Evolutionary Algorithm for Enhanced Gas Path Analysis in Turbofan Engines

2021 ◽  
Author(s):  
T. O. Rootliep ◽  
W. P. J. Visser ◽  
M. Nollet
Keyword(s):  
Path Analysis ◽  
Evolutionary Algorithm ◽  
Operating Point ◽  
Turbofan Engine ◽  
Turbofan Engines ◽  
Novel Approach ◽  
Multiple Data Sets ◽  
Engine Maintenance ◽  
Operating Points ◽  
Health Parameters

Abstract Adaptive modelling (AM) based Gas Path Analysis (GPA) is a powerful diagnostic and prognostic technique for turbofan engine maintenance. This involves the assessment of turbofan component condition using thermodynamic models that can iteratively adapt to measurements values in the gas path by changing component condition parameters. The problem with this approach is that newer turbofan engines such as the General Electric GEnx-1B have fewer gas path sensors installed causing the AM equation systems to become underdetermined. To overcome this problem, a novel approach has been developed that combines the AM model with an Evolutionary Algorithm (EA) optimization scheme and applies it to multiple operating points. Additionally, these newer turbofan engines provide performance data continuously during flight. Information on variable geometry and bleed valve position, active clearance control state and power off-take is included and can be accounted for to further enhance AM model accuracy. A procedure is proposed where the selection of operating points is based on steady-state stability requirements, cycle model operating point uncertainty and parameter outlier filtering. The Gas turbine Simulation Program (GSP) is used as the non-linear GPA modelling environment. A Multiple Operating Point Analysis (MOPA) is chosen to overcome the problem of underdetermination by utilizing multiple data sets at different operating points. The EA finds the best fit of health parameter deviations by minimizing the multi-point objective function using the GSP AM model. A sub-form of the EA class named Differential Evolution (DE) has been chosen as the optimizer. Like all EAs, DE is a parallel direct search method in which a population of parameter vectors evolves following genetic operations towards an optimum output candidate. The resulting hybrid GPA tool has been verified by solving for different simulated deterioration cases of a GSP model. The tool can identify the direction and magnitude of condition deviation of 10 health parameters using 6 gas path sensors. It has subsequently been validated using historical in-flight data of the GEnx-1B engine. It has demonstrated successful tracking of engine component condition for all 10 health parameters and identification of events such as turbine blade failure and water washes. The authors conclude that the tool has proven significant potential to enhance turbofan engine condition monitoring accuracy for minimizing maintenance costs and increasing safety and reliability.

scholarly journals F-16 turbofan engine monitoring system

2019 ◽  
Vol 177 (2) ◽  
pp. 23-35
Author(s):  
Sławomir SZRAMA
Keyword(s):  
Monitoring System ◽  
Health Management ◽  
Engine Performance ◽  
Turbofan Engine ◽  
Turbofan Engines ◽  
Maintenance System ◽  
Management Concept ◽  
And Performance ◽  
Engine Maintenance ◽  
Engine Reliability

The multirole F-16 is the most advanced aircraft in the Polish Air Forces. It has been equipped with the very modern, sophisticated and advanced turbofan engine F100-PW-229. Due to the fact, that there is only one engine, its reliability, durability, efficiency and performance are the crucial factors for the safety reasons. In the article author researched maintenance system of the F100 turbofan engines, to describe Engine Monitoring System features. Engine Monitoring System (EMS) is the key element in the engine prognostic and health monitoring. The EMS provides engine fault indicators to the pilots and technicians and with the engine performance trending affects the F-16 flight safety risk and enhanced engine maintenance management concept. The main goal of this article was to provide information on the F-16 Engine Monitoring System and its impact on the aircraft airworthiness and F-16 fleet readiness resulting from the engine reliability. It is also an introduction to the F-16 Engine Health Management concept.

Active fault tolerant control of turbofan engines with actuator faults under disturbances

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Yan-Hua Ma ◽  
Xian Du ◽  
Lin-Feng Gou ◽  
Si-Xin Wen
Keyword(s):  
Active Fault ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Feedback Controller ◽  
Actuator Faults ◽  
Error Feedback ◽  
Turbofan Engine ◽  
Turbofan Engines ◽  
Interval Observer ◽  
State Error

AbstractIn this paper, an active fault-tolerant control (FTC) scheme for turbofan engines subject to simultaneous multiplicative and additive actuator faults under disturbances is proposed. First, a state error feedback controller is designed based on interval observer as the nominal controller in order to achieve the model reference rotary speed tracking control for the fault-free turbofan engine under disturbances. Subsequently, a virtual actuator based reconfiguration block is developed aiming at preserving the consistent performance in spite of the occurrence of the simultaneous multiplicative and additive actuator faults. Moreover, to improve the performance of the FTC system, the interval observer is slightly modified without reconstruction of the state error feedback controller. And a theoretical sufficiency criterion is provided to ensure the stability of the proposed active FTC system. Simulation results on a turbofan engine indicate that the proposed active FCT scheme is effective despite of the existence of actuator faults and disturbances.

