Multi-levels Kriging surrogate model-based robust aerodynamics optimization design method

2020 ◽  
Vol 34 (14n16) ◽  
pp. 2040115
Author(s):  
Neng Xiong ◽  
Yang Tao ◽  
Jun Lin ◽  
Xue-Qiang Liu
Keyword(s):  
Design Optimization ◽  
Surrogate Model ◽  
Optimization Design ◽  
Design Method ◽  
Computational Cost ◽  
Optimization Method ◽  
Mean Value ◽  
Robust Design Optimization ◽  
Efficient Global Optimization ◽  
Kriging Surrogate Model

Robust design optimization has a great potential application in many engineering fields. In the conventional robust aerodynamics design optimization method, the main difficulty is expensive computational cost related to a large number of function evaluations for uncertainty quantification (UQ). To alleviate the expensive burden for UQ, two levels Kriging surrogate model was introduced. The first level is for the mean value and the second level is for the variances. Through the second level Kriging surrogate models, the method of Monte Carlo Simulation (MCS), which requires a huge number of function evaluations, can be effectively applied to the analysis of variance. Efficient Global Optimization algorithm (EGO) was employed to achieve the global optimized results. To validate the performance of the design method, both one-dimensional function and two-dimensional function were applied. Finally, robust aerodynamics design optimization was applied for a low-drag airfoil. The results show that the optimal solutions obtained from the uncertainty-based optimization formulation are less sensitive to uncertainties to small manufacturing errors.

scholarly journals SADEA-II: A generalized method for efficient global optimization of antenna design

2016 ◽  
Vol 4 (2) ◽  
pp. 86-97 ◽  
Author(s):  
Bo Liu ◽  
Slawomir Koziel ◽  
Nazar Ali
Keyword(s):  
Data Mining ◽  
Global Optimization ◽  
Design Optimization ◽  
Surrogate Model ◽  
Simulation Models ◽  
Optimization Methods ◽  
Optimization Method ◽  
Antenna Design ◽  
Efficient Global Optimization ◽  
Optimization Framework

Abstract Efficiency improvement is of great significance for simulation-driven antenna design optimization methods based on evolutionary algorithms (EAs). The two main efficiency enhancement methods exploit data-driven surrogate models and/or multi-fidelity simulation models to assist EAs. However, optimization methods based on the latter either need ad hoc low-fidelity model setup or have difficulties in handling problems with more than a few design variables, which is a main barrier for industrial applications. To address this issue, a generalized three stage multi-fidelity-simulation-model assisted antenna design optimization framework is proposed in this paper. The main ideas include introduction of a novel data mining stage handling the discrepancy between simulation models of different fidelities, and a surrogate-model-assisted combined global and local search stage for efficient high-fidelity simulation model-based optimization. This framework is then applied to SADEA, which is a state-of-the-art surrogate-model-assisted antenna design optimization method, constructing SADEA-II. Experimental results indicate that SADEA-II successfully handles various discrepancy between simulation models and considerably outperforms SADEA in terms of computational efficiency while ensuring improved design quality. Highlights An EFFICIENT antenna design global optimization method for problems requiring very expensive EM simulations. A new multi-fidelity surrogate-model-based optimization framework to perform RELIABLE efficient global optimization A data mining method to address distortions of EM models of different fidelities (bottleneck of multi-fidelity design).

