Design Considerations of Low Bypass Ratio Mixed Flow Turbofan Engines with Large Power Extraction

The possibility of extracting large amounts of electrical power from turbofan engines is becoming increasingly desirable from an aircraft perspective. The power consumption of a future fighter aircraft is expected to be much higher than today’s fighter aircraft. Previous work in this area has concentrated on the study of power extraction for high bypass ratio engines. This motivates a thorough investigation of the potential and limitations with regards to performance of a low bypass ratio mixed flow turbofan engine. A low bypass ratio mixed flow turbofan engine was modeled, and key parts of a fighter mission were simulated. The investigation shows how power extraction from the high-pressure turbine affects performance of a military engine in different parts of a mission within the flight envelope. An important conclusion from the analysis is that large amounts of power can be extracted from the turbofan engine at high power settings without causing too much penalty on thrust and specific fuel consumption, if specific operating conditions are fulfilled. If the engine is operating (i) at, or near its maximum overall pressure ratio but (ii) further away from its maximum turbine inlet temperature limit, the detrimental effect of power extraction on engine thrust and thrust specific fuel consumption will be limited. On the other hand, if the engine is already operating at its maximum turbine inlet temperature, power extraction from the high-pressure shaft will result in a considerable thrust reduction. The results presented will support the analysis and interpretation of fighter mission optimization and cycle design for future fighter engines aimed for large power extraction. The results are also important with regards to aircraft design, or more specifically, in deciding on the best energy source for power consumers of the aircraft.

Optimization-based transient control of turbofan engines: a sequential quadratic programming approach

Engine transient control has been challenging due to its stringent requirements from both performance and safety. Many methodologies have been proposed such as conventional schedule-based methods, linear parameter varying, multiobjective optimization and evolutionary computations etc. These approaches have been well-established and led to a series of significant results. However, they are either not providing limit protection or requiring exhaustive computational resources, particularly when generating results into full flight envelope applications. Consequently a compromise between limit protection and computational complexity is necessitated. This note considers a sequential quadratic programming (SQP)-based method for full flight envelope investigations. The proposed method can provide important design guidance and the corresponding claims are validated through detailed analysis and simulations.

Data-Driven Models for Gas Turbine Online Diagnosis

The lack of gas turbine field data, especially faulty engine data, and the complexity of fault embedding into gas turbines on test benches cause difficulties in representing healthy and faulty engines in diagnostic algorithms. Instead, different gas turbine models are often used. The available models fall into two main categories: physics-based and data-driven. Given the models’ importance and necessity, a variety of simulation tools were developed with different levels of complexity, fidelity, accuracy, and computer performance requirements. Physics-based models constitute a diagnostic approach known as Gas Path Analysis (GPA). To compute fault parameters within GPA, this paper proposes to employ a nonlinear data-driven model and the theory of inverse problems. This will drastically simplify gas turbine diagnosis. To choose the best approximation technique of such a novel model, the paper employs polynomials and neural networks. The necessary data were generated in the GasTurb software for turboshaft and turbofan engines. These input data for creating a nonlinear data-driven model of fault parameters cover a total range of operating conditions and of possible performance losses of engine components. Multiple configurations of a multilayer perceptron network and polynomials are evaluated to find the best data-driven model configurations. The best perceptron-based and polynomial models are then compared. The accuracy achieved by the most adequate model variation confirms the viability of simple and accurate models for estimating gas turbine health conditions.

THE INFLUENCE OF PURGE FLOW PARAMETERS ON HEAT TRANSFER AND FILM COOLING IN TURBINE CENTER FRAMES

The imperative improvement in the efficiency of turbofan engines is commonly facilitated by increasing the turbine inlet temperature. This development has reached a point where also components downstream of the high-pressure turbine have to be adequately cooled. Such a component is the turbine center frame (TCF), known for a complex aerodynamic flow highly influenced by purge-mainstream interactions. The purge air, being injected through the wheelspace cavities of the upstream high-pressure turbine, bears a significant cooling potential for the TCF. Despite this, fundamental knowledge of the influencing parameters on heat transfer and film cooling in the TCF is still missing. This paper examines the influence of purge-to-mainstream blowing ratio, density ratio and purge swirl angle on heat transfer and film cooling in the TCF. The experiments are conducted in a sector-cascade test rig specifically designed for such heat transfer studies using infrared thermography and tailor-made flexible heating foils with constant heat flux. Three purge-to-mainstream blowing ratios and an additional no purge case are investigated. The purge flow is injected without swirl and also with engine-similar swirl angles. The purge swirl and blowing ratio significantly impact the magnitude and the spread of film cooling in the TCF. Increasing blowing ratios lead to an intensification of heat transfer. By cooling the purge flow, a moderate variation in purge-to-mainstream density ratio is investigated, and the influence is found to be negligible.

