Optimization of a Gate Valve Using Orthogonal Array and Kriging Model

2007 ◽  
Vol 345-346 ◽  
pp. 901-904
Author(s):  
Seung Hwan Oh ◽  
Jung Ho Kang ◽  
Won Sik Joo ◽  
Xue Guan Song ◽  
Hyeung Geol Kong ◽  
...  
Keyword(s):  
Orthogonal Array ◽  
Gate Valve ◽  
Kriging Model ◽  
Kriging Interpolation ◽  
Kriging Method ◽  
Approximation Model ◽  
Validation Method ◽  
Prediction Function ◽  
The One

The optimization of gate valve was performed using Kriging based approximation model. The DACE modeling, known as the one of Kriging interpolation, is introduced to obtain the surrogate approximation model of the function. In addition, we describe the definition, the prediction function and the algorithm of Kriging method and examine the accuracy of Kriging by using validation method.

Optimization of a Gate Valve Using Orthogonal Array and Kriging Model

2007 ◽  
pp. 901-904
Author(s):  
Seung Hwan Oh ◽  
Jung Ho Kang ◽  
Won Sik Joo ◽  
Xue Guan Song ◽  
Hyeung Geol Kong ◽  
...  
Keyword(s):  
Orthogonal Array ◽  
Gate Valve ◽  
Kriging Model

Adaptive Kriging Method for Uncertainty Quantification of the Photoelectron Sheath and Dust Levitation on the Lunar Surface

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Xinpeng Wei ◽  
Jianxun Zhao ◽  
Xiaoming He ◽  
Zhen Hu ◽  
Xiaoping Du ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Lunar Surface ◽  
Kriging Model ◽  
Upper And Lower Bounds ◽  
Model Parameters ◽  
Kriging Method ◽  
Photoelectron Sheath ◽  
The One ◽  
Adaptive Kriging ◽  
Dust Levitation

Abstract This paper presents an adaptive Kriging based method to perform uncertainty quantification (UQ) of the photoelectron sheath and dust levitation on the lunar surface. The objective of this study is to identify the upper and lower bounds of the electric potential and that of dust levitation height, given the intervals of model parameters in the one-dimensional (1D) photoelectron sheath model. To improve the calculation efficiency, we employ the widely used adaptive Kriging method (AKM). A task-oriented learning function and a stopping criterion are developed to train the Kriging model and customize the AKM. Experiment analysis shows that the proposed AKM is both accurate and efficient.

scholarly journals INTERPOLASI KRIGING DALAM PEMODELAN GSTAR-SUR DAN GSTARX-SUR PADA SERANGAN HAMA PENGGEREK BUAH KOPI

2020 ◽  
Vol 13 (1) ◽  
pp. 25-35
Author(s):  
Henny Pramoedyo ◽  
Arif Ashari ◽  
Alfi Fadliana
Keyword(s):  
Forecast Accuracy ◽  
Kriging Model ◽  
Complete Data ◽  
Kriging Interpolation ◽  
Coffee Berry ◽  
Coffee Berry Borer ◽  
Interpolation Technique

The GSTAR and GSTARX models normally can only be formed from observed locations. The problem that sometimes occurs is that not all locations that want to be modeled have complete data as well as other locations. This study uses GSTAR and GSTARX modeling using SUR approach and combines them with the kriging interpolation technique for forecasting coffee berry borer attack in Probolinggo Regency. This modeling is called GSTAR-SUR Kriging and GSTARX-SUR Kriging. This study aims to determine the best model between GSTAR-SUR Kriging and GSTARX-SUR Kriging for forecasting coffee borer attack in an unobserved location. The result of this study shows that GSTAR-SUR Kriging and GSTARX-SUR Kriging models can be used for forecasting coffee berry borer attack in unobserved locations with high forecast accuracy shown by MAPE values <10%. In this study the GSTARX-SUR Kriging model (1,[1,12])(10,0,0) is the best model for forecasting boffee berry borer attacks in unobserved locations.

Curve and Surface Modeling with Uncertainties Using Dual Kriging

1999 ◽  
Vol 121 (2) ◽  
pp. 249-255 ◽  
Author(s):  
A. Limaiem ◽  
H. A. ElMaraghy
Keyword(s):  
Measurement Errors ◽  
Geometric Modeling ◽  
Curve Surface ◽  
Tolerance Analysis ◽  
Kriging Model ◽  
Surface Modeling ◽  
Kriging Interpolation ◽  
Average Deviation ◽  
Dual Kriging ◽  
Potential Applications

This paper presents a new technique based on dual Kriging interpolation for modeling curves and surfaces in the presence of uncertainties in data points. Uncertainties result from measurement errors; therefore, a direct application of this method is found in curve/surface modeling using discrete sets of digitized points. It focuses on a common problem in geometric modeling, the trade-off between curve/surface smoothness and the approximation errors. The Kriging model filters the noise in the data while controlling the deviation locally at each point. However, the classical least-squares technique minimizes the average deviation, hence allowing only a global control of the model. The presented method generates smoother and more accurate representation of the actual curve or surface. It has potential applications in reverse engineering, NC machining, computer-aided inspection and tolerance analysis and verification. Examples of a computer mouse and a portion of the hood of a scaled-down car are presented for illustration.

