Turbofan Engine Model Research Articles

On-Design Operation and Performance Characteristic of Custom Engine

The purpose of this study is to investigate the design point performance of a custom engine via GasTurb software. In this study, a turbojet engine model is simulated without afterburners and limited to design point (DP) simulation at a speed of 15,000 rpm. The input parameters such as pressure ratio (PR) for the main components, the mechanical and burner efficiency, and isotropic PR for compressor and turbine have been identified for a custom engine as a design point. The results compared at different levels of the condition using GasTurb-13 and GSP-11 software. It was found that each software was able to provide similar results at various conditions tested. There are small differences in the values for the fuel flow and specific fuel consumption. Also, the same results were obtained at the baseline point. Furthermore, the heating value has a primary effect on specific fuel consumption. It was also found that the optimal thrust value was at 34.2 kN, and the best value for optimal specific fuel consumption was 20.9 g/kN.s. The main factors affecting biofuel properties are calorific value and viscosity. When the calorific value of the fuel is reduced, the thrust FN and specific fuel consumption increase. For example, Methanol and Ethanol recorded the highest amount of fuel consumption, which is 54.72 g/KN.s and 47.56 g/(KN.s), respectively. This is because they have the highest mass fuel flow ( 1.79 kg/s for Methanol, and 1.54 kg/s for Ethanol) than other types of fuel, while the mass fuel flow for green diesel (0.78 kg/s) was lower than other fuels, so its specific fuel consumption (22.11 g/(KN.s) was lesser than other fuels.

Dwell-Time-Based Exponential Stabilization of Switched Positive Systems With Actuator Saturation

This article investigates the exponentially stabilization problem for a class of switched positive systems with actuator saturation under dwell time switching signal. The state feedback controllers for each subsystem are designed by proposing a new type of multiple time-varying linear co-positive Lyapunov functions (MTVLCLFs), and sufficient conditions for the exponentially stabilization of the studied systems are proposed under dwell-time switching. In order to cope with actuator saturation, the convex hull technique is utilized. Finally, an example of a turbofan engine model is presented to show the effectiveness of our proposed method.

Comprehensive Performance Analysis of the Turbofan With a Multi-Annular Rotating Detonation Duct Burner

Abstract The performance analysis of mixed-exhaust turbofan engine with multi-annular rotating detonation duct burner (RDDB) is conducted for the first time, considering that the flow path of the bypass duct is ideal for a rotating detonation combustor (RDC). The configuration of the multi-annular rotating detonation combustor is constructed aiming at the advantages of a wider operation range and uniform outlet parameters over the single-annular one. Then, a parametric analysis model of the mixed-exhaust turbofan engine with a rotating detonation duct burner is developed. Thereafter, the effects of duct burner parameters on the engine performance and operating characteristics are investigated. The mixed-exhaust turbofan engine with a rotating detonation duct burner shows superior overall performance to that of one with an isobaric afterburner (ICAB) over a wide operation range. The separate-exhaust rotating detonation duct burner can hold characteristics that are higher than those of the mixed-exhaust one at lower values of fan pressure ratio, while the mixed-exhaust one corresponds to lower values of turbine inlet temperature. When the rotating detonation duct burner is “on,” the low-pressure rotor operating line moves toward the surge line on the low corrected shaft speed side but away from the surge line on the high corrected shaft speed side.