Research on the Multidisciplinary Optimization Design Method for the Diesel Engine

2011 ◽  
Vol 84-85 ◽  
pp. 3-7
Author(s):  
Meng Sheng Wang ◽  
Rui Ping Zhou ◽  
Xiang Xu
Keyword(s):  
Diesel Engine ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimization Design ◽  
Design Method ◽  
Optimization Method ◽  
Multidisciplinary Optimization ◽  
Design Parameters ◽  
Technical Performance ◽  
The Mathematical Model

Multidisciplinary Design Optimization (MDO) is a new method for achieving an overall optimum design of the complex system. In this paper have researched how to make the mathematical model of the diesel engine system in the CO (Coordination Design Optimization) method, and applied it in the actual practice. The application result demonstrates that in this optimization method, we can achieve the optimal design of this diesel engine by the coordination of rationally configuring the design parameters, and improve the economy, the technical performance, the reliability and the service life of the designed engine.

scholarly journals How to compare performance of robust design optimization algorithms, including a novel method

2017 ◽  
Vol 31 (3) ◽  
pp. 286-297 ◽  
Author(s):  
Johan A. Persson ◽  
Johan Ölvander
Keyword(s):  
Robust Optimization ◽  
Design Optimization ◽  
Robust Design ◽  
Performance Metrics ◽  
Computational Cost ◽  
Optimization Method ◽  
Engineering Problem ◽  
Robust Design Optimization ◽  
Optimization Approach ◽  
Novel Method

AbstractThis paper proposes a method to compare the performances of different methods for robust design optimization of computationally demanding models. Its intended usage is to help the engineer to choose the optimization approach when faced with a robust optimization problem. This paper demonstrates the usage of the method to find the most appropriate robust design optimization method to solve an engineering problem. Five robust design optimization methods, including a novel method, are compared in the demonstration of the comparison method. Four of the five compared methods involve surrogate models to reduce the computational cost of performing robust design optimization. The five methods are used to optimize several mathematical functions that should be similar to the engineering problem. The methods are then used to optimize the engineering problem to confirm that the most suitable optimization method was identified. The performance metrics used are the mean value and standard deviation of the robust optimum as well as an index that combines the required number of simulations of the original model with the accuracy of the obtained solution. These measures represent the accuracy, robustness, and efficiency of the compared methods. The results of the comparison show that sequential robust optimization is the method with the best balance between accuracy and number of function evaluations. This is confirmed by the optimizations of the engineering problem. The comparison also shows that the novel method is better than its predecessor is.

Robust Design Optimization of Gear Parameters for a Wind Turbine Using Interval Analysis

2010 ◽  
Vol 224 (10) ◽  
pp. 2235-2245 ◽  
Author(s):  
R Dong ◽  
W Sun ◽  
H Xu
Keyword(s):  
Wind Turbine ◽  
Design Optimization ◽  
Robust Design ◽  
Optimization Method ◽  
Mean Value ◽  
Robust Design Optimization ◽  
Upper And Lower Bounds ◽  
Interval Numbers ◽  
Minimum Sensitivity ◽  
The Mean

A robust design optimization method is suggested to optimize the system parameters of a gear transmission for a wind turbine with minimum sensitivity of fatigue strength to variations in uncertain application factor, dynamic load coefficient, material property parameters, and other coefficients representing unknown working condition. Interval numbers are employed to model the uncertainties by which only the upper and lower bounds are needed. Based on interval mathematics, the original real-valued objective and constraint functions are replaced by the interval-valued functions, which directly represent the variation bounds of the new functions under uncertainty. The single-objective function is converted into two-objective functions for minimizing the mean value and the variation, and the constraint functions are reformulated with the acceptable robustness level, resulting in a bi-level mathematical model. The optimization results of gear parameter demonstrate the validity and feasibility of the presented method.

scholarly journals The Effect of Multi-Additional Sampling for Multi-Fidelity Efficient Global Optimization

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1499
Author(s):  
Atthaphon Ariyarit ◽  
Tharathep Phiboon ◽  
Masahiro Kanazaki ◽  
Sujin Bureerat
Keyword(s):  
Global Optimization ◽  
Surrogate Model ◽  
Computer Aided Design ◽  
Computational Cost ◽  
Sampling Technique ◽  
Efficient Global Optimization ◽  
Kriging Surrogate Model ◽  
Engineering Product ◽  
Sampling Points ◽  
Aided Design

