Performance Of Turbofan Engine Research Articles

A simple model for the estimation of turbofan engine performance in all airborne phases of flight

Abstract The overall efficiency of a turbofan engine may be expressed as a function of the Mach number, flight level and one other parameter. This may be either the net thrust, the turbine entry temperature or the fuel flow rate. Using basic aero-thermodynamic principles, dimensional analysis, normalisation and curve fitting, five approximate and “near universal” relations have been identified for engines having bypass ratios between 1 and 13. These relations contain five independent characteristic engine parameters. When these parameters are known, the relations form the basis of an estimation method for engine overall efficiency that is simple, fast, open source, completely transparent and, as new information appears, capable of further refinement. Since the empirical relations presented in this analysis are valid for Mach numbers greater than 0.2, the method is applicable to all airborne phases of flight. For a given aircraft, if the flight trajectory is specified in sufficient detail for the variation of net thrust with Mach number and flight level to be determined, only three of the five relations, together with the value of engine overall efficiency at a single reference condition, are needed to estimate the overall efficiency at every point on the trajectory. Comparisons with the data used in this analysis suggest that the accuracy is better than ±5% in most cases. In the completely general case, two additional engine characteristic parameters, one a total temperature ratio and the other a Mach number, are introduced. If these are known, both engine overall efficiency and net thrust can be expressed as functions of Mach number, flight level and turbine entry temperature. This allows the method to be used for the estimation of operating limits in the various phases of flight and in simplified optimisation studies, e.g. finding the environmentally optimum flight trajectory. In previous work, estimates of engine overall efficiency at the “design optimum” condition have been estimated for 53 aircraft and engine combinations. It is shown that the ‘design optimum’ condition is an appropriate choice for the engine reference condition. Updated and revised values for the relevant parameters for these 53 examples, together with estimates for the two additional engine characteristic parameters, are given in tabular form.

A Comparative Study of Data-Driven Prognostic Approaches under Training Data Deficiency

In industrial system health management, prognostics play a crucial role in ensuring safety and enhancing system availability. While the data-driven approach is the most common for this purpose, they often face challenges due to insufficient training data. This study delves into the prognostic capabilities of four methods under the conditions of limited training datasets. The methods evaluated include two neural network-based approaches, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks, and two similarity-based methods, Trajectory Similarity-Based Prediction (TSBP) and Data Augmentation Prognostics (DAPROG), with the last being a novel contribution from the authors. The performance of these algorithms is compared using the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) datasets, which are made by simulation of turbofan engine performance degradation. To simulate real-world scenarios of data deficiency, a small fraction of the training datasets from the original dataset is chosen at random for the training, and a comprehensive assessment is conducted for each method in terms of remaining useful life prediction. The results of our study indicate that, while the Convolutional Neural Network (CNN) model generally outperforms others in terms of overall accuracy, Data Augmentation Prognostics (DAPROG) shows comparable performance in the small training dataset, being particularly effective within the range of 10% to 30%. Data Augmentation Prognostics (DAPROG) also exhibits lower variance in its predictions, suggesting a more consistent performance. This is worth highlighting, given the typical challenges associated with artificial neural network methods, such as inherent randomness, non-intuitive decision-making processes, and the complexities involved in developing optimal models.

Study of the Effects of Heat Exchanger Location on Turbofan Engine Performance

Study of the Effects of Heat Exchanger Location on Turbofan Engine Performance

Interpretable and efficient RUL prediction of turbofan engines using EM-enhanced Bi-LSTM with TCN and attention mechanism

Remaining useful life (RUL) prediction for turbofan engines is important in prognostics and health management (PHM) for the maintenance and operation of critical equipment. With continuous innovations in deep learning techniques, the complexity of models continues to increase, but the interpretability and comprehensibility of the prediction results become particularly important in industrial applications. Therefore, in this study, an improved bidirectional long and short-term memory network (Bi-LSTM) based interpretable hybrid deep learning model for RUL prediction of turbofan engines is proposed, which ingeniously integrates time series convolutional networks (TCNs), expectation maximization (EM), Bi-LSTMs, and attention mechanisms. By capturing time-series features at different levels, the model adapts to the complex dynamics of turbofan engine performance evolution in an efficient and cost-effective manner. Experimental validation on the C-MAPSS dataset demonstrated that the model significantly outperforms other methods in terms of RUL prediction performance, especially in improving prediction accuracy and coping with the degradation of complex system dynamics. The largest contribution of key metrics to the model is validated through consistent results from multiple interpretable tools, providing comprehensive and consistent support for understanding and trusting prediction results in industrial applications. This study further enhances the robustness of the model and the reliability of the interpretable results by delving into the dynamic relationships between the properties of the different life stages, which not only reveal the importance of these characteristics in engine life prediction but also provide more comprehensive information about the engine performance variations by observing the dynamic relationships.