Advanced Constraints Management Strategy for Real-Time Optimization of Gas Turbine Engine Transient Performance

Motivated by the growing technology of control and data processing as well as the increasingly complex designs of the new generation of gas turbine engines, a fully automatic control strategy that is capable of dealing with different aspects of operational and safety considerations is required to be implemented on gas turbine engines. An advanced practical control mode satisfaction method for the entire operating envelope of gas turbine engines is proposed in this paper to achieve the optimal transient performance for the engine. A constraint management strategy is developed to generate different controller settings for short-range fighters as well as long-range intercontinental aircraft engines at different operating conditions by utilizing a model predictive control approach. Then, the designed controller is tuned and modified with respect to different realistic considerations including the practicality, physical limitations, system dynamics, and computational efforts. The simulation results from a verified two-spool turbofan engine model and controller show that the proposed method is capable of maneuverability and/or fuel economy optimization indices while satisfying all the predefined constraints successfully. Based on the parameters, natural frequencies, and dynamic behavior of the system, a set of optimized weighting factors for different engine parameters is also proposed to achieve the optimal and safe operation for the engine at different flight conditions. The paper demonstrates the effects of the prediction length and control horizon; adding new constraints on the computational effort and the controller performance are also discussed in detail to confirm the effectiveness and practicality of the proposed approach in developing a fully automatic optimized real-time controller for gas turbine engines.

Aerodynamic Thermodynamic Modeling and Simulation of Turbofan Engine

The mathematical model of turbofan engine is established and simulation calculation is carried out in this paper. An engine component model is established by means of the gas flow path of the engine. Then, according to the common working conditions, common working equations are established and the N+1 residual method is used to solve the nonlinear equations. The general model of an engine is established based on the design point parameters, and the specific performance parameters of the intermediate state are calculated under standard atmospheric conditions. The simulation results are compared with the measured performance data of the engine, which shows that: there are errors between the calculated value of the model and the measured value of the engine. The maximum error is 2.85%, which meets the requirement of model error less than 3% in general engineering.

Sensor Fault Tolerant Control for Aircraft Engines Using Sliding Mode Observer

This paper investigated the problem of fault estimation and fault-tolerant control (FTC) against sensor faults for aircraft engines. By applying a second order sliding mode observer (SOSMO) to the engine on-board model, estimations of the system states and sensor faults could be obtained simultaneously, and the result of state estimation was unaffected when using the reduced-order sliding mode system. This result gave rise to the idea to use the estimated states instead of physical sensor signal in the engine close-loop feedback control. Unlike those using passive FTC concepts, the tradeoff between control performance and robustness was inherently unnecessary. Meanwhile, compared to active FTC approaches, because any classical state/output feedback method can be directly applied to the proposed scheme without any controller reconfiguration, extra undesired dynamic responses caused by parameter reconfiguring were avoided. In this paper, the proposed FTC scheme was tested on the nonlinear model of a civil aircraft turbofan engine, and numerical simulation results showed satisfactory sensor FTC performance.

Performance analysis of turbofan engine with additional combustion chamber fueled by alternative fuel

Presented paper is focused on the performance analysis of turbofan engine with additional combustion chamber fueled by alternative fuel. Additional combustion chamber was situated between high pressure turbine and low pressure turbine. Such configuration is also know under the name inter turbine burner (ITB). As an alternative fuels burn in additional combustion chamber were selected cryogenics fuels: liquid hydrogen and liquid methane. The main combustion chamber was supplied by conventional aviation fuel - kerosene. Thermodynamic model of turbofan engine with ITB was implemented in MATLAB environment. The problem of modeling an additional combustion chamber was presented.Analysis was carried out for take-off conditions. Obtained results indicate that implementation of ITB exert a positive influence on engine performance result in thrust increase.

A neuro-inspired computational model for adaptive fault diagnosis

Fault diagnosis is a key process to ensure reliable and cost-effective performance of time-critical engineered systems. This article develops a data-driven computational model for adaptive fault diagnosis by drawing an analogy with the neurobiological process of conscious attention—a dynamic process that brings only the most novel 0.01% of the signals we receive with our five senses to our conscious experience. A model of conscious attention based on the theory of dynamic core hypothesis is first outlined, followed by a computational model that mimics key stages of the conscious attention process. Convolutional neural networks serve as a basis for modeling perceptual categorization and concept formation through automatic feature extraction, due to their analogy with the processes of neural group selection and reentry in the brain. Further, the process of incremental learning and its impact on signal novelty are modeled via transfer learning. The model is tested on the NASA C-MAPSS turbofan engine model, which indicated 95–99% fault diagnosis accuracy. This study aims at familiarizing the engineering community with the neurobiological process of conscious attention and its applications for adaptive process monitoring and improvement in engineered systems.

