Systematic identification of drag coefficients for a heaving wave follower

Davis, Andrew F.; Fabien, Brian C.

doi:10.1016/j.oceaneng.2018.08.054

Cited by 7 publications

(16 citation statements)

References 9 publications

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“…Finally, the drag coefficient is given by C D . A discussion on the treatment of the viscous drag force as well as an identification method can be found in [10] A small linear damping force bV is included to compensate for the fluid shear forces acting on the body and linear damping in the mooring lines.…”

Section: Drag Force F Dmentioning

confidence: 99%

“…A typical assumption for the development of a Kalman filter is to assume that no cross-correlation exists between states [11]. For the equation of motion the radiation states are excepted from having a process noise variance as the error in the radiation force is included in the error for the position and velocity states [10].…”

Section: Process Noisementioning

confidence: 99%

“…A similar disturbance model estimation method to [6] is [9], where a linear Kalman filter is used on simulation data to perform an investigation into excitation force estimation and prediction. The direct estimation method shown in [7] applies the same principle that is used for drag estimation in [10] and unknown parameter identification in [11]. The work of [12] provides an overview of control algorithms, many of which rely on the use of a linear Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

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Wave excitation force estimation of wave energy floats using extended Kalman filters

Davis

Fabien

2020

Ocean Engineering

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In many advanced control strategies the wave excitation force is key to determining the control input. However, it is often difficult to measure the excitation force on a Wave Energy Converter (WEC). The use of Kalman filters to estimate the wave excitation force based on readily available measurement data can potentially fill the gap between the development of WEC control strategies and the data that is available. Two different estimation methods using an nonlinear Extended Kalman Filter are tested on experimental wave tank data for a heaving semi-submerged float. The first method relies on directly including the excitation force as a state in the first order dynamics-which allows the "random walk" of the Kalman filter to identify an estimate of the excitation force.The second method of estimation involves modeling the wave excitation force as a harmonic oscillator comprised of sinusoidal components. Both methods are evaluated for a variety of incident waves and additional sensitivity analyses are performed to investigate the susceptibility of these estimation methods to changes in the model, measurement noise, and sampling rate.

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Section: Drag Force F Dmentioning

confidence: 99%

Section: Process Noisementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wave excitation force estimation of wave energy floats using extended Kalman filters

Davis

Fabien

2020

Ocean Engineering

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“…An alternative method is to use system identification. The training data can be obtained from CFD simulation [2] or scale experiments in a towing tank [20,21]. A popular method is to estimate t he real hydrodynamic force using an EKF observer, which assumes that the excitation force can be represented as the sum of a finite set of harmonic components [22,23].…”

Section: Introductionmentioning

confidence: 99%

Gray- and Black-Box Modeling of Ships and Wave Energy Converters Based on Bayesian Regression

Liu¹,

Xue²,

Huang³

et al. 2020

Preprint

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Establishing an accurate mathematical model is the foundation of simulating the motion of marine vehicles and structures, and it is the basis of modeling-based control design. System identification from observed input-output data is a practical and powerful method. However, for modeling objects with different characteristics and known information, a single modeling framework can hardly meet the requirements of model establishment. Moreover, there are some challenges in system identification, such as parameter drift and overfitting. In this work, three robust methods are proposed for generating ocean hydrodynamic models based on Bayesian regression. Two Bayesian techniques, semi-conjugate linear regression and noisy input Gaussian process regression, are used for parametric and nonparametric gray-box modeling and black-box modeling. The experimental free-running tests of the KVLCC2 ship model and a multi-freedom wave energy converter (WEC) are used to validate the proposed Bayesian models. The results demonstrate that the proposed schemes for system identification of the ship and WEC have good generalization ability and robustness. Finally, the developed modeling methods are evaluated considering the aspects required conditions, operating characteristics and prediction accuracy.

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“…The viscous force can have a significant impact on the control strategy considered. For a heaving wave follower type wave energy device, the identification of the drag using an extended Kalman filter was reported in Reference [13]; the study showed the importance of the viscous correction in relation to the control system performance.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Viscous Drag for a Domed Cylindrical Moored Wave Energy Converter

Bhinder

Murphy

2019

JMSE

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Viscous drag, nonlinear in nature, is an important aspect of the fluid–structure interaction modelling and is usually not taken into account when the fluid is assumed to be inviscid. Potential flow solvers can competently compute radiation damping, which is related to the radiated wave field. However, the drag damping primarily related to the viscous effects is usually neglected in the radiation/diffraction problems solved by the boundary element method (BEM), also known as the boundary integral element method (BIEM). This drag force can have a significant impact in the case of structures extending much deeper below the free surface, or for those that are completely submerged. In this paper, the drag coefficient C d was quantified for the heave and surge response of a structure which consists of a moored horizontally oriented domed cylinder with two surface piercing square columns located at the top surface. The domed cylinder is the primary part and is submerged. The drag coefficient is estimated using the experimental measurements related to harmonic monochromatic wave–structure interaction. Finally, this estimated drag coefficient was used in the modified time domain model, which includes the nonlinear viscous correction term, and the resulting device response in heave and surge directions is presented for an irregular incoming wave field. The comparison of the numerical model and the experiments validates the estimated C d values obtained earlier. Prior to the time domain model, frequency-dependent parameters such as added mass, radiation damping, and excitation force were computed using three mainstream potential flow packages (that is, ANSYS AQWA, WAMIT, and NEMOH), and a comparison is presented. The effect of free surface on the drag coefficient is investigated through differences in C d values between heave and surge modes.

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Systematic identification of drag coefficients for a heaving wave follower

Cited by 7 publications

References 9 publications

Wave excitation force estimation of wave energy floats using extended Kalman filters

Wave excitation force estimation of wave energy floats using extended Kalman filters

Gray- and Black-Box Modeling of Ships and Wave Energy Converters Based on Bayesian Regression

Evaluation of the Viscous Drag for a Domed Cylindrical Moored Wave Energy Converter

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