Test and Analysis on High-pressure Turbine Tip Clearance Measurement of Turbofan Engine

The tip clearance of high-pressure turbine rotor is a key parameter for the performance and control of turbofan engine. It is of great significance to master the variation law of tip clearance for evaluating the working efficiency and performance attenuation of turbofan engine. The high-frequency dynamic analysis method based on time-frequency analysis is established. The variation law of high-pressure turbine tip clearance with engine state is obtained, and the influencing factors are analysed by simulation. The test results show that the variation law of the tip clearance of the high-pressure is consistent with the design, that is about 0∼3mm. The tip clearance of the high-pressure turbine is greatly affected by the rotor vibration. It means that we can monitor vibration of the rotor by monitoring changes in tip clearance. Real-time measurement and control adjustment should be carried out in the flight test to improve the working efficiency of the engine.

A fault-tolerant acceleration control strategy for turbofan engine based on multi-layer perceptron with exponential Gumbel loss

The acceleration performance of turbofan engine is mainly limited by the surge boundary of high-pressure compressor (HPC). A certain amount of surge margin (SM) is reserved between the boundary and the acceleration schedule (AS) of acceleration control to avoid the risk caused by intake distortion, etc. However, the performance improvement by low SM design is the requirement for advanced engines. Hence it must execute a fault-tolerant strategy for the impact of sensor error, fuel metering error, etc. This paper proposes a method by adopting four ASs for weighted average to replace the single AS. The weights are altered by the multi-layer perceptron with reference to the real-time fuel results from four ASs, intake condition and rotor speed. For safety and accuracy, an exponential Gumbel loss function is introduced into the model training. The simulation results demonstrate that the method can significantly tolerate the impact of a single fault and the normal deviations of other factors without causing SM of HPC to be less than 3.6%. These also indicate that the acceleration time on the ground does not exceed acceptable 5 s, with a median that is 0.34 s less than the minimum method of two ASs within the flight envelope.

Investigation of overall performance of turbofan engine with pulse detonation combustor in bypass duct

The performance model was established to study the performance of mixed exhaust turbofan engine with pulse detonation combustor (PDC) in the bypass duct. The effects of the total pressure loss of isolator and bypass circulation parameters on the characteristic of PDC and performance of engine were analyzed. Then, the sensitivity analysis of performance parameters was carried out on the influence of the components parameters. The performance of mixed exhaust turbofan engine with PDC in the bypass duct was compared with that of the conventional afterburner turbofan engine at the same design circulation parameters. The results show that to increase the total pressure loss of isolator is beneficial to the improvement of performance because of the improvement of pressure ratio; when fan pressure ratio is constant, with the increasing of bypass ratio, the specific fuel consumption and specific thrust increase; with the increasing of fan pressure ratio, when bypass ratio is 0.2 to 0.4, specific thrust increases first and then decreases. When bypass ratio is 0.4 to 0.5, specific thrust increases first and then remains constant. When bypass ratio is 0.5 to 0.9, specific thrust increases, but the rate of increase reduces. At different bypass ratio, specific fuel consumption decreases with increasing of fan pressure ratio; the performance of engine is more sensitive to the component parameters that directly affect the flow state parameters of the bypass duct; because of supercharging performance of PDC, PDC in the bypass duct only uses part of air flow to organize combustion that can make specific thrust of turbofan engine with PDC in the bypass duct and conventional afterburner turbofan engine are equivalent. Meanwhile, specific fuel consumption can be reduced by 27.7% at design point and 12.8%-26.8% at off-design points.

