Heuristics for Balancing Turbine Fans

Amiouny, Samir V.; Bartholdi, John J.; Vate, John H. Vande

doi:10.1287/opre.48.4.591.12413

Cited by 14 publications

(7 citation statements)

References 6 publications

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“…Additionally, the results obtained by five techniques reported in Choi and Storer 10 including ordinal pairing, interchange, storer, iterative NP, and GRASP algorithms are depicted in Figure 8. It should be noted that, following Amiouny et al 8 and Choi and Storer, 10 the average weight of blades from a normal distribution is 100 with a standard deviation of 5/3 and the radius of the disc has been set to 100.…”

Section: Resultsmentioning

confidence: 99%

“…Additionally, they used a Lagrangean dual scheme for a random arrangement of blades as the initial set for the interchanges. Amiouny et al 8 mostly focused on reducing computational time in their developed heuristics. According to their study, after sorting blades in descending sequence based on their weights, a greedy grouping approach is employed.…”

Section: Introductionmentioning

confidence: 99%

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Genetic algorithm-based search heuristic for turbine balancing problem

Osguei

Khamoushi

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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Turbine balancing problem (TBP) can be defined as arranging blades of each stage of turbine engines in such a way that provides a minimum residual unbalance to eliminate the undesired vibration. This problem is regarded as an NP-hard problem and no exact solution has been found for such problems up to now. In each stage of the turbine, an optimal arrangement of blades around a disk by considering the difference in weight of blades to achieve a minimum residual unbalance is essential for preventing the early failure of turbine engines. In this article, a genetic algorithm (GA) has been proposed and applied to TBP for defining an optimal blade arrangement of a turbine stage. This algorithm proposes a suitable choice for operators of GA, based on permutation representation. By eliminating the shortcomings of previous researchers’ attempts with a simple form of GA, our proposed GA is capable of attaining an optimal arrangement of blades with reasonable computational efforts. Moreover, suitable values for parameters of GA including population size, crossover, and mutation rates of the proposed GA have been determined using practical sets of blades belonging to a Siemens gas turbine engine. The proposed GA with tuned parameters has been successfully employed for a variety of TBPs, and obtained results show a good convergence to an optimal value of residual unbalance with reasonable computational efforts.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genetic algorithm-based search heuristic for turbine balancing problem

Osguei

Khamoushi

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…There are one, three, and seven blades connected to eight, and one has been selected before, so we chose between three and seven; because there are more adjacent blades to seven, we chose three. Repeatedly, we can get: C1 = (1,8,3,2,7,6,5,4) The crossover probability(p c ) is given by the cloud adaptive genetic algorithm:…”

Section: Crossovermentioning

confidence: 99%

“…Lavagnoli developed a numerical strategy to select the best blade arrangement around the rotor, taking into account the measured blade weight distribution and any residual disk unbalance, which makes the algorithm calculate the best blade sort in the fan-constrained rotor row in a few seconds [7]. Amiouny developed a heuristic of a problem that models the static balance of a turbofan: load point mass at regularly spaced locations on the circumference so that the residual imbalance around the center corresponding to the fan's axis of rotation is as small as possible [8]. Pitsoulis proposed a heuristic algorithm to solve an NP-Hard combinatorial optimization problem in turbine engine manufacturing and maintenance [9].…”

Section: Introductionmentioning

confidence: 99%

Blade Sorting Method for Unbalance Minimization of an Aeroengine Concentric Rotor

et al. 2021

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This paper proposes a blade sorting method based on the cloud adaptive genetic algorithm (CAGA), which is used to optimize the unbalanced of asymmetric rotor of aero-engine. Firstly, by analyzing the unbalance of the arrangement caused by the deviation of the mass moment of the blade, and considering the concentricity of the disk, an optimization model of the unbalanced amount of the blade assembly was established. Secondly, the selection operator, crossover operator, and mutation operator of the algorithm were designed, and the cloud adaptive genetic algorithm was used to optimize the assembly unbalance. Thirdly, the mass moments of a group of aero-engine blades were weighed using a moment scale (MW0), and the blade mass moment distribution and assembly unbalance under the six blade arrangements were analyzed. Finally, by setting different disk concentricity, the corresponding blade arrangement and the final rotor unbalance were obtained. Through analysis, it was found that the unbalance of GA is at least 57.5% optimized relative to the weight sorted, sorting type 2, sorting type 4, and sorting-1/4 skip method, and the unbalance optimized by the CAGA is 95.7% optimized relative to GA. In the case of different initial concentricity of the disk, the effective algorithm accuracy is still maintained, which proves the effectiveness of the method for the arrangement of asymmetric rotor blades. This method establishes an effective asymmetric rotor blade arrangement model, uses the cloud adaptive genetic algorithm to sort the blade assembly, and effectively reduces the unbalanced amount of the asymmetric rotor.

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“…It was originally proposed by Mosevich (1986) as a combinatorial optimisation problem, but it was also formulated as a quadratic assignment problem (Laporte and Mercure, 1988). Since then, it has been attacked by other researchers, using both formulations and different kinds of turbines (Sinclair, 1993;Amiouny et al, 2000;Pitsoulis et al, 2001;Choi and Storer, 2004).…”

Section: The Turbine Balancing Problemmentioning

confidence: 99%

A new Metropolis optimisation method, the cross-section particle collision algorithm: some preliminary results

Sacco

Rios-Coelho

2016

IJNEST

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Abstract:In this work, we introduce a new Metropolis algorithm, which is an enhancement of the recent Particle Collision Algorithm (PCA), loosely inspired by the neutron interactions in a reactor. This novel method is called the CrossSection Particle Collision Algorithm (CSPCA), as it incorporates the concept of cross-section from Reactor Physics, in the sense that points in the search space and their respective fitness-function values are analogous to the neutron cross-sections which are used to express the likelihood of interaction between an incident neutron and a target nucleus. CSPCA is compared against the original PCA and two state-of-the-art metaheuristics, differential evolution and big bang-big crunch. These methods are applied to the turbine balancing problem, which is an NP-hard (i.e. non-deterministic polynomial-time hard) combinatorial optimisation problem that can be used to assess the potential of an algorithm to be applied to Fuel Management Optimisation (FMO). CSPCA performs better than its opponents, showing potential to be used not only in FMO, but also in other nuclear science and engineering optimisation problems.

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Heuristics for Balancing Turbine Fans

Cited by 14 publications

References 6 publications

Genetic algorithm-based search heuristic for turbine balancing problem

Genetic algorithm-based search heuristic for turbine balancing problem

Blade Sorting Method for Unbalance Minimization of an Aeroengine Concentric Rotor

A new Metropolis optimisation method, the cross-section particle collision algorithm: some preliminary results

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