Component-level Model Research Articles

Proactive Aircraft Engine Removal Planning with Dynamic Bayesian Networks

Aircraft engine removal for maintenance is an expensive ordeal, and planning for it while balancing fleet stability objectives is a complex multi-faceted challenge. This is further compounded by uncertainties associated with usage or condition-based maintenance approaches that are becoming prevalent. Engine removal decisions rely on accurate estimation of damage growth or remaining useful life of critical components and a framework for aggregating these component-level estimates (and their uncertainties) into an engine-level removal forecasting model. An approach to this planning challenge is to develop probabilistic prognostic digital twins tailored to engine-specific operations and calibrate/update them with inspection data from the field. To this end, this work outlines a framework involving: 1) building component-level probabilistic models capable of forecasting damage growth or remaining useful life, 2) aggregating the outputs of these component-level models into a system-level view using a Dynamic Bayesian Network (DBN), and 3) updating the states of the DBN with inspection information as and when they become available.

Real-time adaptive model of mainstream parameters for aircraft engines based on OSELM-EKF

Gas path parameters play a crucial role in the health management of aeroengines, which are usually generated from the nonlinear component-level model. However, risks of stability and substantial time consumption confine online applications of conventional models. This paper proposes a mainstream parameter model that exclusively involves rotating components using fewer gas path parameters, including an adaptive strategy based on an online sequential extreme learning machine and an extended Kalman filter. The mainstream parameter model is designed in a linear parameter-varying form with a non-iterative smooth state-switching mechanism and enables real-time operation by simplifying the component complexity. Besides, some sensor measurements are employed to update rotor speeds, thus eliminating the need for derivative computations. Neural networks are introduced in compressor component calculations. Additionally, the extended Kalman filter is developed to estimate health parameters to tune the system equation residuals, and the learning machine is applied to compensate for rotating components’ pressure ratios under different degradation magnitudes. Finally, systematic tests are carried out to evaluate the computation accuracy and fast capabilities of the mainstream parameter adaptive model in various scenarios. Simulations demonstrate the proposed methodology's superiority over traditional adaptive correction schemes.

Performance analysis of the adaptive cycle engine with cooling air: From propulsion performance, thermodynamic efficiency, exergy and infrared characteristics

The adaptive cycle engine gains significant attention due to its variable thermodynamic cycles and multi-duct characteristics, which are utilized for thermal management to enhance engine performance. This paper explores the features of multi-duct technology in adaptive cycle engines and aims to improve engine flight performance. An analysis is conducted on the impact of duct air bleed on engine propulsion characteristics, thermodynamic cycle properties, and exhaust system infrared radiation characteristics. Initially, a component-level model of the adaptive cycle engine was established, considering CDFS duct, bypass duct, and third duct air cooling devices. This model uses the ducted cold air to cool the temperature of low-pressure turbine exit, center cone wall, and main nozzle expansion section wall. Subsequently, a method for analyzing the infrared radiation of the engine exhaust system was proposed based on the above model, and the performance of engine components and the overall engine was analyzed in terms of energy, exergy, mechanical efficiency, and thermal efficiency. Finally, the numerical simulations of the propulsion performance, thermodynamic efficiency and infrared characteristics of the adaptive cycle engine with cooling air were carried out under conditions of ground takeoff, subsonic cruise, and supersonic cruise. The simulation results show that the cooling air can effectively enhance engine performance and infrared stealth capabilities under various flight conditions. The ducted air extraction measures proposed in the paper could effectively improve the propulsion efficiency and stealth characteristics of the engine, providing important references for the development of more efficient aeroengine.

Constrained Model Predictive Control for Generation Power Distribution on Aircraft Engines

Aiming at the increasing demand for electric energy in aircraft in the future, a multi-objective optimization aircraft engine constrained model predictive control method based on generation power distribution is proposed. Firstly, based on the aircraft engine component level model and the equilibrium manifold theory, the aircraft engine equilibrium manifold expansion model is established. Secondly, the influence of the power generation is modeled, and the influence of the low- and high-pressure shaft generators on the normal operation of the aircraft engine is studied and compared. The control variables such as fuel flow and total generation power are taken as the constraint conditions to design the constraint model predictive controller. Furthermore, the multi-objective grey wolf optimization algorithm is introduced to intelligently optimize the parameters of the designed controller. At last, the simulation based on the component level model shows that the high-pressure shaft generator has less influence on the state quantity, including engine thrust, than the low-pressure shaft generator. The proposed control method using the multi-objective gray wolf optimization (MOGWO) algorithm has rapid response and no steady-state error.