Development of a Predictive Tool for the Parametric Analysis of a Turbofan Engine

Parametric cycle analysis, an on-design engine study, specifies the required design characteristics that optimize engine performance. This study aimed to conduct a parametric analysis of a low-bypass turbofan engine with an afterburner, F100-PW229, and develop a technique for estimating its performance based on data using machine learning and deep learning. Commercially available gas turbine simulation software, GasTurb 14, was used to create a dataset of engine performance response variables and input design parameters. The effects of the Mach number, fan pressure ratio, altitude, turbine entry temperature, and bypass ratio on the specific thrust, propulsive efficiency, specific fuel consumption, and total fuel flow were investigated. Regression learning models and deep neural networks were then programmed on this dataset to predict responses for new input data. In MATLAB, a total of 24 regression models were trained with cross-validation, and the model with the least root mean square error was selected as the final model. The machine learning regression models produced reliable output parameter predictions, with the least root mean square error of 9.076 × 10−5. Among the numerous regression models tested, Gaussian process regression, the quadratic support vector machine, and the wide neural network emerged to be the most successful in predicting turbofan engine performance metrics. A multilayer perceptron model was coded in Python with two hidden layers that accurately predicted the performance parameters. The mean square error value on test data was found to be as low as 0.0046. In comparison to intensive computational simulations, machine learning and deep learning models offer an efficient method for conducting parametric analysis of turbofan engines.

Assessment of performance degradation of a mixed flow low bypass turbofan engine through GasTurb simulation

Abstract Aero engine performance analysis is very important for engines under development as well as for engines in service for condition monitoring. Predictions of aero engine performance and ability of building the simulation model is an invaluable asset for designers, manufacturer and end-use operator. This paper presents the methodology in establishing the baseline performance of a twin spool mixed flow low bypass turbofan engine through extensive testing at engine test bench. The baseline data is used to validate a GasTurb model which is subsequently used for assessment of off-design performance and component degradation responsible for performance deterioration at various service hours. The estimated exhaust gas temperatures by the model for degraded engines are in good agreement with the measured data. The model further assesses the drop in HP compressor efficiency and shift in operating line which will be very useful for taking judicious decision for withdrawal of engines and is expected to reduce or delay withdrawals and increase the availability of engines at operating base.

마이크로 터보제트 엔진의 터보팬 파생개발 연구

A study to modify and develop a turbofan engine using a micro turboprop engine has been conducted. The turbofan engine has been designed by adding a fan blade module as well as other required accessories to the existing turboprop engine. CFD and FEM methods were used for numerical analyses of the additional fan module to assess its aerodynamic performance and structural stability. Components of the fan module were fabricated with consideration for manufacture and assembly, and a test rig was also designed and built to measure the turbofan engine performance. The performances of the turbofan engine were measured and compared to an existing micro-turbojet engine. The performance tests were conducted in two phases: In the first phase, the robustness of the designed turbofan and test rig were investigated and in the second phase, the maximum power test has been conducted to measure performance variables such as engine thrust, engine exhaust temperature(EGT) and fuel flow rate according to the engine RPM. The test results indicated that the turbofan engine SFC was reduced by 40∼50% of the existing turbojet engine, demonstrating the successful implementation of turbofan characteristics.

High bypass turbofan engine and anti-icing system performance: Mass flow rate of anti-icing bleed air system effect

Most aircraft are flying at high altitude which can cause icing accumulation, changes the wing profile and the aerodynamic characteristics of wing. The anti-icing bleed air system (AI-BAS) is considered an important system used to prevent the icing accumulation using hot air from engine (bleed); but it may lead to reduced engine performance. This work aims to study the effect of bleed variation on engine performance and (AI-BAS). The bleed air was extracted from the intermediate stage of engine compressor. MATLAB software was used to simulate the engine cycle and estimate performance. The performance of (AI-BAS) was investigated computationally for different bleed air ratio. The computational domain was constructed and solved by ANSYS software for partial span model (PSM) with suitable periodic conditions. The obtained results show that the engine performance characteristics are decreased with increasing of anti-icing bleed air and the overall efficiency of engine is decreased by about 1.29%, 2.63% and 4.01%, for bleed air ratio of 0.02, 0.04 and 0.06 respectively. On the other hand, the average temperature of wing leading edge was increased by about 2.68% and 4.86%, for bleed air ratio of 0.04 and 0.06 respectively compared to the bleed air ratio of 0.02.

