Adaptive Risk Refinement Methodology for Gas Turbine Engine Rotor Disks

Engine rotors are one of the most critical components of a heavy duty industrial gas turbine engine, as it transfers mechanical energy from rotor blades to a generator for the production of electrical energy. In general, these are larger bolted rotors with complex geometries, which make analytical modeling of the rotor to determine its static, transient or dynamic behaviors difficult. For this purpose, powerful numerical analysis approaches, such as, the finite element method, in conjunction with high performance computers are being used to analyze the current rotor systems. The complexity in modeling bolted rotor behavior under various loadings, such as, airfoil, centrifugal and gravity loadings, including engine induced vibration is one of the main challenges of simulating the structural performance of an engine rotor. In addition, the internal structural temperature gradients that can be encountered in the transient state as a result of start-up and shutdown procedures are generally higher than those that occur in the steady-state and hence thermal shock is important factor to be considered relative to ordinary thermal stress. To address these issues, the current paper presents the steady-state & quasi-static analyses (to approximate transient responses) of two full 3-D industrial gas turbine engine rotors, SW501F & GE-7FA rotor, comprising of both compressor & turbine sections together. Full 3-D rotor analysis was carried out, since the 2-D axisymmetric model is inadequate to capture the complex geometries & out of plane behavior of the rotor. Both non-linear steady-state & transient analyses of a full gas turbine engine rotor was performed using the general purpose finite element analysis program ABAQUS. The paper presents in detail the FEA modeling technique, overall behavior of the full rotor under various loadings, as well as, the critical locations in the rotor with respect to its strength and life. The identification of these critical locations is needed to help with the repair of the existing rotors and to improve and extend the operational/service life of these rotors.

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Evaluation of a Gas-Lubricated Foil Bearing for Control of Gas Turbine Engine Rotor Critical Speeds

10.4271/751072 ◽

1975 ◽

Cited By ~ 3

Author(s):

R. H. Badgley ◽

J. Reddecliff

Keyword(s):

Gas Turbine ◽

Gas Turbine Engine ◽

Turbine Engine ◽

Critical Speeds ◽

Foil Bearing ◽

Engine Rotor

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Reduction of the finite element model of a gas turbine engine rotor using CMS technique

Journal of Physics Conference Series ◽

10.1088/1742-6596/1891/1/012039 ◽

2021 ◽

Vol 1891 (1) ◽

pp. 012039

Author(s):

S.V. Semenov ◽

M.Sh. Nikhamkin ◽

N.A. Sazhenkov

Keyword(s):

Finite Element ◽

Finite Element Model ◽

Gas Turbine ◽

Element Model ◽

Gas Turbine Engine ◽

Turbine Engine ◽

The Finite Element Model ◽

Engine Rotor

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Optimization of a Gas Turbine Engine Rotor Disc Using Case-Based Reasoning and the GATE Genetic Algorithm

Volume 7: Turbomachinery, Parts A, B, and C ◽

10.1115/gt2010-23011 ◽

2010 ◽

Author(s):

S. Dominique ◽

J.-Y. Tre´panier

Keyword(s):

Genetic Algorithm ◽

Gas Turbine ◽

Structural Design ◽

Gas Turbine Engine ◽

Pattern Search ◽

Case Based Reasoning ◽

Turbine Engine ◽

Local Searches ◽

Case Based ◽

Engine Rotor

The implementation of an automated decision support system in the field of structural design and optimization can give a significant advantage to any industry working on mechanical design. Such a system can reduce the project cycle time or allow more time to produce a better design by providing solution ideas to a designer or by upgrading existing design solutions while the designer is not at work. This paper presents an approach to automating the process of designing a gas turbine engine rotor disc using case-based reasoning (CBR), combined with a new genetic algorithm, the Genetic Algorithm with Territorial core Evolution (GATE). GATE was specifically created to solve problems in the mechanical structural design field, and is essentially a real number genetic algorithm that prevents new individuals from being born too close to previously evaluated solutions. The restricted area becomes smaller or larger during optimization to allow global or local searches when necessary. The CBR process uses a databank filled with every known solution to similar design problems. The closest solutions to the current problem in terms of specifications are selected, along with an estimated solution from an artificial neural network. Each solution selected by the CBR is then used to initialize the population of a GATE island. Our results show that CBR may significantly upgrade the performance of an optimization algorithm when sufficient preliminary information is known about the design problem. It provides an average solution 5.0% lighter than the average solution found using random initialization. The results are compared to other results obtained for the same problems by four optimization algorithms from the I-SIGHT 3.5 software: the sequential quadratic programming algorithm (SQP), the insular genetic algorithm (GA), the Hookes & Jeeves generalized pattern search (HJ) and POINTER. Results show that GATE can be a very good candidate for automating and accelerating the structural design of a gas turbine engine rotor disc, providing an average disc 18.9% lighter than SQP, 11.2% lighter than HJ, 23.9% lighter than GA and 4.3% lighter than POINTER, even when starting with the same solution set.

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