Fast engine model for FMU-less small turbojet engines

The focus of this paper is on small low-cost turbojet engines equipped with a gear-type fuel pump rather than a more traditional fuel metering unit. The incorporation of such type of fuel flow actuation devices introduce additional nonlinearities into the system and therefore make traditional modeling and system identification methods difficult to apply. In this paper, we propose a nonlinear fast engine model structure that can be used for various applications, including identification, modeling and simulation, and controller design of such sub-class turbojet engines. A high-fidelity turbojet engine model including its nonlinear gear-type fuel pump is developed, which is later used to generate the fast engine model. The parameters of the fast engine model are estimated using regression analysis. The identification procedure is also applied to real engine test data to verify the proposed approach.

Reference and Limit Governors for Limit Protection of Turbofan Engines

This paper proposes a novel architecture of limit protection including the references governors and limit governors and applies this architecture to limit protection in turbofan engines. References governors are designed as add-on schemes to a pre-stability engine control system that modifies reference commands to avoid constraints violation. Limit governors are proposed as an assistant part for references governors adjusting constraints to prevent references from stopping updates. The use of output admissible sets for a class of variable constraints is exploited to form invariant sets. Simulation results based on a turbofan engine model show that references governors with limit governors can effectively enforce the multiple constraints and provide enhanced engine thrust when steady violation occurs.

H∞ bumpless transfer reliable control of Markovian switching LPV systems subject to actuator failures

In this paper, the H∞ bumpless transfer reliable control problem of Markovian switching linear parameter-varying (LPV) systems subject to actuator failures is addressed. A bumpless transfer reliable control constraint is introduced to describe the transient performance for such systems. By the use of the parameter-dependent Lyapunov function approach to reduce control bumps generated by the Markovian switching, a reliable switching controller which is also parameter-dependent is constructed to attenuate external disturbances. A criterion checking the solvability of the H∞ bumpless transfer reliable control problem is provided. As an application, a turbofan-engine model is used to verify the validity of the proposed design method.

Modeling of the turbofan with an ejector nozzle based on infrared prediction

In order to study the effect of ejector nozzle on the overall performance of turbofan engine, a turbofan engine model with ejector nozzle is established in this paper, which includes parameter cycle analysis module and performance cycle analysis module. A backward prediction method of infrared radiation intensity for exhaust system is proposed, which includes solid wall and gas infrared radiation intensity prediction. The infrared characteristics and overall performance of the turbofan engine with ejector nozzle are analyzed and calculated in the flight missions. Firstly, the simulation model of turbofan engine with ejector nozzle is established and the influence of ejector bypass ratio on thrust performance of engine is studied. Secondly, the prediction model of engine infrared radiation intensity is established based on the prediction method of infrared radiation intensity. Finally, numerical simulations were carried out for different bypass ratios and different flight missions.

Analyzing different numerical linearization methods for the dynamic model of a turbofan engine

State equations of aircraft engine dynamics usually required for controller design, are not available in closed form, so the dynamic models are commonly linearized numerically. Development of model-based controllers for aeroengine in the recent years necessitates the use of accurate linear models. However, there is no comprehensive study about the accuracy of the linear models obtained from nonlinear engine models. In this paper, the accuracy of different numerical linearization methods for linearizing the dynamic model of a turbofan engine is investigated. For this objective, a thermodynamic model of a two-spool turbofan engine is considered and three various numerical linearization methods are defined. The first method is based on the perturbation technique, including ordinary and central difference perturbation. The second one is a system identification method and the third one is tuning the elements of the matrices of the linear state-space model using genetic algorithm. The accuracy analysis of the presented procedures is performed for both single-input and double-input cases. In the single-input case, the fuel mass flow rate and in the double-input, in addition to the fuel, the bleed air taken from between the two compressors are considered as control variables. Finally, by defining different error criterions, the accuracy of the linearization methods is evaluated. The results show that the linear model obtained from system identification and central difference perturbation methods have higher percentage of compliances compared to the others.

