for Aviation Research and Innovation in Europe (ACARE) include reductions of 75%, 90%, and 65% in CO2, NOx, and noise, respectively. These goals are not fully satisfied by marginal improvements in gas turbine technology or aircraft design. A novel control design procedure for the next generation of turbofan engines is proposed in this paper to improve Full Authority Digital Engine Control (FADEC) systems and reduce the emission levels to meet the Flightpath 2050 regulations. Hence, an Adaptive Network–based Fuzzy Inference System (ANFIS), nonlinear autoregressive network with exogenous inputs (NARX) techniques, and the block-structure Hammerstein–Wiener approach are used to develop a model for a turbofan engine. The Min–Max control structure is chosen as the most widely used practical control algorithm for gas turbine aero engines. The objective function is considered to minimize the emission level for the engine in a pre-defined maneuver while keeping the engine performance in different aspects. The Genetic Algorithm (GA) is applied to find the optimized control structure. The results confirm the effectiveness of the proposed approach in emission reduction for the next generation of turbofan engines.

show abstract

“…Traditional engine models base on the thermodynamic description of the gas turbine, so they are called white-box or physics-based. Such a model of a micro turbojet [33] was recently developed and fine-tuned in GSP (Gas turbine Simulation Program) [34,35], and validated with experimental flight data. This model was reused here for generating training data for an artificial neural network, for a planned engine health management system.…”

Section: Introductionmentioning

confidence: 99%

Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine

Menga¹,

Mothakani²,

Giorgi³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Micro turbojets are used for propelling radio-controlled aircraft, aerial targets and personal air vehicles. When compared to full-scale engines, they are characterized by relatively low efficiency and durability. In this context, the degraded performance of gas path components could lead to an unacceptable reduction in the overall engine performance. In this work, a data-driven model based on a conventional Artificial Neural Network (ANN) and an extreme learning machine (ELM) was used for estimating the performance degradation of the micro turbojet. The training datasets containing the performance data of the engine with degraded components were generated using the validated GSP model and the Monte Carlo approach. In particular, compressor and turbine performance degradation were simulated for three different flight regimes. It was confirmed that component degradation had a similar impact in flight than at sea level. Finally, the datasets were used in the training and testing process of the ELM algorithm with four different input vectors. Two vectors had an extensive number of virtual sensors, and the other two were reduced to just fuel flow and Exhaust Gas Temperature. Even with the small number of sensors, the high prediction accuracy of ELM was maintained for takeoff and cruise but was slightly worse for variable flight conditions.

show abstract

Model-Based Dynamic Performance Simulation of a Microturbine Using Flight Test Data

Cited by 13 publications

References 63 publications

Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine

Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine

Advanced Control Algorithm for FADEC Systems in the Next Generation of Turbofan Engines to Minimize Emission Levels

Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine

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