Fatigue-damage prediction for ship and offshore structures under wide-banded non-Gaussian random loadings part I: Approximation of cycle distribution in wide-banded gaussian random processes

Kim, Hyeonjin; Jang, Beom-Seon; Kim, Jeong Du

doi:10.1016/j.apor.2020.102294

Cited by 11 publications

(3 citation statements)

References 26 publications

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“…SHM consists of five keys: (i) sensing system description, (ii) sensor data acquisition, (iii) shape/strain/stress-sensing process, (iv) global/local failure prediction, and (v) decision-making process. Real-time deformation evaluation [ 8 , 9 ], vibration-based monitoring [ 10 , 11 , 12 , 13 ], and other SHM approaches [ 14 , 15 ] involve the implementation of sensors such as accelerometers, displacement sensors, or strain gauges [ 16 , 17 , 18 ], which are directly mounted on structural components to measure mechanical responses. SHM can assess the structural integrity, cracks, and damage during their initial phase and subsequently reduce the maintenance costs.…”

Section: Introductionmentioning

confidence: 99%

Sensor Placement Optimization for Shape Sensing of Plates and Shells Using Genetic Algorithm and Inverse Finite Element Method

Ghasemzadeh

Kefal

2022

Sensors

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This paper reports the first investigation of the inverse finite element method (iFEM) coupled with the genetic algorithm (GA) to optimize sensor placement models of plate/shell structures for their real-time and full-field deformation reconstruction. The primary goal was to reduce the number of sensors in the iFEM models while maintaining the high accuracy of the displacement results. Here, GA was combined with the four-node quadrilateral inverse-shell elements (iQS4) as the genes inherited through generations to define the optimum positions of a specified number of sensors. Initially, displacement monitoring of various plates with different boundary conditions under concentrated and distributed static/dynamic loads was conducted to investigate the performance of the coupled iFEM-GA method. One of these case studies was repeated for different initial populations and densities of sensors to evaluate their influence on the accuracy of the results. The results of the iFEM-GA algorithm indicate that an adequate number of individuals is essential to be assigned as the initial population during the optimization process to ensure diversity for the reproduction of the optimized sensor placement models and prevent the local optimum. In addition, practical optimization constraints were applied for each plate case study to demonstrate the realistic applicability of the implemented method by placing the available sensors at feasible sites. The iFEM-GA method’s capability in structural dynamics was also investigated by shape sensing the plate subjected to different dynamic loadings. Furthermore, a clamped stiffened plate and a curved shell were also considered to assess the applicability of the proposed method for the shape sensing of complex structures. Remarkably, the outcomes of the iFEM-GA approach with the reduced number of sensors agreed well with those of the full-sensor counterpart for all of the plate/shell case studies. Hence, this study reveals the superior performance of the iFEM-GA method as a viable sensor placement strategy for the accurate shape sensing of engineering structures with only a few sensors.

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Section: Introductionmentioning

confidence: 99%

Sensor Placement Optimization for Shape Sensing of Plates and Shells Using Genetic Algorithm and Inverse Finite Element Method

Ghasemzadeh

Kefal

2022

Sensors

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“…e selection of acceleration parameters and determination of the equivalent acceleration time directly affect the rationality of the acceleration fatigue life. In engineering problems, many loads exhibit obvious non-Gaussian characteristics, particularly under harsh working conditions, while the non-Gaussian characteristics of excitation are particularly obvious [1]. However, in the analysis, we usually assume that the random load on the structure obeys a stationary Gaussian distribution, which often leads to the danger of fatigue damage, which implies a hidden danger to the equipment service [2].…”

Section: Introductionmentioning

confidence: 99%

Fatigue Life Analysis of Aluminum Alloy Notched Specimens under Non‐Gaussian Excitation based on Fatigue Damage Spectrum

