Kriging/RBF-Hybrid Response Surface Methodology for Highly Nonlinear Functions

Namura, Nobuo; Shimoyama, Koji; Jeong, Shinkyu; Obayashi, Shigeru

doi:10.1299/jcst.6.81

Cited by 10 publications

(6 citation statements)

References 13 publications

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“…On the other hand, the UK model is a hybrid method between the PR and Kriging models. It effectively approximates the output function consisting of complex macro‐ and micro‐trends . In the UK model, the PR model approximates the macro‐trend of the function, and the Kriging model approximates the micro‐trend of the function.…”

Section: Wind Shear Estimation Modelmentioning

confidence: 99%

“…A WSEM is generated as a surrogate model 17 polynomial regression (PR), 18 radial basis function (RBF), 19,20 ordinary Kriging (OK), 21 and universal Kriging (UK), 22 to compare their estimation accuracy and select a suitable model for wind shear estimation.…”

Section: Surrogate Model Generationmentioning

confidence: 99%

“…The leaning data consist of the values of input variables (eg, M y and V ( z ref ) for the WSEM) and an output function (eg, α ). We use four types of surrogate models, polynomial regression (PR), radial basis function (RBF), ordinary Kriging (OK), and universal Kriging (UK), to compare their estimation accuracy and select a suitable model for wind shear estimation.…”

Section: Wind Shear Estimation Modelmentioning

confidence: 99%

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Wind shear estimation model using load measurement of wind turbine tower and surrogate model

Namura

2019

Wind Energy

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A wind shear estimation method based on fore-aft moment is proposed to estimate wind shear strength without a Doppler lidar. We construct wind shear estimation models (WSEMs) using surrogate models whose input is the time-averaged fore-aft moment and various supervisory control and data acquisition (SCADA) system data.Learning data for the WSEMs are generated by numerical simulation or field measurement of a real turbine using SCADA, strain gauges, and Doppler lidar. By using simulation data, we construct 20 WSEMs with various input combinations and surrogate methods to select a model with the highest accuracy. The best WSEM is constructed with the universal Kriging surrogate model and uses the fore-aft moment and wind speed as its input. Subsequently, the best WSEM is applied to a real turbine to validate its accuracy in real wind conditions, and we confirm that the WSEM has reasonable accuracy. However, the estimation error in the real wind condition is about twice as high as that in the simulation due to the real wind shear not completely corresponding to the assumed wind profile and a large yaw error. Further improvement in wind shear estimation accuracy will be achieved by adding yaw error and turbulence intensity to the input variables and applying the WSEM to wind farms on simple terrain or offshore wind farms where wind profile error decreases.

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Section: Wind Shear Estimation Modelmentioning

confidence: 99%

Section: Surrogate Model Generationmentioning

confidence: 99%

Section: Wind Shear Estimation Modelmentioning

confidence: 99%

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Wind shear estimation model using load measurement of wind turbine tower and surrogate model

Namura

2019

Wind Energy

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“…Many researchers have done a lot of work on the surrogate-based optimization problem [8][9][10][11]. The response surface methodology [12][13] become a research hotspot. Especially, surrogate model is widely applied in the field of Turbomachinery, such as axial compressor [14] and helicopter rotor blade [15].…”

Section: Introductionmentioning

confidence: 99%

Surrogate-Based Aerodynamic Shape Optimization of Contra Rotating Open Rotor

Wang¹,

Li²,

Wang³

2022

Proceedings of Global Power &Amp; Propulsion Society

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In this paper, the study on aerodynamic shape optimization of contra rotating open rotor is presented. Computational fluid dynamics simulations combined with Neural networks (radial basis function) surrogate model is applied to solve the problem. The difficulty is associated with both time-consuming computational fluid dynamics simulations and the large number of design variables involved. The contra rotating open rotor optimization problem is to find the variables that produces the maximum propulsive efficiency for a limited range of thrust requirement. The DOE (deign of experiment) process is completed by uniform design and computational fluid dynamics simulations. The non-linear programming by quadratic lagrangian (NLPQL) is used to tackle the optimization problem. Infill sampling criteria is used in this process to reduce demand on the number of initial samples while keep the accuracy of surrogate model. After building the initial surrogate from DOE sampling, the update point is selected according to the infill sampling criteria. It shows that the infill sampling criteria is helpful in surrogate model building process and surrogate-based optimization method is valid for aerodynamic shape optimization of contra rotating open rotor.

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“…During the model validation and selection process, the commonly used method is dividing the data into two mutually exclusive subsets called the training set and the validation set, which is called the holdout method (Kohavi 1995). This method only uses part of the data to train the surrogate model and uses the rest of data to validate the surrogate model (Namura et al 2012), which may result in overfitting of the training data, and underfitting of the other data. Cross validation is an improvement of holdout method because it uses all data for both training and validation.…”

Section: Introductionmentioning

confidence: 99%

Comparison of surrogate models with different methods in groundwater remediation process

Luo

2014

J Earth Syst Sci

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Surrogate modelling is an effective tool for reducing computational burden of simulation optimization. In this article, polynomial regression (PR), radial basis function artificial neural network (RBFANN), and kriging methods were compared for building surrogate models of a multiphase flow simulation model in a simplified nitrobenzene contaminated aquifer remediation problem. In the model accuracy analysis process, a 10-fold cross validation method was adopted to evaluate the approximation accuracy of the three surrogate models. The results demonstrated that: RBFANN surrogate model and kriging surrogate model had acceptable approximation accuracy, and further that kriging model's approximation accuracy was slightly higher than RBFANN model. However, the PR model demonstrated unacceptably poor approximation accuracy. Therefore, the RBFANN and kriging surrogates were selected and used in the optimization process to identify the most cost-effective remediation strategy at a nitrobenzenecontaminated site. The optimal remediation costs obtained with the two surrogate-based optimization models were similar, and had similar computational burden. These two surrogate-based optimization models are efficient tools for optimal groundwater remediation strategy identification.

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Kriging/RBF-Hybrid Response Surface Methodology for Highly Nonlinear Functions

Cited by 10 publications

References 13 publications

Wind shear estimation model using load measurement of wind turbine tower and surrogate model

Wind shear estimation model using load measurement of wind turbine tower and surrogate model

Surrogate-Based Aerodynamic Shape Optimization of Contra Rotating Open Rotor

Comparison of surrogate models with different methods in groundwater remediation process

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