Actuator fault diagnosis and severity identification of turbofan engines for steady-state and dynamic conditions

Actuator faults can be critical in turbofan engines as they can lead to stall, surge, loss of thrust and failure of speed control. Thus, fault diagnosis of gas turbine actuators has attracted considerable attention, from both academia and industry. However, the extensive literature that exists on this topic does not address identifying the severity of actuator faults and focuses mainly on actuator fault detection and isolation. In addition, previous studies of actuator fault identification have not dealt with multiple concurrent faults in real time, especially when these are accompanied by sudden failures under dynamic conditions. This study develops component-level models for fault identification in four typical actuators used in high-bypass ratio turbofan engines under both dynamic and steady-state conditions and these are then integrated with the engine performance model developed by the authors. The research results reported here present a novel method of quantifying actuator faults using dynamic effect compensation. The maximum error for each actuator is less than 0.06% and 0.07%, with average computational time of less than 0.0058 s and 0.0086 s for steady-state and transient cases, respectively. These results confirm that the proposed method can accurately and efficiently identify concurrent actuator fault for an engine operating under either transient or steady-state conditions, even in the case of a sudden malfunction. The research results demonstrate the potential benefit to emergency response capabilities by introducing this method of monitoring the health of aero engines.

Synchrophasing control of multiple propellers based on hardware in the loop experimental platform

A hardware in the loop experimental platform is established to evaluate the optimal noise reduction prediction and maintenance capability of multiple propellers under high-precision synchrophasing control. This platform incorporates an online propeller noise model and a digital turboprop engine model into a novel integrated measurement system. Firstly, an improved propeller signature theory using CFD simulation's sound pressure signals is proposed to predict the online propeller noise efficiently. It achieves acceptable noise prediction accuracy using a subset of synchrophase angles to predict noise for all synchrophase angles at all receivers. Secondly, a high-priority interrupt method is proposed for the novel integrated measurement system to guarantee precise measurement and ultimate high-precision synchrophasing control. Thirdly, a turboprop engine model based on a component level model and propeller performance maps' CFD data is also established. To enhance the simulation confidence of the system, we compare the dynamic synchrophasing control effects between systems with and without the integration of a turboprop engine mode. The experimental results demonstrate that the high-priority interrupt method effectively reduces the synchrophase angle(θ) error. These approaches reduce noise by 3.62dB at SPL, exhibit a noise variation within ±0.13dB/°, and effectively manage thrust fluctuation within 4.14%. These results indicate that the method meets the control accuracy and noise reduction requirements in a twin-engined turboprop aircraft.

Research on an inlet-engine hybrid integrated modelling method with pressure dynamic self-tuning

With aircraft flying faster and higher, the interaction between the inlet and engine cannot be ignored. The inlet engine integration characteristics must be considered in engine design and performance evaluation. The total pressure between the inlet and compressor is a critical parameter that directly affects the accuracy of engine performance evaluation. The traditional solution to inlet-engine integrated modelling is to add an external balance equation to the component-level model (CLM), which may reduce real-time performance. In this study, an inlet-engine hybrid integrated modelling (IEHIM) method is proposed to solve this problem, which combines the advantages of the volume effect model and the CLM method. The proposed IEHIM method consists of an engine model, characteristics of the inlet component, and a pressure dynamic self-tuning (PDST) algorithm. The PDST algorithm coefficients were optimised to enhance the performance of the IEHIM method. Finally, the effectiveness of the IEHIM was verified through simulations and ground experiments. The method was also verified on an actual controller (operating frequency: 132 MHz), which exhibited a mass-flow matching error below 0.2 % and an average time consumption of 5.191 ms in each control cycle. The results were also compared with the actual experimental data, where the errors in the compressor entry total pressure and rotor speed were below 1.1520 % and 1.2195 %, respectively.