Effect of air properties on a twin-shaft turbofan engine performance during start-up

When engines start in coastal areas in summer, the performance of turbofan engine and starter will be inevitably affected by ambient humidity and temperature. It is important to predict the change of start-up performance by capturing the effect of ambient humidity and temperature on the matching process between turbofan engine and starter. However, in the start-up model, starter model is usually simplified by look-up table of starter power and semi-empirical formulas. It is difficult to simulate the variation of transient performance of turbofan engine caused by the power change of the starter when the ambient humidity and temperature increase. In this paper, a new method for simulating the transient performance of start-up process is presented. The nonlinear transient model including turbofan engine and starter is developed based on real engine configurations. The power compatibility equation is established to correlate the turbofan engine and starter to predict the matching performance in the start-up stage. The model is validated using mature commercial software. The steady-state errors of the model are within 2 % and the dynamic errors are within 2.5 %. In this paper, the start-up process is divided into four stages, and the transient performance of turbofan engine and starter in each stage is analyzed. This paper simulates the transient performance of start-up process under different ambient temperature and humidity conditions. The results show that when the ambient temperature is 308.5 K and the relative humidity is 0.8, the starter power is reduced by 76.86 % and the start-up time is increased by 10.28 s.

Turbofan engine performance prediction methodology integrated high-fidelity secondary air system models

This research studies the performance of an ultra-high bypass ratio turbofan engine, and specifically its secondary air system (SAS). A co-simulation methodology is explored whereby a high-fidelity SAS model and an engine performance code featuring flexible modules can be coupled and interactively executed. The percentage of bleed flows and boundary conditions for the SAS are updated at each iteration step. For this purpose, a SAS model including different elements is developed. Furthermore, a commercial computational fluid dynamics (CFD) solver is adopted to capture the complex flow field in the pre-swirl system. The credibility of cycle calculation and SAS elements is validated by comparing with publicly available data. Subsequently, an elaborately designed SAS is modeled and co-simulated with the AGTF30 engine using a flow network simulation method. The coupling effect between the engine performance and the SAS is studied for eight different flight conditions. The correlation and prediction of engine performance due to seal clearance change is presented. The co-simulation approach clarifies the mutual interactions between the engine overall parameters and the SAS. The results reveal that the enhanced flow network model can improve the simulation accuracy of engine performance over a wide range of operating conditions.

Energy and performance analysis of a turbofan engine with the aid of dynamic component efficiencies

Assessment of performance of turbofan engine with different design parameters is crucial for meeting the performance requirements by considering each key components of the engine. To comprehend influences of component efficiencies to mitigate environmental effect from turbofans has been hot topics in aviation field recently. In this study, firstly, impacts of polytropic efficiencies of fan, compressor and turbine as well as pressure ratio of combustor (CPR) on several turbofan performance are dealt with at several flight conditions. Secondly, it is tried to show difference of performance metrics under ideal and real conditions. The discrepancy between performance parameters computed at ideal and real cases gets relatively high. Namely, at take-off condition, the difference between ideal and real specific fuel consumption is computed as 29.12% whereas it is found as 28.37% at cruise condition, which shows that considering the system as ideal makes the computations inappropriate for performance analysis. Moreover, performance parameters of turbofan is more sensitive to compressor efficiency compared with turbine. As the polytropic efficiencies of fan and compressor are close to highest, net thrust of the engine develops from 109.05 kN (baseline) to 124.71 kN at take-off while it increases from 26.36 kN (baseline) to 29.27 kN at cruise condition. With effect of the elevated pressure ratio of combustor and efficiency of turbine, thrust of the engine increases to 118.23 kN at take off and to 27.84 kN at cruise condition. Finally, as Mach number increases, the difference between ideal and real performance values sharply increases. Therefore, when analyzing on turbofan engines, the assumptions should be minimum as possible as, otherwise the findings make the engineers to misguide for system optimization. Besides, these outcomes show that if the components with higher polytropic efficiency can be obtained, overall efficiency of turbofan, thereby environmental sustainability could be elevated to upper level compared with baseline.