Study on the biparametrical transudations circuits with distributed parameters

This paper highlights the methods of resenting mode in the transudation with distributed parameters. The transient parameters of the power supply were analyzed. It was discovered that resonance is provided in a small range of movement of the moving component of the sensor in known turbofan engines, indicating that the known techniques of sustaining the resonance mode are flawed. Further study should focus on developing novel methods for preserving resonance mode over the entire range of change of the converted value, general principles of turbojet engine construction, and a complete examination of their resonant circuits, according to the findings.

Semigroup of Finite-State Deterministic Intuitionistic Fuzzy Automata with Application in Fault Diagnosis of an Aircraft Twin-Spool Turbofan Engine

The twin-spool turbofan engine is an important component of almost every modern aircraft. Fault detection at an early stage can improve engine performance and health. The current research is based on the construction of an inference system for fault diagnosis in a generalized fuzzy environment. For such an inference system, finite-state deterministic intuitionistic fuzzy automata (FDIFA) are established. A semigroup of FDIFA and its algebraic properties including substructures and structure-preserving maps are studied. The FDIFA semigroups are used as variables for the inference system, and FDIFA semigroup homomorphisms are used to indicate the relation between variables. The newly established model is then applied to diagnose the possible fault and their nature in aircraft twin-spool turbofan engines by modelling the performance of the supercharger and air cooler.

Deep-Learning Based Prognosis Approach for Remaining Useful Life Prediction of Turbofan Engine

The entire life cycle of a turbofan engine is a type of asymmetrical process in which each engine part has different characteristics. Extracting and modeling the engine symmetry characteristics is significant in improving remaining useful life (RUL) predictions for aircraft components, and it is critical for an effective and reliable maintenance strategy. Such predictions can improve the maximum operating availability and reduce maintenance costs. Due to the high nonlinearity and complexity of mechanical systems, conventional methods are unable to satisfy the needs of medium- and long-term prediction problems and frequently overlook the effect of temporal information on prediction performance. To address this issue, this study presents a new attention-based deep convolutional neural network (DCNN) architecture to predict the RUL of turbofan engines. The prognosability metric was used for feature ranking and selection, whereas a time window method was employed for sample preparation to take advantage of multivariate temporal information for better feature extraction by means of an attention-based DCNN model. The validation of the proposed model was conducted using a well-known benchmark dataset and evaluation measures such as root mean square error (RMSE) and asymmetric scoring function (score) were used to validate the proposed approach. The experimental results show the superiority of the proposed approach to predict the RUL of a turbofan engine. The attention-based DCNN model achieved the best scores on the FD001 independent testing dataset, with an RMSE of 11.81 and a score of 223.

Control-Oriented Modeling for Nonlinear MIMO Turbofan Engine Based on Equilibrium Manifold Expansion Model

This paper investigates the control-oriented modeling for turbofan engines. The nonlinear equilibrium manifold expansion (EME) model of the multiple input multiple output (MIMO) turbofan engine is established, which can simulate the variation of high-pressure rotor speed, low-pressure rotor speed and pressure ratio of compressor with fuel flow and throat area of the nozzle. Firstly, the definitions and properties of the equilibrium manifold method are presented. Secondly, the steady-state and dynamic two-step identification method of the MIMO EME model is given, and the effects of scheduling variables and input noise on model accuracy are discussed. By selecting specific path, a small amount of dynamic data is used to identify a complete EME model. Thirdly, modeling and simulation at dynamic off-design conditions show that the EME model has model accuracy close to the nonlinear component-level (NCL) model, but the model structure is simpler and the calculation is faster than that. Finally, the linearization results are obtained based on the properties of the EME model, and the stability of the model is proved through the analysis of the eigenvalues, which all have negative real parts. The EME model constructed in this paper can meet the requirements of real-time simulation and control system design.

A novel multivariable nonlinear robust control design for turbofan engines

Traditional steady-state control methods are applied to turbofan engines operating in the small region near certain operating conditions, which need to switch controllers for operating in the large region and then may lead to instability and performance degradation of the closed-loop system. In this paper, a novel multivariable nonlinear robust control method for turbofan engines is proposed to improve the control performance within the large region. To enlarge the controllable region, a polynomial state-space model describes the nonlinear characteristics of turbofan engines. Based on the analysis of the closed-loop control system, by using the Lyapunov function theorems, a polynomial robust controller is designed to ensure the stability and desired nonlinear control performance of turbofan engines. Compared with the classical PI, mixed sensitivity, and H∞ control, simulation results show that the proposed method has better transient responses, disturbance rejection, and other control performance for the turbofan engine within the large region.