Finite element model updating using deterministic optimisation: A global pattern search approach

Hofmeister, Benedikt; Bruns, Marlene; Rolfes, Raimund

doi:10.1016/j.engstruct.2019.05.047

Cited by 44 publications

(22 citation statements)

References 27 publications

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“…The optimization problem can be solved using continuous design variables

accounting for the location of the sensors over the physical domain of the structure or discrete design variables

accounting for the discrete locations (e.g., DOF at nodes for placing displacement/acceleration sensors or Gauss integration points for placing strains sensors in a finite element mesh). Global optimization algorithms [ 87 , 88 ] as well as stochastic optimization algorithms, such as CMA-ES [ 89 ] and genetic algorithms [ 45 , 90 , 91 , 92 , 93 ] can be employed in order to avoid premature convergence to a local optimum. Alternative heuristic forward and backward sequential sensor placement (FSSP/BSSP) algorithms [ 54 , 57 ] are effective in solving the optimization problem.…”

Section: Optimal Sensor Placement Formulationmentioning

confidence: 99%

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

Tulay

Papadimitriou

2021

Sensors

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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.

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“…The optimization problem can be solved using continuous design variables

accounting for the location of the sensors over the physical domain of the structure or discrete design variables

Section: Optimal Sensor Placement Formulationmentioning

confidence: 99%

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

Tulay

Papadimitriou

2021

Sensors

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“…Hence, a minimisation and a maximisation problem have to be solved for each realisation within the Monte Carlo integration. Here, they are solved using global optimisation by applying the global pattern search approach by Hofmeister et al 31 A track number of

T = 2

is chosen. A low track number results in a fairly local version of the global pattern search approach.…”

Section: Fatigue Lifetime Modellingmentioning

confidence: 99%

Analysis of the influence of climate change on the fatigue lifetime of offshore wind turbines using imprecise probabilities

Hübler

Rolfes

2020

Wind Energy

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When discussing the connection of wind energy and climate change, normally, the potential of wind energy to reduce green house gas emissions is emphasised. Hence, effects of wind energy on climate change are analysed. However, what about the other direction? What is the impact of climate change on wind energy? Recently, the effect of a reversal in global terrestrial stilling,that is, an increase in global wind speeds in the last decade, on the wind energy production has been analysed. Certainly, knowledge about potential changes in energy production is essential to plan future energy supply. Nonetheless, at least similarly important is the effect on loads acting on wind turbines. Increasing loads due to higher wind speeds might reduce wind turbine lifetimes and yield higher costs. Moreover, especially for already existing turbines, it might even affect the structural reliability. Since the impact of climate change on wind turbine loads is largely unknown, it is studied in this work in more detail. For this purpose, different existing models for predicted changes in wind speed and air temperature and their uncertainties are used to forecast the environmental conditions an exemplary offshore wind turbine is exposed to. Subsequently, for this turbine, the lifetime fatigue damages are calculated for different prediction models. It is shown that the expected changes in lifetime fatigue damages are present but relatively small compared to other uncertainties in the fatigue damage calculation.

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“…In order to ensure that the models provide an adequate representation of the governing system dynam-ics, they must be regularly calibrated through model updating [10]. Different studies have shown how indirect model updating-in which a subset of model parameters are estimated in an inverse problem setting-can allow for efficient calibration of numerical models of wind turbines and subsystems hereof [11,12,13,14].…”

Section: Introductionmentioning

confidence: 99%

Closed-loop updating of wind turbine models using blade pitch excitation

Schwarz-Wolf¹,

Ulriksen²,

Augustyn³

et al. 2021

Preprint

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Structural monitoring of wind turbines often involves the use of a numerical model, which must be regularly updated to yield an adequate representation of the governing dynamics. The model updating can be cast as an inverse problem, in which selected model parameters are estimated by minimizing the discrepancy between experimental target poles and model-predicted ones. An issue that may prevail in this setting is that the problem will be ill-posed, because the number of model parameters to be updated exceeds the number of target poles. The noted issue can be remedied by a virtual implementation of output feedback, which allows for computation of multiple closed-loop (CL) eigenstructures through non-linear transformations of the open-loop transfer matrix. The present paper offers an application study, in which virtual output feedback is used for updating numerical wind turbine models based on CL system poles. In particular, we explore the feasibility of employing the excitation stemming from the blade pitch control system as the input in the open-loop input-output realization required in the virtual output feedback. The methodological concept is outlined, and subsequently the applicability of the procedure is tested numerically in the context of simulations with a finite element model of a parked wind turbine. The preliminary findings suggest that procedures for customizing the pitch excitation to yield sufficient system excitation are required for the CL model updating to be feasible.

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Finite element model updating using deterministic optimisation: A global pattern search approach

Cited by 44 publications

References 27 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

Analysis of the influence of climate change on the fatigue lifetime of offshore wind turbines using imprecise probabilities

Closed-loop updating of wind turbine models using blade pitch excitation

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