Efficient uncertainty propagation for MAPOD via polynomial chaos-based Kriging

2019 ◽  
Vol 37 (1) ◽  
pp. 73-92 ◽  
Author(s):  
Xiaosong Du ◽  
Leifur Leifsson
Keyword(s):  
Polynomial Chaos ◽  
State Of The Art ◽  
Uncertainty Propagation ◽  
Kriging Model ◽  
Probability Of Detection ◽  
Kriging Interpolation ◽  
Benchmark Problems ◽  
Content Type ◽  
Order Of Magnitude ◽  
Polynomial Chaos Expansions

Purpose Model-assisted probability of detection (MAPOD) is an important approach used as part of assessing the reliability of nondestructive testing systems. The purpose of this paper is to apply the polynomial chaos-based Kriging (PCK) metamodeling method to MAPOD for the first time to enable efficient uncertainty propagation, which is currently a major bottleneck when using accurate physics-based models. Design/methodology/approach In this paper, the state-of-the-art Kriging, polynomial chaos expansions (PCE) and PCK are applied to “a^ vs a”-based MAPOD of ultrasonic testing (UT) benchmark problems. In particular, Kriging interpolation matches the observations well, while PCE is capable of capturing the global trend accurately. The proposed UP approach for MAPOD using PCK adopts the PCE bases as the trend function of the universal Kriging model, aiming at combining advantages of both metamodels. Findings To reach a pre-set accuracy threshold, the PCK method requires 50 per cent fewer training points than the PCE method, and around one order of magnitude fewer than Kriging for the test cases considered. The relative differences on the key MAPOD metrics compared with those from the physics-based models are controlled within 1 per cent. Originality/value The contributions of this work are the first application of PCK metamodel for MAPOD analysis, the first comparison between PCK with the current state-of-the-art metamodels for MAPOD and new MAPOD results for the UT benchmark cases.

scholarly journals Estimating CPT Parameters at Unsampled Locations Based on Kriging Interpolation Method

2021 ◽  
Vol 11 (23) ◽  
pp. 11264
Author(s):  
Jinhao Liu ◽  
Jinming Liu ◽  
Zhongwei Li ◽  
Xiaoyu Hou ◽  
Guoliang Dai
Keyword(s):  
Bearing Capacity ◽  
Direct Method ◽  
Precast Concrete ◽  
Kriging Interpolation ◽  
Soil Parameters ◽  
Engineering Practice ◽  
Kriging Method ◽  
True Value ◽  
Kriging Interpolation Method ◽  
Geotechnical Investigations

The cone penetrometer test (CPT) has been widely used in geotechnical investigations. However, how to use the limited CPT data to reasonably predict the soil parameters of the unsampled regions remains a challenge. In the present study, we adopted the Kriging method to obtain the CPT data of an unsampled location in Adelaide, South Australia, based on the collected CPT data from six soundings around this location. Interpolation results showed that the trend of the estimated parameters is consistent with the trend of parameters of the surrounding points. From the Kriging interpolation result, we further carried out axial bearing capacity calculation of a precast concrete pile using the CPT-based direct method to verify the reliability of the method. The calculated bearing capacity of the pile is 99.6 kN which is very close to the true value of 102.8 kN. Our results demonstrated the effectiveness of the Kriging method in considering the soil spatial variability and predicting soil parameters, which is quite suitable for the application in engineering practice.

Design of a low noise turbofan duct via an acoustic gradient-enhanced Kriging method

2020 ◽  
pp. 095440622093364
Author(s):  
S Qiu
Keyword(s):  
Shape Optimization ◽  
Adjoint Method ◽  
Kriging Model ◽  
Low Noise ◽  
Adjoint Equations ◽  
Design Parameters ◽  
Kriging Method ◽  
Gradient Information ◽  
Design Variables ◽  
Large Numbers

The Kriging models which are frequently used in aerodynamic shape optimization may become computationally inefficient when solving problems with large numbers of design variables. One solution to this problem would be the application of gradient-enhanced Kriging model. A gradient-enhanced Kriging and acoustic adjoint-method approach to duct acoustic problems is developed, aimed to improve the efficiency and accuracy of the existing Kriging approach at acoustic problems with many design parameters. To our knowledge, it is the first application of gradient-enhanced Kriging for duct acoustic problem. It employs a Kriging response surface in the parameter space, augmented with gradients obtained from the acoustic adjoint equations efficiently. The present paper aims at describing the potential of the gradient-enhanced Kriging method for low noise turbofan duct design. Prior to the optimization process, the implementation of the unsteady aeroacoustic adjoint method in shape optimization is validated by comparing the gradient values with that obtained by finite differences. In this work, the ordinary Kriging model and gradient-enhanced Kriging method are applied firstly to a benchmark functions and the results show that the additional gradient information can significantly enhance the accuracy of Kriging model. And then, the original Kriging-based, adjoint-based and the gradient-enhanced Kriging method are all used to model 50 variable duct acoustic problems, respectively. The test results show that this approach whose gradient information is introduced by using acoustic adjoint method developed from multimode LEE, named as acoustic gradient-enhanced Kriging, can significantly enhance the accuracy of Kriging models when the gradient data are available and thus provide an optimized low noise intake while maintaining the aerodynamic performance.