Chen

Yang

Zhang

et al. 2021

Shock and Vibration

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In this study, a non-Gaussian excitation acceleration method is proposed, using aluminum alloy notched specimens as a research object and measured acceleration signal of a certain airborne bracket, during aircraft flight as input excitations, based on the fatigue damage spectrum (FDS) theory. The kurtosis and skewness of the input signal are calculated and the non-Gaussian characteristics and amplitude distribution are evaluated. Five task segments obey a non-Gaussian distribution, while one task segment obeys a Gaussian distribution. The fatigue damage spectrum calculation method of non-Gaussian excitation is derived. The appropriate FDS calculation method is selected for each task segment and the acceleration parameters are set to construct the acceleration power spectral density, which is equivalent to the pseudo-acceleration damage. A finite-element model is established, the notch stress concentration factor of the specimen is calculated, the large mass point method is used to simulate the shaking table excitation, and a random vibration analysis is carried out to calculate the accelerated fatigue life. The simulation results show that the relative error between the original cumulative damage and test original fatigue life is 15.7%. The shaking table test results show that the relative error of fatigue life before and after acceleration is less than 16.95%, and the relative error of test and simulation is 24.27%. The failure time of the specimen is accelerated from approximately 12 h to 1 h, the acceleration ratio reaches 12, and the average acceleration ideal factor is 1.125, which verifies the effectiveness of the acceleration method. It provides a reference for the compilation of the load spectrum and vibration endurance acceleration test of other airborne aircraft equipment.

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“…In the following years, several spectral methods were developed to improve the accuracy of the fatigue assessment [18,19], when both low-and high-frequency components occur. In the last decade, improved methods were developed for bimodal Gaussian [20][21][22] and non-Gaussian processes [23][24][25][26][27][28], with the main aim of improving the accuracy of the fatigue damage assessment.…”

Section: Introductionmentioning

confidence: 99%

Fatigue Assessment of Moorings for Floating Offshore Wind Turbines by Advanced Spectral Analysis Methods

Piscopo

Scamardella

Rossi

et al. 2021

JMSE

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The fatigue assessment of mooring lines for floating offshore wind turbines represents a challenging issue not only for the reliable design of the stationkeeping system but also for the economic impact on the installation and maintenance costs over the entire lifetime of the offshore wind farm. After a brief review about the state-of-art, the nonlinear time-domain hydrodynamic model of floating offshore wind turbines moored by chain cables is discussed. Subsequently, the assessment of the fatigue damage in the mooring lines is outlined, focusing on the combined-spectrum approach. The relevant fatigue parameters, due to the low- and wave-frequency components of the stress process, are estimated by two different methods. The former is based on the time-domain analysis of the filtered stress process time history. The latter, instead, is based on the spectral analysis of the stress process by two advanced methods, namely the Welch and Thomson ones. Subsequently, a benchmark study is performed, assuming as reference floating offshore wind turbine the OC4-DeepCWind semisubmersible platform, equipped with the 5 MW NREL wind turbine. The cumulative fatigue damage is determined for eight load conditions, including both power production and parked wind turbine situations. A comparative analysis between time-domain and spectral analysis methods is also performed. Current results clearly show that the endorsement of advanced spectral analysis methods can be helpful to improve the reliability of the fatigue life assessment of mooring lines.

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Fatigue-damage prediction for ship and offshore structures under wide-banded non-Gaussian random loadings part I: Approximation of cycle distribution in wide-banded gaussian random processes

Cited by 11 publications

References 26 publications

Sensor Placement Optimization for Shape Sensing of Plates and Shells Using Genetic Algorithm and Inverse Finite Element Method

Sensor Placement Optimization for Shape Sensing of Plates and Shells Using Genetic Algorithm and Inverse Finite Element Method

Fatigue Life Analysis of Aluminum Alloy Notched Specimens under Non‐Gaussian Excitation based on Fatigue Damage Spectrum

Fatigue Assessment of Moorings for Floating Offshore Wind Turbines by Advanced Spectral Analysis Methods

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