A novel method for aero-engine map calibration using adaptation factor surface

High-fidelity component-level models (CLMs) are essential for aero-engine performance modeling, monitoring, and diagnosis, relying heavily on precise component maps. Inaccuracies in these maps can cause significant deviations between predicted and actual measurements. This study introduces a novel wide-range performance adaptation method (WPAM) utilizing adaptation factor surfaces, enabling precise adjustment of speed line operating points. The adaptation factors for the component maps are calculated based on both steady-state and transient measurements using the Levenberg-Marquardt method, with transient adaptation calculations converted into a steady-state solution. Adaptation factor surfaces are constructed using the moving least squares method to modify component maps in both spool speed and β-value directions. Validated on two engines with distinct maps, WPAM significantly enhances CLM accuracy under steady-state and transient conditions, considering multidimensional engine and CLM map discrepancies. It is applicable to degraded and variable geometry engines, exhibiting robustness in handling noisy measurement data.

Prediction of engine combustion chamber outlet temperature field based on deep Learning: Application in aero-engine life extension control

One of the current research hotspots is the prediction of combustion chamber flow field based on deep learning methods, but how to effectively utilize these prediction results is a key issue that needs to be addressed urgently. This paper further proposes a study on the predictive control method for engine life extension considering the dangerous point temperature of turbine guide vane based on the prediction of combustion chamber outlet temperature field. Firstly, a combustion chamber outlet temperature distribution prediction model is established through Computational Fluid Dynamics (CFD) numerical simulation and inception deconvolution network, and integrated into the variable cycle engine component level model to achieve real-time prediction of combustion chamber outlet temperature distribution. Furthermore, based on this, the dangerous point temperature of the high-pressure turbine (HT) guide vanes is introduced as a constraint into the prediction optimization, and a predictive control method for engine life extension considering the thermal mechanical fatigue life (TMFL) of the guide vanes is proposed. The simulation results show that compared to the traditional LEC control method, the temperature at the critical point of the blade is reduced by 12.28 K, and the TMFL of the blade is increased by 29.23 %. The research results of this article provide a good application example for predicting the temperature distribution at the outlet of the combustion chamber, expanding the application scenarios for predicting the flow field parameters inside the engine.

Multi-fidelity simulation of aeroengine for far-off-design conditions using iterative coupled method based on auxiliary maps

Iterative coupled methods are widely used in multi-fidelity simulation of rotating components due to the simple implementation, which iteratively eliminates the errors between the computational fluid dynamics models and approximate characteristic maps. However, the convergence and accuracy of the iterative coupled method are trapped in characteristic maps. In particular, iterative steps increase sharply as the operation point moves away from the design point. To address these problems, this paper developed an auxiliary iterative coupled method that introduces the static-pressure-auxiliary characteristic maps and modification factor of mass flow into the component-level model. The developed auxiliary method realized the direct transfer of static pressure between the high-fidelity models and the component-level model. Multi-fidelity simulations of the throttle characteristics were carried out using both the auxiliary and traditional iterative coupled methods, and the simulation results were verified using the experimental data. Additionally, the consistency between the auxiliary and traditional iterative coupled methods was confirmed. Subsequently, multi-fidelity simulations of the speed and altitude characteristics were also conducted. The auxiliary and traditional iterative coupled methods were evaluated in terms of convergence speed and accuracy. The evaluation indicated that the auxiliary iterative coupled method significantly reduces iterative steps by approximately 50% at the near-choked state. In general, the auxiliary iterative coupled method is preferred as a development of the traditional iterative coupled method in the near-choked state, and the combined auxiliary-traditional iterative coupled method provides support for successful multi-fidelity simulation in far-off-design conditions.

Aero-engine direct thrust control based on nonlinear model predictive control with composite predictive model

Abstract A novel NMPC (Nonlinear Model Predictive Control) based on composite predictive model is proposed and applied to direct engine thrust control. To improve the real-time of NMPC, an adaptive composite model based on SVM (State Variable Model), KF (Kalman Filter), and CLM (Component Level Model) is proposed as predictive model. The correction theory is adopted to establish a full envelope adaptive on-board predictive dynamic model and reduce the data storage of predictive model. At each sampling time, the CLM is calculated only once in the proposed NMPC, instead of many times in the popular NMPC based on EKF (extended Kalman filler). Therefore, the proposed NMPC has better real-time performance than the popular one. The simulations that consist of the proposed NMPC, the popular NMPC based on EKF, and the traditional controller PID are conducted. The simulations demonstrate that the proposed NMPC not only has greatly better real time performance than popular NMPC, but also has faster response speed than traditional controller PID.

