Efficient Optimization Design Method Using Kriging Model

Jeong, Shinkyu; Murayama, Mitsuhiro; Yamamoto, Kazuomi

doi:10.2514/1.c10485e

Cited by 46 publications

(63 citation statements)

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“…The governing equation of Kriging model and derivation was reported in the Ref. [15,[24][25]. Kriging model is one of the global approximation models.…”

Section: Surrogate Modelsmentioning

confidence: 99%

“…Several author such as Donald [16] showed that the surrogate model is suitable for efficient design optimization. In addition, Jeong et al [15] applied a Kriging-based genetic algorithm to the aircraft design with reducing time consuming aerodynamics evaluations, Shahrokhi et al [17] successfully implemented a neural network method to evaluate the aerodynamics with consideration of the computation cost and M. Ghoreyshi and his partner, investigate the time dependent of surrogate approach to produce accurate predictions specifically in the transonic speed range pitching motions of X-31 aircraft [18].…”

Section: Introductionmentioning

confidence: 99%

“…Latin Hypercube Sampling (LHS) is used simultaneously to produce the sample variables of USAF DATCOM [23][24][25]. By using USAF DATCOM, the data mimic is developed to construct the Kriging model to make the prediction process [15]. Next after the determination of the aerodynamic force and derivatives coefficients, the estimation of static, dynamic and control stability characteristics needs to be done.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the prediction method is required when the function needs to be evaluated via a time-consuming solver that can be helping the reduction of time consumption of experiment or CFD [1,14]. Several types of surrogate models have been widely used in the engineering field, such as the Kriging model [15]. Several author such as Donald [16] showed that the surrogate model is suitable for efficient design optimization.…”

Section: Introductionmentioning

confidence: 99%

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Surrogate Model of Aerodynamic Model toward Efficient Digital Flight

Othman

Kanazaki

2015

Procedia Engineering

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Digital flight is the study commonly called flight simulation. In flight simulation analysis, the biggest challenges are about preparing the complete databases that can be invoked as result files in future. The ability to simulate in a computer regarding the flight motion must be convinced the governing equations of motion for six-degree of freedom. The accuracy of a flight simulation is depends to a large extent on the quality of an aerodynamic model database prediction. Key objective is to fly and simulate an aircraft in a computer environment with sufficient level of autonomy under the interest variables. In order to realize the goals, a surrogate model is used as the prediction function based on sampling data obtained by the design of experiments. The sample data for models are acquired from the USAF Stability and Control DATCOM which are considered as an exact function, and a database is constructed for two main variables angle of attack and Mach number along the X-force axis, Z-force axis, and pitching moment respectively. Furthermore, a comparison value of exact and predicted function was compared through the error production. The validation for the digital flight was qualified through the mode motion analysis and flying qualities scale to prove the mission level of the flight status.

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“…The governing equation of Kriging model and derivation was reported in the Ref. [15,[24][25]. Kriging model is one of the global approximation models.…”

Section: Surrogate Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Surrogate Model of Aerodynamic Model toward Efficient Digital Flight

Othman

Kanazaki

2015

Procedia Engineering

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“…An overview of Kriging models is described bellow. Kriging [6], [7], [8] is popular analysis approach for computer experiments for the purpose of creating a cheap-to-computer "meta-model" as a surrogate to a computationally expensive engineering simulation model. In this work, Kriging will replace the objective function during the optimization process.…”

Section: Kriging Modelmentioning

confidence: 99%

Optimization of Valve Disc Using Orthogonal Array and Kriging Model

Song¹,

Lin²,

Jung-Ho³

et al. 2008

AIP Conference Proceedings

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Kriging with trend functions nonlinear in their parameters: Theory and application in enzyme kinetics

Freier

Wiechert

Lieres

2017

Engineering in Life Sciences

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Kriging is an interpolation method commonly applied in empirical modeling for approximating functional relationships between impact factors and system response. The interpolation is based on a statistical analysis of given data and can optionally include a priori defined trend functions. However, Kriging can so far only be used with trend functions that are linear with respect to the parameters. In this contribution, we present an extension of the Kriging approach for handling trend functions that are nonlinear in their parameters. Our approach is based on Taylor linearization combined with an iterative parameter estimation procedure whose solution is practically computed via a root finding problem. We demonstrate our novel approach with measurement data from the application field of biocatalysis.

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Efficient Optimization Design Method Using Kriging Model

Cited by 46 publications

References 0 publications

Surrogate Model of Aerodynamic Model toward Efficient Digital Flight

Surrogate Model of Aerodynamic Model toward Efficient Digital Flight

Optimization of Valve Disc Using Orthogonal Array and Kriging Model

Kriging with trend functions nonlinear in their parameters: Theory and application in enzyme kinetics

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