scholarly journals Kriging Method Optimization for the Process of DTM Creation Based on Huge Data Sets Obtained from MBESs

Geosciences ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 433 ◽  
Author(s):  
Maleika Wojciech
Keyword(s):  
Spatial Data ◽  
Moving Average ◽  
Digital Terrain Model ◽  
Computation Time ◽  
Fixed Number ◽  
Kriging Interpolation ◽  
Computational Time ◽  
Kriging Method ◽  
Terrain Model ◽  
Digital Terrain

The paper presents an optimized method of digital terrain model (DTM) estimation based on modified kriging interpolation. Many methods are used for digital terrain model creation; the most popular methods are: inverse distance weighing, nearest neighbour, moving average, and kriging. The latter is often considered to be one of the best methods for interpolation of non-uniform spatial data, but the good results with respect to model’s accuracy come at the price of very long computational time. In this study, the optimization of the kriging method was performed for the purpose of seabed DTM creation based on millions of measurement points obtained from a multibeam echosounder device (MBES). The purpose of the optimization was to significantly decrease computation time, while maintaining the highest possible accuracy of created model. Several variants of kriging method were analysed (depending on search radius, minimum of required points, fixed number of points, and used smoothing method). The analysis resulted in a proposed optimization of the kriging method, utilizing a new technique of neighbouring points selection throughout the interpolation process (named “growing radius”). Experimental results proved the new kriging method to have significant advantages when applied to DTM estimation.

Taylor Kriging Metamodeling for Stochastic Simulation Interpolation

2013 ◽  
pp. 25-39
Author(s):  
Heping Liu ◽  
Yanli Chen
Keyword(s):  
Stochastic Simulation ◽  
Ordinary Kriging ◽  
Taylor Expansion ◽  
Kriging Model ◽  
Kriging Interpolation ◽  
The Novel ◽  
Key Factors ◽  
Empirical Results ◽  
Simple Kriging ◽  
Kriging Metamodeling

This paper applies a novel Kriging model to the interpolation of stochastic simulation with high computational expense. The novel Kriging model is developed by using Taylor expansion to construct a drift function for Kriging, thus named Taylor Kriging. The interpolation capability of Taylor Kriging for stochastic simulation is empirically compared with those of Simple Kriging and Ordinary Kriging according to two stochastic simulation cases. Results show that the interpolation of Taylor Kriging is more accurate than Simple Kriging and Ordinary Kriging. The authors analyze two key factors in stochastic simulation, simulation replications and variance, which influence the accuracy of Kriging interpolation, and obtain some important empirical results.

scholarly journals INTERPOLASI SPASIAL DENGAN METODE ORDINARY KRIGING MENGGUNAKAN SEMIVARIOGRAM ISOTROPIK PADA DATA SPASIAL (Studi Kasus: Curah Hujan di Kabupaten Karangasem)

2015 ◽  
Vol 4 (1) ◽  
pp. 26
Author(s):  
PUTU MIRAH PURNAMA D. ◽  
KOMANG GDE SUKARSA ◽  
KOMANG DHARMAWAN
Keyword(s):  
Spatial Data ◽  
Ordinary Kriging ◽  
Spatial Interpolation ◽  
Interpolation Method ◽  
Kriging Interpolation ◽  
Control Points ◽  
Kriging Method ◽  
3D Data ◽  
Kriging Interpolation Method ◽  
Relationship Of

Spatial data is data that is presented in the geographic of an object, related to the location, shape and relationship of the earth in space. One of example of spatial data is rainfall. To determine the value of rainfall in an area, built to predict rain post information regarding rainfall. Spatial interpolation is used to estimate rainfall by collecting rainfall values held rain heading around. Assessment methods used in the estimate the rainfall in the Karangasem district is ordinary kriging using isotropic semivariogram that takes into account height on spatial data. Isotropic semivariogram which only takes into account the distance alone. Ordinary kriging method using isotropic semivariogram that takes into account height  value estimated rainfall is much different to the values at the control points Amlapura and Besakih. Interpolation on 3D data are not suitable for use on ordinary kriging method, grouping should be done at the data into a few weeks to application of ordinary kriging interpolation method using anisotropic semivariogram on 3D data.

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