Application of Multiple Handle Gas Path Analysis on a Singlespool Turbofan Engine

2001 ◽  
Vol 18 (4) ◽  
Author(s):  
Eftychios Kleinakis ◽  
Pericles Pilidis ◽  
Petros Kotsiopoulos
Keyword(s):  
Path Analysis ◽  
Turbofan Engine

First and Second Law Analysis of Intercooled Turbofan Engine

2015 ◽  
Vol 138 (2) ◽  
Author(s):  
Xin Zhao ◽  
Oskar Thulin ◽  
Tomas Grönstedt
Keyword(s):  
High Pressure ◽  
Conceptual Design ◽  
Exergy Analysis ◽  
Second Law ◽  
Turbofan Engine ◽  
Blade Height ◽  
Turbofan Engines ◽  
Fuel Burn ◽  
Aero Engine ◽  
Last Stage

Although the benefits of intercooling for aero-engine applications have been realized and discussed in many publications, quantitative details are still relatively limited. In order to strengthen the understanding of aero-engine intercooling, detailed performance data on optimized intercooled (IC) turbofan engines are provided. Analysis is conducted using an exergy breakdown, i.e., quantifying the losses into a common currency by applying a combined use of the first and second law of thermodynamics. Optimal IC geared turbofan engines for a long range mission are established with computational fluid dynamics (CFD) based two-pass cross flow tubular intercooler correlations. By means of a separate variable nozzle, the amount of intercooler coolant air can be optimized to different flight conditions. Exergy analysis is used to assess how irreversibility is varying over the flight mission, allowing for a more clear explanation and interpretation of the benefits. The optimal IC geared turbofan engine provides a 4.5% fuel burn benefit over a non-IC geared reference engine. The optimum is constrained by the last stage compressor blade height. To further explore the potential of intercooling the constraint limiting the axial compressor last stage blade height is relaxed by introducing an axial radial high pressure compressor (HPC). The axial–radial high pressure ratio (PR) configuration allows for an ultrahigh overall PR (OPR). With an optimal top-of-climb (TOC) OPR of 140, the configuration provides a 5.3% fuel burn benefit over the geared reference engine. The irreversibilities of the intercooler are broken down into its components to analyze the difference between the ultrahigh OPR axial–radial configuration and the purely axial configuration. An intercooler conceptual design method is used to predict pressure loss heat transfer and weight for the different OPRs. Exergy analysis combined with results from the intercooler and engine conceptual design are used to support the conclusion that the optimal PR split exponent stays relatively independent of the overall engine PR.

Advanced Turbofan Engines for Low Fuel Consumption

1978 ◽  
Author(s):  
William Sens
Keyword(s):  
Fuel Consumption ◽  
Advanced Technology ◽  
Commercial Aircraft ◽  
New Production ◽  
Turbofan Engine ◽  
Fuel Savings ◽  
Turbofan Engines ◽  
Aircraft Fuel ◽  
Design Characteristics ◽  
Year 2000

The anticipated commercial aircraft fuel usage through the year 2000 is divided into three categories: that which will be consumed by existing engines, new production of current type engines, and new turbofan engines with advanced technology. Means of improving fuel consumption of each of these engine categories will be reviewed and the potential fuel savings identified. The cycle selection and design characteristics of an advanced turbofan engine configuration will be discussed and the potential improvements in fuel consumption and economics identified.

Experience With Gas Path Analysis for On-Wing Turbofan Condition Monitoring

2013 ◽  
Vol 136 (1) ◽  
Author(s):  
Michel L. Verbist ◽  
Wilfried P. J. Visser ◽  
Jos P. van Buijtenen
Keyword(s):  
Path Analysis ◽  
Condition Monitoring ◽  
Operating Conditions ◽  
Performance Data ◽  
Test Rig ◽  
Substantial Cost ◽  
Substantial Cost Saving ◽  
Turbofan Engine ◽  
Monitoring Information

Gas path analysis (GPA) is an effective method for determination of turbofan component condition from measured performance parameters. GPA is widely applied on engine test rig data to isolate components responsible for performance problems, thereby offering substantial cost saving potential. Additional benefits can be obtained from the application of GPA to on-wing engine data. This paper describes the experience with model-based GPA on large volumes of on-wing measured performance data. Critical is the minimization of the GPA results uncertainty in order to maintain reliable diagnostics and condition monitoring information. This is especially challenging given the variable in-flight operating conditions and limited on-wing sensor accuracy. The uncertainty effects can be mitigated by statistical analysis and filtering and postprocessing of the large datasets. By analyzing correlations between measured performance data trends and estimated component condition trends errors can be isolated from the GPA results. The various methods assessed are described and results are demonstrated in a number of case studies on a large turbofan engine fleet.

scholarly journals WH-EA: An Evolutionary Algorithm for Wiener-Hammerstein System Identification

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
J. Zambrano ◽  
J. Sanchis ◽  
J. M. Herrero ◽  
M. Martínez
Keyword(s):  
System Identification ◽  
Evolutionary Algorithm ◽  
Nonlinear System Identification ◽  
Search Space ◽  
Single Step ◽  
Fine Tuning ◽  
Hammerstein Model ◽  
Hammerstein System ◽  
Hammerstein Systems ◽  
Novel Approach

Current methods to identify Wiener-Hammerstein systems using Best Linear Approximation (BLA) involve at least two steps. First, BLA is divided into obtaining front and back linear dynamics of the Wiener-Hammerstein model. Second, a refitting procedure of all parameters is carried out to reduce modelling errors. In this paper, a novel approach to identify Wiener-Hammerstein systems in a single step is proposed. This approach is based on a customized evolutionary algorithm (WH-EA) able to look for the best BLA split, capturing at the same time the process static nonlinearity with high precision. Furthermore, to correct possible errors in BLA estimation, the locations of poles and zeros are subtly modified within an adequate search space to allow a fine-tuning of the model. The performance of the proposed approach is analysed by using a demonstration example and a nonlinear system identification benchmark.

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