Mechanics‐based model updating for identification and virtual sensing of an offshore wind turbine using sparse measurements

Nabiyan, Mansureh-Sadat; Khoshnoudian, Faramarz; Moaveni, Babak; Ebrahimian, Hamed

doi:10.1002/stc.2647

Cited by 35 publications

(19 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Following these works, the modal expansion and filtering techniques were applied for strain/stress virtual sensing [ 3 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ] and fatigue estimation [ 16 , 17 , 18 , 19 , 20 , 21 ] for a number of structures using limited number of displacement/strain physical sensors. In particular, modal expansion techniques have been used in mechanical and aerospace systems for shape and/or strain reconstruction using sparse displacement and/or strain measurements [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ] or fusing acceleration and strain measurements [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

“…In particular, modal expansion techniques have been used in mechanical and aerospace systems for shape and/or strain reconstruction using sparse displacement and/or strain measurements [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ] or fusing acceleration and strain measurements [ 30 ]. The aforementioned studies cover a number of applications, including components of mechanical structures [ 1 , 2 ], components of civil structures such as railway bridges [ 20 ], wind turbine jacket substructures [ 11 ], truss structures [ 12 ], wind turbine towers [ 7 , 8 , 9 , 10 , 16 , 25 ], wind turbine blades [ 27 , 28 ], offshore structures [ 11 ], components of industrial structures [ 17 , 21 ], roller coaster [ 18 ], rotating [ 26 ] and underwater structures [ 13 ], as well as biologicaly inspired wing structures for robotic applications [ 29 ]. Recently, it is suggested to use virtual sensing in isolated linear components of linear and nonlinear models of structures [ 31 , 32 , 33 , 34 ] considering the forces at the interface between the analyzed linear component and the rest of the structure as unknown forces.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Sensor Placement for Reliable Virtual Sensing Using Modal Expansion and Information Theory

Tulay

Papadimitriou

2021

Sensors

View full text Add to dashboard Cite

A framework for optimal sensor placement (OSP) for virtual sensing using the modal expansion technique and taking into account uncertainties is presented based on information and utility theory. The framework is developed to handle virtual sensing under output-only vibration measurements. The OSP maximizes a utility function that quantifies the expected information gained from the data for reducing the uncertainty of quantities of interest (QoI) predicted at the virtual sensing locations. The utility function is extended to make the OSP design robust to uncertainties in structural model and modeling error parameters, resulting in a multidimensional integral of the expected information gain over all possible values of the uncertain parameters and weighted by their assigned probability distributions. Approximate methods are used to compute the multidimensional integral and solve the optimization problem that arises. The Gaussian nature of the response QoI is exploited to derive useful and informative analytical expressions for the utility function. A thorough study of the effect of model, prediction and measurement errors and their uncertainties, as well as the prior uncertainties in the modal coordinates on the selection of the optimal sensor configuration is presented, highlighting the importance of accounting for robustness to errors and other uncertainties.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Sensor Placement for Reliable Virtual Sensing Using Modal Expansion and Information Theory

Tulay

Papadimitriou

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…For this purpose, the DT is used in a forward simulation for a given input motion and the local responses (e.g., section moment curvature, material stress-strain) are collected and used for damage localization and quantification. The process is often referred to as virtual sensing [ 45 ]. Although the level of available earthquake motions was small in this study and thus, no damage is expected in the structure, the application of the DT can still be demonstrated.…”

Section: Digital Twin and Virtual Sensing Applicationmentioning

confidence: 99%

Bridge Digital Twinning Using an Output-Only Bayesian Model Updating Method and Recorded Seismic Measurements

Ghahari

Malekghaini

Ebrahimian

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

Rapid post-earthquake damage diagnosis of bridges can guide decision-making for emergency response management and recovery. This can be facilitated using digital technologies to remove the barriers of manual post-event inspections. Prior mechanics-based Finite Element (FE) models can be used for post-event response simulation using the measured ground motions at nearby stations; however, the damage assessment outcomes would suffer from uncertainties in structural and soil material properties, input excitations, etc. For instrumented bridges, these uncertainties can be reduced by integrating sensory data with prior models through a model updating approach. This study presents a sequential Bayesian model updating technique, through which a linear/nonlinear FE model, including soil-structure interaction effects, and the foundation input motions are jointly identified from measured acceleration responses. The efficacy of the presented model updating technique is first examined through a numerical verification study. Then, seismic data recorded from the San Rogue Canyon Bridge in California are used for a real-world case study. Comparison between the free-field and the foundation input motions reveals valuable information regarding the soil-structure interaction effects at the bridge site. Moreover, the reasonable agreement between the recorded and estimated bridge responses shows the potentials of the presented model updating technique for real-world applications. The described process is a practice of digital twinning and the updated FE model is considered as the digital twin of the bridge and can be used to analyze the bridge and monitor the structural response at element, section, and fiber levels to diagnose the location and severity of any potential damage mechanism.

show abstract

“…In that sense, it is important to assess the degree of influence that both the vibration modes and the excitation imposed by the harmonics (due to the rotor rotation and the tower shadow effect) hold over the structure. This information is especially helpful when designing damping devices, such as tuned mass dampers, usually installed in offshore wind turbines 6,7 and for the validation of digital twins 8,9 …”

Section: Introductionmentioning

confidence: 99%

Modal decomposition of the dynamic response of wind turbine during one year of continuous monitoring

Oliveira

Magalhães

Cunha

et al. 2021

Struct Control Health Monit

View full text Add to dashboard Cite

Summary The analysis of the dynamic behavior of wind turbines is a complex task. These structures present diverse configurations, dependent on the wind conditions. Consequently, the dynamic behavior is highly dependent on their operating conditions. This paper analyzes the dynamic behavior of a 2.0 MW wind turbine during 1 year of operation. In that sense, post‐processing tools aiming to decompose the measured vibration response of structures into modal responses, originally developed to be applied with the SSI‐DATA algorithm, are implemented and applied. Initially, an original data processing that permits the application of these tools to two additional output‐only modal identification algorithms is introduced. Then, these tools are applied for the first time to data collected in a wind turbine during a period of 1 year. The obtained results evidenced the effectiveness of the proposed post‐processing procedure and the distinct dynamic response of the wind turbine according to its operating conditions.

show abstract

Mechanics‐based model updating for identification and virtual sensing of an offshore wind turbine using sparse measurements

Cited by 35 publications

References 37 publications

Optimal Sensor Placement for Reliable Virtual Sensing Using Modal Expansion and Information Theory

Optimal Sensor Placement for Reliable Virtual Sensing Using Modal Expansion and Information Theory

Bridge Digital Twinning Using an Output-Only Bayesian Model Updating Method and Recorded Seismic Measurements

Modal decomposition of the dynamic response of wind turbine during one year of continuous monitoring

Contact Info

Product

Resources

About