A novel method for aero-engine time-series forecasting based on multi-resolution transformer

Time-series forecasting is widely studied in the data-driven component-level modeling, fault diagnosis, and performance prediction of aero-engines. The operational data of aero-engines have the characteristics of complexity and nonlinearity. This paper proposes a multi-resolution transformer (MRT) model for the non-steady state process of aero-engines. This transformer-based model can learn temporal patterns of different scales by performing multi-resolution down-sampling and patch-based tokeniczation on the input time-series. On this basis, a two-stage multi-resolution transformer (TSMRT) framework is proposed for the entire processes time-series modeling of aero-engines. The TSMRT framework employs Augmented Dickey-Fuller (ADF) test to classify aero-engine operational data into steady state processes and non-steady state processes, and models the two processes separately using time–frequency feature neural network (TFNN) model and MRT model. The time-series forecasting performance of the MRT model is validated on three publicly available benchmark datasets and an ablation study is conducted on turbofan engine test bench datasets. The results indicate that the TSMRT framework demonstrates superior forecasting performance across steady state, non-steady state, and entire processes. This framework stands out as a novel method for component-level modeling of aero-engines.

Research on performance seeking control of turbofan engine in minimum hot spot temperature mode

Abstract The uneven temperature distribution at the combustion chamber outlet seriously affects the working life of the engine. In order to reduce the heat spot temperature at the combustion chamber outlet, a performance optimization control method of the engine minimum heat spot temperature pattern is proposed. Firstly, based on CFD method, the temperature distribution characteristics of combustion chamber outlet under different working conditions were obtained, and a component-level model of turbofan engine was established to characterize the heat spot temperature at combustion chamber outlet. Secondly, the high precision and high real-time engine on-board model is established by deep neural network. Compared with the component-level model, the average relative error of each performance parameter is less than 0.3 %, and the real-time performance is improved by 12 times. Finally, based on the feasible sequential quadratic programming algorithm, the performance optimization control of the minimum hot spot temperature model in the typical flight envelope is simulated and verified. The simulation results show that under the condition of ensuring the safe and stable operation of the engine and constant thrust, the heat spot temperature at the combustion chamber outlet decreases by 21 K maximum. Compared with the conventional minimum turbine front temperature optimization mode, the minimum heat spot temperature mode significantly reduces the heat spot temperature at the combustion chamber outlet.

An Improved Onboard Adaptive Aero-Engine Model Based on an Enhanced Neural Network and Linear Parameter Variance for Parameter Prediction

Achieving measurable and unmeasurable parameter prediction is the key process in model-based control, for which an accurate onboard model is the most important part. However, neither nonlinear models like component level models or LPV models, nor linear models like state–space models can fully meet the requirements. Hence, an original ENN-LPV linearization strategy is proposed to achieve the online modelling of the state–space model. A special network structure that has the same format as the state–space model’s calculation was applied to establish the state–space model. Importantly, the network’s modelling ability was improved through applying multiple activation functions in the single hidden layer and an experience pool that records data of past sampling instants, which strengthens the ability to capture the engine’s strongly nonlinear dynamics. Furthermore, an adaptive model, consisting of a component-level model with adaptive factors, a linear Kalman filter, a predictive model, an experience pool, and two ENN-LPV networks, was developed using the proposed linearization strategy as the core process to continuously update the Kalman filter and the predictive model. Simulations showed that the state space model built using the ENN-LPV linearization strategy had a better model identification ability in comparison with the model built using the OSELM-LPV linearization strategy, and the maximum output error between the ENN-LPV model and the simulated engine was 0.1774%. In addition, based on the ENN-LPV linearization strategy, the adaptive model was able to make accurate predictions of unmeasurable performance parameters such as thrust and high-pressure turbine inlet temperature, with a maximum prediction error within 0.5%. Thus, the effectiveness and the advantages of the proposed method are demonstrated.

An enhanced non-iterative real-time solver via multilayer perceptron for on-board component-level models

The real-time on-board component-level model (CLM) of gas turbine engines (GTEs) is an important foundation for fault-tolerant control and health management. However, traditional iterative solving algorithms typically rely on the multiple computations of gas-path thermodynamic parameters, which seriously limits their practical application. In this paper, aiming to improve the real-time performance of CLM, an enhanced non-iterative real-time solver (ENRS) method is proposed. It has combined an input selection strategy, an under-sampling supplemental enhancement training (USET) technique, and a multilayer perceptron (MLP) network. Compared with the existing CLM solving methods, its innovations are as follows: (1) a novel framework for directly solving CLM is proposed, which helps get rid of the dilemma of increased time consumption due to multiple iterations. (2) an enhanced training process is designed for the MLP network, which helps improve the adaptivity to the full flight envelope and all engine states, especially to reduce the maximum error. Simulation tests eventually show that the ENRS effectively improves the real-time performance of CLM in practical applications, and maintains solution accuracy and robustness.