Powerful computer-aided design tools are presently vital for engineering product development. Efficient global optimization (EGO) is one of the most popular methods for design of a high computational cost problem. The original EGO is proposed for only one additional sample point. In this work, parallel computing is applied to the original EGO process via a multi-additional sampling technique. The weak point of the multi-additional sampling is it has slower convergence rate when compared with the original EGO. This paper applies the multi-fidelity technique to the multi-additional EGO process to see the effect of the number of multi-additional sampling points and the converge rate. A co-kriging method and a hybrid RBF/Kriging surrogate model are selected for the surrogate model in the EGO process to show the advantage of the multi-additional EGO process compared with the single-fidelity Kriging surrogate model. In the experiment, single-additional sampling points and two to four number of multi-additional sampling per iteration are tested with symmetry and asymmetry mathematical test functions. The results show the hybrid RBF/Kriging surrogate model can obtain the similar optimal points when using the multi-additional sampling EGO.

scholarly journals Updated Kriging-Assisted Shape Optimization of a Gravity Dam

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 87
Author(s):  
Yongqiang Wang ◽  
Ye Liu ◽  
Xiaoyi Ma
Keyword(s):  
Surrogate Model ◽  
Computational Cost ◽  
Region Of Interest ◽  
Optimization Procedure ◽  
Optimal Shape ◽  
Small Sample ◽  
Gravity Dam ◽  
Kriging Surrogate Model ◽  
Gravity Dams ◽  
Sample Set

The numerical simulation of the optimal design of gravity dams is computationally expensive. Therefore, a new optimization procedure is presented in this study to reduce the computational cost for determining the optimal shape of a gravity dam. Optimization was performed using a combination of the genetic algorithm (GA) and an updated Kriging surrogate model (UKSM). First, a Kriging surrogate model (KSM) was constructed with a small sample set. Second, the minimizing the predictor strategy was used to add samples in the region of interest to update the KSM in each updating cycle until the optimization process converged. Third, an existing gravity dam was used to demonstrate the effectiveness of the GA–UKSM. The solution obtained with the GA–UKSM was compared with that obtained using the GA–KSM. The results revealed that the GA–UKSM required only 7.53% of the total number of numerical simulations required by the GA–KSM to achieve similar optimization results. Thus, the GA–UKSM can significantly improve the computational efficiency. The method adopted in this study can be used as a reference for the optimization of the design of gravity dams.

A new jig-shape optimization method for the high aspect ratio wing

2018 ◽  
Vol 91 (1) ◽  
pp. 124-133
Author(s):  
Zhe Yuan ◽  
Shihui Huo ◽  
Jianting Ren
Keyword(s):  
Aspect Ratio ◽  
Shape Optimization ◽  
High Aspect Ratio ◽  
Optimization Design ◽  
Design Method ◽  
Aircraft Design ◽  
Optimization Method ◽  
Attack Angle ◽  
Structural Deformation ◽  
Content Type

Purpose Computational efficiency is always the major concern in aircraft design. The purpose of this research is to investigate an efficient jig-shape optimization design method. A new jig-shape optimization method is presented in the current study and its application on the high aspect ratio wing is discussed. Design/methodology/approach First, the effects of bending and torsion on aerodynamic distribution were discussed. The effect of bending deformation was equivalent to the change of attack angle through a new equivalent method. The equivalent attack angle showed a linear dependence on the quadratic function of bending. Then, a new jig-shape optimization method taking integrated structural deformation into account was proposed. The method was realized by four substeps: object decomposition, optimization design, inversion and evaluation. Findings After the new jig-shape optimization design, both aerodynamic distribution and structural configuration have satisfactory results. Meanwhile, the method takes both bending and torsion deformation into account. Practical implications The new jig-shape optimization method can be well used for the high aspect ratio wing. Originality/value The new method is an innovation based on the traditional single parameter design method. It is suitable for engineering application.

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