A Linearization Model of Turbofan Engine for Intelligent Analysis Towards Industrial Internet of Things

Big data processing technologies, e.g., multi-sensor data fusion and cloud computing are being widely used in research, development, manufacturing, health monitoring and maintenance of aero-engines, driven by the ever-rapid development of intelligent manufacturing and Industrial Internet of Things (IIoT). This has promoted rapid development of the aircraft engine industry, increasing the aircraft engine safety, reliability and intelligence. At present, the aero-engine data computing and processing platform used in the industrial Internet of things is not complete, and the numerical calculation and control of aero-engine are inseparable from the linear model, while the existing aero-engine model linearization method is not accurate enough to quickly calculate the dynamic process parameters of the engine. Therefore, in this paper, we propose a linear model of turbofan engine for intelligent analysis in IIoT, with the aim to provide a new perspective for the analysis of engine dynamics. The construction of the proposed model includes three steps: First, a nonlinear mathematical model of a turbofan engine is established by adopting the component modeling approach. Then, numerous parameters of the turbofan engine components and their operating data are obtained by simulating various working conditions. Finally, based on the simulated data for the engine under these conditions, the model at the points during the dynamic process is linearized, such that a dynamic real-time linearized model of turbofan engine is obtained. Simulation results show that the proposed model can accurately depict the dynamic process of the turbofan engine and provide a valuable reference for designing the aero-engine control system and supporting intelligent analysis in IIoT.

Aero-Engine On-Board Model Based on Batch Normalize Deep Neural Network

A new on-board turbo-fan engine modeling method based on a batch normalize (BN) mini-batch gradient descent (MGD) deep neural network (NN) is proposed. This new method adopts BN algorithm, which accelerates the network training speed and overcomes the gradient vanish problem. Hence, using the BN algorithm, the neural network adopts the deeper structure, which means the network has a stronger representation capacity. This mini-batch gradient descent (MGD-NN) algorithm that consumes much less time to update the NN parameters is adopted. Therefore, it is more suitable for training big dataset and establishing a high-accuracy engine model in a large flight envelope. Finally, to verify whether the proposed method could be applied to larger flight envelope, the conventional NN also adopts MGD (called MGD-NN). The turbo-fan engine models based on these two modeling methods are both conducted within a sub-sonic cruise envelope. The simulation results show that the proposed modeling method has much higher accuracy than the MGD-NN. Moreover, the proposed method has the characteristics of less data storage, low computation complexity, and good real-time performance, which are the most importance indices for model realize on-board.

A Novel Control Scheme for Aircraft Engine Based on Sliding Mode Control With Acceleration/Deceleration Limiter

To provide the desired thrust and prevent the engine from exceeding any safety or operational limits, a min–max selector with linear limiters is widely employed in current aircraft engine control logic. However, with the further requirements of engine performance, the traditional linear limiters should be improved. Though there are many researchers working on the development of improvement methods, none of those methods consider the limitation of core shaft acceleration. In this paper, a novel control scheme for aircraft engine based on sliding mode control with acceleration/deceleration limiter is proposed. Above all, the controller construction process is introduced, and the asymptotic stability of the whole controller is given. Then, with linearized model of JT9D turbofan engine, the control performance of the new approach is presented, which is also compared with the traditional methods. The simulation results show that the proposed method is efficient, and it can ensure all outputs of the controller, including the core shaft acceleration $\dot {N}_{c}$ , high-pressure turbine outlet temperature increment $\Delta T_{48}$ , high-pressure compressor stall margin increment $\Delta SmHPC$ , and so on, are well controlled.