Full-envelope acceleration control method of turbofan engine based on variable geometry compound adjustment

In order to reduce the influence of compressor stability margin constraint and enhance the full envelope acceleration performance of turbofan engine, a full envelope acceleration control method of turbofan engine based on variable compressor intermediate stage bleed and adjustable low-pressure (LP) turbine guide vane is proposed. Firstly, a high confidence turbofan engine component level model is established, which is capable of modifying the compressor adiabatic efficiency caused by variable intermediate stage bleed and adjusting the LP turbine guide vane adaptively. Then, the main combustion chamber fuel flow, compressor intermediate stage bleed flow and LP turbine guide vane angle are selected as the optimization variables. Meanwhile, the PSO-SQP (Particle Swarm Optimization-Sequential Quadratic Programming) method is adopted to obtain the acceleration compound control schedule. Finally, the full envelope compound adjustment acceleration control plan is available based on the similarity conversion theory of consistent engine inlet total temperature. The simulation results demonstrate that the acceleration control method based on variable geometry compound adjustment can significantly promote engine acceleration performance in the left flight envelope, shorten the maximum acceleration time by more than 50%, and dramatically improve the stability margin of the compressor during acceleration phase. In the right flight envelope, the variation law of the minimum surge margin during acceleration procedure almost coincides with the engine inlet isotherm. It proves that the method proposed possesses good applicability and feasibility in the full envelope.

Study on the Effect of Swallowing Birds on the Performance of Turbofan Engine

Study on the Effect of Swallowing Birds on the Performance of Turbofan Engine

Nonlinear Model Predictive Control-Based Optimal Energy Management for Hybrid Electric Aircraft Considering Aerodynamics-Propulsion Coupling Effects

Hybrid electric propulsion systems have been identified as feasible solutions for regional jets and narrow-body aircraft to reduce block fuel burn, emissions, and operating costs. In this article, a nonlinear model predictive control-based optimal energy management scheme (MPC-EMS) has been proposed to minimize the block fuel burn during flight. First, the artificial neural network (ANN) model is adopted to predict turbofan engine performance; meanwhile, gas turbine–electrical powertrain integration is investigated and analyzed for typical operating conditions. Then, by combining a point-mass aircraft dynamic model, nonlinear MPC with the cross-entropy method (CEM) is proposed to obtain optimal energy management based on a fully coupled aerodynamics-propulsion hybrid electric aircraft model. Besides, this state-constrained optimal control problem is reformulated as a state-unconstrained problem with a penalty function to reduce the computational load. Finally, the proposed MPC-EMS algorithm is applied to Boeing 737-800 aircraft with mechanically parallel hybrid electric propulsion configuration to minimize the block fuel burn and compared with the optimization results using global genetic algorithm (GA)-based EMS and equivalent consumption minimization strategy (ECMS). The simulation results indicate that the proposed MPC-EMS can effectively reduce the computational time compared with global GA-based EMS while achieving global optimization performance with only a minor difference of 1.71% of block fuel burn and emissions reductions.

Predicting the energy and exergy performance of F135 PW100 turbofan engine via deep learning approach

In the present study, the effects of flight-Mach number, flight altitude, fuel types, and intake air temperature on thrust specific fuel consumption, thrust, intake air mass flow rate, thermal and propulsive efficiecies, as well as the exergetic efficiency and the exergy destruction rate in F135 PW100 engine are investigated. Based on the results obtained in the first phase, to model the thermodynamic performance of the aforementioned engine cycle, Flight-Mach number and flight altitude are considered to be 2.5 and 30,000 m, respectively; due to the operational advantage of supersonic flying at high altitude flight conditions, and the higher thrust of hydrogen fuel. Accordingly, in the second phase, taking into account the mentioned flight conditions, an intelligent model has been obtained to predict output parameters (i.e., thrust, thrust specific fuel consumption, and overall exergetic efficiency) using the deep learning method. In the attained deep neural model, the pressure ratio of the high-pressure turbine, fan pressure ratio, turbine inlet temperature, intake air temperature, and bypass ratio are considered input parameters. The provided datasets are randomly divided into two sets: the first one contains 6079 samples for model training and the second set contains 1520 samples for testing. In particular, the Adam optimization algorithm, the cost function of the mean square error, and the active function of rectified linear unit are used to train the network. The results show that the error percentage of the deep neural model is equal to 5.02%, 1.43%, and 2.92% to predict thrust, thrust specific fuel consumption, and overall exergetic efficiency, respectively, which indicates the success of the attained model in estimating the output parameters of the present problem.

Effect of Air Properties on a Twin-Shaft Turbofan Engine Performance During Start-Up

Effect of Air Properties on a Twin-Shaft Turbofan Engine Performance During Start-Up

Effect of Air Properties on a Twin-Shaft Turbofan Engine Performance During Start-Up

Effect of Air Properties on a Twin-Shaft Turbofan Engine Performance During Start-Up