Detection of Three-Dimensional Parameter of Defects for Gas Turbine Blades Based on Two-Dimensional Digital Radiographic Projective Imaging

Chen, Lei; Li, Bing; Zhou, Hao; Li, Zhangbing; Shang, Zhongyu

doi:10.1007/s10921-019-0640-3

Cited by 8 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such differences in the two models are caused by the free-form surface of the tested blade, which have been discussed in previous studies [46][47][48]. Because of the significant thickness variation in free-form objects, radiographic images are obtained using compromised exposure settings to capture all possible data.…”

Section: Discussionmentioning

confidence: 99%

Defects Prediction Method for Radiographic Images Based on Random PSO Using Regional Fluctuation Sensitivity

Shang

Chen

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

This paper presents an advanced methodology for defect prediction in radiographic images, predicated on a refined particle swarm optimization (PSO) algorithm with an emphasis on fluctuation sensitivity. Conventional PSO models with stable velocity are often beleaguered with challenges in precisely pinpointing defect regions in radiographic images, attributable to the lack of a defect-centric approach and the propensity for premature convergence. The proposed fluctuation-sensitive particle swarm optimization (FS-PSO) model, distinguished by an approximate 40% increase in particle entrapment within defect areas and an expedited convergence rate, necessitates a maximal additional time consumption of only 2.28%. The model, also characterized by reduced chaotic swarm movement, enhances efficiency through the modulation of movement intensity concomitant with the escalation in swarm size. The FS-PSO algorithm’s performance was rigorously evaluated via a series of simulations and practical blade experiments. The empirical findings evince that the FS-PSO model substantially outperforms the conventional stable velocity model, particularly in terms of shape retention in defect extraction.

show abstract

Section: Discussionmentioning

confidence: 99%

Defects Prediction Method for Radiographic Images Based on Random PSO Using Regional Fluctuation Sensitivity

Shang

Chen

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…As turbines are exposed to harsh operating conditions, predicting their remaining service life is important to reduce the risk of fracture. Turbine blades are currently subjected to regular in-service inspections and non-destructive detection of cracks using ultrasonic (Kalambe et al, 2020;Mevissen and Meo, 2020;Wang et al, 2021;Meng et al, 2023;Qin et al, 2023) and radiation methods (Błachnio et al, 2021;Zhang et al, 2023;Chen et al, 2019). However, because the crack propagation rate is relatively high in some areas methods for predicting the remaining life of a plant before crack initiation are required.…”

Section: Introductionmentioning

confidence: 99%

Non-destructive estimation of three-dimensional creep strain based on non-linear inverse analysis using displacement

FUJII,

OGAWA

2024

Mechanical Engineering Journal

View full text Add to dashboard Cite

In mechanical structures exposed to high temperatures, such as turbine blades for power generation, cracks are detected during in-service inspections. However, there is a risk of fracture owing to the relatively rapid rate of crack propagation, even when a large safety factor is in force. Therefore, a technique is required to estimate the creep-strain distribution before crack initiation and to predict the remaining service life. An inverse analysis method, based on the eigenstrain methodology, has been proposed to estimate the three-dimensional creep-strain distribution using non-destructively measured surface displacements. Three-dimensional creep-strain distributions can be estimated even when the relationship between the creep strain and displacement is non-linear. Through iterative calculations, this method converges to a solution with relatively high estimation accuracy, however, a method for estimating actual complex creep-strain distributions is needed. In this study, a method was proposed to improve the convergence of the iterative calculations. Its effectiveness was demonstrated by numerical analysis using a torsional geometry model that could be used to describe an actual turbine blade. Many unknown values were assumed so that relatively complex creep-strain distributions could be estimated. Convergence calculations were performed to improve the estimation accuracy through iterative calculations, even when displacement measurement errors were considered. The estimation accuracy was improved by using more actual measurements in the inverse analysis.

show abstract

Enhancement of Digital Radiographic Images for Gas Turbine Blades Based on Simple Scattering Model

Zhang

Chen

et al. 2023

J Nondestruct Eval

View full text Add to dashboard Cite

Detection of Three-Dimensional Parameter of Defects for Gas Turbine Blades Based on Two-Dimensional Digital Radiographic Projective Imaging

Cited by 8 publications

References 15 publications

Defects Prediction Method for Radiographic Images Based on Random PSO Using Regional Fluctuation Sensitivity

Defects Prediction Method for Radiographic Images Based on Random PSO Using Regional Fluctuation Sensitivity

Non-destructive estimation of three-dimensional creep strain based on non-linear inverse analysis using displacement

Enhancement of Digital Radiographic Images for Gas Turbine Blades Based on Simple Scattering Model

Contact Info

Product

Resources

About