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

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.

Real-time improvement method of turbo-shaft engine component-level model based on dimensional reduction of residual iterative equation

This paper explores a method to improve the real-time computation performance of turbo-shaft engine component-level model by reducing the dimensionality of residual equations set. For turbo-shaft engine, nozzle typically operates in a subcritical working state, assuming that the gas total temperature and pressure parameters of power turbine inlet are the same, and by assuming the gas flow balance between gas generator outlet and nozzle inlet, the iterative solution of the pressure drop of nozzle can be obtained. Then the pressure ratio of power turbine is naturally obtained, thereby achieving the objective of dimensionality reduction of the residual equations set. The component-level model’s steady-state and transient performance were simulated on a desktop computer with a clock frequency of 2.6 GHz. Simulation results show that, with a 2% margin of error, the steady state computation time for the component-level model was reduced by 54.8%, while the transient performance computation time was reduced by 18.6%. This represents a noticeable improvement in real-time performance. Simulation results on the P2020 embedded processor platform with an 800MHz clock frequency indicate a 22.3% reduction in single-step maximum transient performance computation time of the reduced dimensional model, compared to the original model. This demonstrates the effectiveness of dimensionality reduction of the residual equations set in enhancing the model real-time computation performance.

Developing and correction methods for a one-dimensional axial flow compressor model

This paper establishes a one-dimensional model of an axial flow compressor using the Average Mid-Diameter Method and employs polynomials to adjust the off-design stagger angle and off-design loss models. Furthermore, a model stepwise correction strategy based on the Particle Swarm Optimization (PSO) algorithm is proposed. The accuracy of the corrected one-dimensional axial flow compressor model was verified and integrated into the overall engine model for validation. Simulation results indicate that the maximum error in calculating the characteristics of the established one-dimensional axial flow compressor model does not exceed 6%, and the maximum error in simulating the overall engine model remains below 4%. These findings validate the effectiveness of modeling and correction methods for a one-dimensional axial flow compressor model targeting aero-engine component-level model.

A model-based control strategy for turbo-shaft engine

This paper takes the turbo-shaft engine (the power source of the helicopter) as the research object, and mainly conducts the simulation of its related control law and control structure. The working principle of the turbo-shaft engine is briefly introduced initially, and then the component-level model of the turbo-shaft engine is established. This paper focuses on the design of the control law upon the turbo-shaft engine, and verifies the designed control law through the results of simulation by MATLAB/Simulink. Finally, the relevant data collected from 240KW hybrid system is taken into the consideration to verify the control algorithm. The results show that the control accuracy of the corresponding control algorithm in this paper is high (accuracy≤5%).

An improved high-fidelity adaptive model for integrated inlet-engine-nozzle based on mechanism-data fusion

Nowadays, there has been an increasing focus on integrated flight propulsion control and the inlet-exhaust design for the aero-propulsion system. Traditional component-level models are inadequate due to installed performance deviations and mismatches between the real engine and the model, failing to meet the accuracy requirements of supersonic conditions. This paper establishes a quasi-one-dimensional model for the inlet-exhaust system and conducts experimental calibration. Additionally, a mechanism-data fusion adaptive modeling scheme using an Extreme Learning Machine based on the Salp Swarm Algorithm (SSA-ELM) is proposed. The study reveals the inlet model's efficacy in reflecting installed performance, flow matching, and mitigating pressure distortion, while the nozzle model accurately predicts flow coefficients and thrust coefficients, and identifies various operational states. The model's output closely aligns with typical experimental parameters. By combining offline optimization and online adaptive correction, the mechanism-data fusion adaptive model substantially reduces output errors during regular flights and varying levels of degradation, and effectively handles gradual degradation within a single flight cycle. Notably, the mechanism-data fusion adaptive model holistically addresses total pressure errors within the inlet-exhaust system and normal shock location correction. This approach significantly curbs performance deviations in supersonic conditions. For example, at Ma = 2.0, the system error impressively drops from 34.17% to merely 6.54%, while errors for other flight conditions consistently stay below the 2.95% threshold. These findings underscore the clear superiority of the proposed method.