Integrated Lifing Analysis Tool for Gas Turbine Components

Tinga, Tiedo; Visser, W. P. J.; Wolf, W.B. de; Broomhead, Michael J.

doi:10.1115/2000-gt-0646

Cited by 12 publications

(11 citation statements)

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“…A typical integrated life estimation method, covering the process of transforming on-board, recorded flight path data of military fighter aircraft to life estimates for the HPT blades, has been presented in the past by Tinga et al [3]. The work done by Saurez et al [4] on condition-based maintenance systems for aircraft engines takes a similar approach to that of Tinga et al [3].…”

Section: Severity Estimation Methodsmentioning

confidence: 99%

“…The work done by Saurez et al [4] on condition-based maintenance systems for aircraft engines takes a similar approach to that of Tinga et al [3].…”

Section: Severity Estimation Methodsmentioning

confidence: 99%

“…The main difference between the current method and those presented by Tinga et al [3] and Saurez et al [4] is the development of a generic tool that is capable of generating the necessary data required for lifing and severity calculations by estimating the relevant aircraft flight data, engine operational conditions, and component parameters of nominal engines, whereas minimal information is available in the public domain regarding their design and performance specifications. Because of the generic nature of the tool, it can also be used to compare and assess different engines while they are at their preliminary design phase.…”

Section: Severity Estimation Methodsmentioning

confidence: 99%

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Severity estimation and effect of operational parameters for civil aircraft jet engines

Hanumanthan

Laskaridis

Singh

2011

Proceedings of the Institution of Mechanical Engineers, Part G:

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Engine maintenance costs for civil aircraft largely depend on the life consumption of critical parts like high-pressure turbine blade and disk. Severity (defined as a measure of relative damage) can be used to characterize the degree of life consumption for the parts subjected to low cycle fatigue, creep, and oxidation as predominant life-limiting modes under normal engine operation. In this context, a numerical tool has been developed to study the impact of engineaircraft operational parameters on engine severity. This is accomplished by combining aircraft performance, gas turbine performance, component sizing, heat transfer calculation, thermal analysis, structural analysis, life and severity estimation for the high-pressure turbine, and the disk components of jet engines. Operational parameters such as take-off (TO) derate, climb derate, outside air temperature, and airport altitude have been studied for lower thrust and larger thrust engines. The observations from the study show that TO derate and outside air temperature are dominant parameters in terms of engine severity. The use of climb derate also lowers the severity and is more beneficial for larger thrust engines, while airport altitude effects could be minimized by the use of suitable TO derate. The severity characteristics estimated through the numerical tool can provide an enhanced perspective on aircraft engine maintenance.

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Section: Severity Estimation Methodsmentioning

confidence: 99%

“…The work done by Saurez et al [4] on condition-based maintenance systems for aircraft engines takes a similar approach to that of Tinga et al [3].…”

Section: Severity Estimation Methodsmentioning

confidence: 99%

Section: Severity Estimation Methodsmentioning

confidence: 99%

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Severity estimation and effect of operational parameters for civil aircraft jet engines

Hanumanthan

Laskaridis

Singh

2011

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…Here the gasifier obtains air from the gas turbine, and delivers low calorific value fuel to the gas turbine. Tinga et al (2000) use GSP as an element in a series of models for analysis of thermal load calculation for gas turbine hot section life consumption modelling.…”

Section: Other Applicationsmentioning

confidence: 99%

GSP, a Generic Object-Oriented Gas Turbine Simulation Environment

Visser

Broomhead

2000

Volume 1: Aircraft Engine; Marine; Turbomachinery; Microturbines and Small Turbomachinery

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106

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NLR’s primary tool for gas turbine engine performance analysis is the ‘Gas turbine Simulation Program’ (GSP), a component based modeling environment. GSP’s flexible object-oriented architecture allows steady-state and transient simulation of any gas turbine configuration using a user-friendly drag&drop interface with on-line help running under Windows95/98/NT. GSP has been used for a variety of applications such as various types of off-design performance analysis, emission calculations, control system design and diagnostics of both aircraft and industrial gas turbines. More advanced applications include analysis of recuperated turboshaft engine performance, lift-fan STOVL propulsion systems, control logic validation and analysis of thermal load calculation for hot section life consumption modeling. In this paper the GSP modeling system and object-oriented architecture are described. Examples of applications for both aircraft and industrial gas turbine performance analysis are presented.

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“…However, in pursuit of such improved accuracy and reliability, model-based creep life estimation methods have become more and more interdisciplinary and multitasking [4][5][6][7][8]. However, in pursuit of such improved accuracy and reliability, model-based creep life estimation methods have become more and more interdisciplinary and multitasking [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Creep Life Prediction for Aero Gas Turbine Hot Section Component Using Artificial Neural Networks

Ghafir

Li²,

Wang

2013

Journal of Engineering for Gas Turbines and Power

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Accurate and reliable component life prediction is crucial to ensure safety and economics of gas turbine operations. In pursuit of such improved accuracy and reliability, modelbased creep life prediction methods have become more complicated and demand higher computational time. Therefore, there is a need to find an alternative approach that is able to provide a quick solution to creep life prediction for production engines while at the same time maintain the same accuracy and reliability as that of the model-based methods. In this paper, a novel creep life prediction approach using artificial neural networks is introduced as an alternative to the model-based creep life prediction approach to provide a quick and accurate estimation of gas turbine creep life. Multilayer feed forward baekpropagation neural networks have been utilized to form three neural network-based creep life prediction architectures known as the range-based, functional-based, and sensor-based architectures. The new neural network creep life prediction approach has been tested with a model single-spool turboshaft gas turbine engine. The results show that good generalization can be achieved in all three neural network architectures. It was also found that the sensor-based architecture is better than the other two in terms of accuracy, with 98% of the post-test samples possessing prediction errors within ±0.4%.2 Artificial Neural Networks 2.1 Artificial Neural Networks. ANN is a well-established algorithm that involves a large number of processors operating in parallel, designed to mimic the architecture and function of biological neural systems. The most widely used ANN is the multilayer feed forward baekpropagation (MFBP) network [13], as shown in Fig. 1. The terms "feed forward" and "baekpropagation" indicate that the network has two processing elements, which are Journal of Engineering for Gas Turbines and Power

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Integrated Lifing Analysis Tool for Gas Turbine Components

Cited by 12 publications

References 1 publication

Severity estimation and effect of operational parameters for civil aircraft jet engines

Severity estimation and effect of operational parameters for civil aircraft jet engines

GSP, a Generic Object-Oriented Gas Turbine Simulation Environment

Creep Life Prediction for Aero Gas Turbine Hot Section Component Using Artificial Neural Networks

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