Methods for assessment of NO<SUB align="right">x emissions from aircraft engine

During the last decade a lot of investigations also focused on the evaluation of the impact of aircraft engine emissions on the local and regional air quality in the vicinity of the airport. The certificated emission indices rely on well-defined measurement procedures and conditions during engine test. Under real circumstances, however, operational (power setting, time-in-mode and fuel flow rate) and meteorological (air temperature, humidity and pressure) conditions may vary from ICAO definition, consequently deviations from the certificated emission indices may occur. The advanced emission model of turbofan engine (TURBOGAS emission model) was developed for the assessment of aircraft engine emissions taking into account the influences of operational and meteorological conditions on emission indexes to generate precise aircraft emissions inventories as well as contrails likelihood and lifetime. Both BFFM2 and P3T3 methods have been implemented in the TURBOGAS emission model.

Methods for assessment of NO<SUB align="right">x emissions from aircraft engine

During the last decade a lot of investigations also focused on the evaluation of the impact of aircraft engine emissions on the local and regional air quality in the vicinity of the airport. The certificated emission indices rely on well-defined measurement procedures and conditions during engine test. Under real circumstances, however, operational (power setting, time-in-mode and fuel flow rate) and meteorological (air temperature, humidity and pressure) conditions may vary from ICAO definition, consequently deviations from the certificated emission indices may occur. The advanced emission model of turbofan engine (TURBOGAS emission model) was developed for the assessment of aircraft engine emissions taking into account the influences of operational and meteorological conditions on emission indexes to generate precise aircraft emissions inventories as well as contrails likelihood and lifetime. Both BFFM2 and P3T3 methods have been implemented in the TURBOGAS emission model.

Joint Steady State and Transient Performance Adaptation for Aero Engine Mathematical Model

In the field of aero-engine control, it is valuable to build highly accurate component level models to meet the requirements of controller validation and model-based control. The accuracy of current performance map generation method is limited by the test data that always cannot cover the full envelope. The scaling-based performance map correction methods only focus on the adjustment of the steady-state performance. Therefore, a performance map segmentation-based joint steady-state and transient performance adaptation technique is proposed. Both the idle point and the design point are taken as precision references to scale the performance maps with large deviations. A scaling factor domain determination method is provided based on the characters of the speed lines. The performance map is first optimized with the steady state calculation based on the steady-state test data, and further optimized with the transient calculation based on the transient test data. Both the steady-state and transient performance adaptations are achieved by adjusting the performance maps. The proposed method was applied to a turbofan engine model. For continuous acceleration case, the average error is less than 1% at steady state points and less than 2% during transient operation. For large magnitude acceleration and rapid deceleration cases, the maximum errors for parameters around the idle point decrease by about one time (from 8.3% to 4.1%) and three and a half times (from 28% to 8%), respectively, by comparing with the model without transient performance adaptation. Thus, the effectiveness of the proposed method is demonstrated.

Deep Belief Network-Based Gas Path Fault Diagnosis for Turbofan Engines

A gas path fault diagnosis scheme for turborfan engines based on deep belief network (DBN) is presented. The scheme is constructed according to the diagnosis principles of gas path faults and is composed of a turbofan engine reference model and a DBN diagnosis model. The DBN diagnosis model is a stacked network of several restricted Boltzmann machines (RBM) and was trained with the contrastive divergence algorithm and the back propagation algorithm. To optimize the DBN performance, the orthogonal tests L25 (57) were adopted to determine the hyper-parameters, such as learning rate, hidden layer number, hidden layer neuron number, etc. The proposed DBN-based scheme was applied to diagnose the gas path faults of a turbofan engine model and compared with BP-based and SVM-based schemes. The results show that the fault diagnosis accuracy of the DBN-based scheme is as high as 96.59%, and the DBN-based scheme has dramatic performance advantages over the other two schemes.