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.

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 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.

Hybrid surrogate-model-based multi-fidelity efficient global optimization applied to helicopter blade design

2017 ◽  
Vol 50 (6) ◽  
pp. 1016-1040 ◽  
Author(s):  
Atthaphon Ariyarit ◽  
Masahiko Sugiura ◽  
Yasutada Tanabe ◽  
Masahiro Kanazaki
Keyword(s):  
Global Optimization ◽  
Surrogate Model ◽  
Efficient Global Optimization ◽  
Blade Design ◽  
Helicopter Blade ◽  
Model Based

scholarly journals A Kriging Surrogate Model for the Interference Reduction in the Settlement Surveillance Sensors of Steel Transmission Towers

2019 ◽  
Vol 9 (16) ◽  
pp. 3343 ◽  
Author(s):  
Jiajia Shi ◽  
Liu Chu ◽  
Eduardo Souza de Cursi
Keyword(s):  
Surrogate Model ◽  
High Efficiency ◽  
Computational Cost ◽  
Element Model ◽  
Kriging Surrogate Model ◽  
Transmission Tower ◽  
Modal Frequency ◽  
Interference Reduction ◽  
Transmission Towers ◽  
Tower Foundation

The utilization of modal frequency sensors is a feasible and effective way to monitor the settlement problem of the transmission tower foundation. However, the uncertainties and interference in the real operation environment of transmission towers highly affect the accuracy and identification of modal frequency sensors. In order to reduce the interference of modal frequency sensors for transmission towers, a Kriging surrogate model is proposed in this study. The finite element model of typical transmission towers is created and validated to provide the effective original database for the Kriging surrogate model. The prediction accuracy and convergences of the Kriging surrogate model are measured and confirmed. Besides the merits in computational cost and high-efficiency, the Kriging surrogate model is proven to have a satisfied and robust interference reduction capacity. Therefore, the Kriging surrogate model is feasible and competitive for interference filtration in the settlement surveillance sensors of steel transmission towers.

Orthogonal Distance Fields Representation for Machine-Learning Based Manufacturability Analysis

2020 ◽  
Author(s):  
Aditya Balu ◽  
Sambit Ghadai ◽  
Soumik Sarkar ◽  
Adarsh Krishnamurthy
Keyword(s):  
Machine Learning ◽  
Computer Aided Design ◽  
Boundary Representation ◽  
Engineering Product ◽  
Distance Fields ◽  
Computer Aided ◽  
Cad Models ◽  
Product Design Process ◽  
3D Cnn ◽  
Aided Design

Abstract Computer-aided Design for Manufacturing (DFM) systems play an essential role in reducing the time taken for product development by providing manufacturability feedback to the designer before the manufacturing phase. Traditionally, DFM rules are hand-crafted and used to accelerate the engineering product design process by integrating manufacturability analysis during design. Recently, the feasibility of using a machine learning-based DFM tool in intelligently applying the DFM rules have been studied. These tools use a voxelized representation of the design and then use a 3D-Convolutional Neural Network (3D-CNN), to provide manufacturability feedback. Although these frameworks work effectively, there are some limitations to the voxelized representation of the design. In this paper, we introduce a new representation of the computer-aided design (CAD) model using orthogonal distance fields (ODF). We provide a GPU-accelerated algorithm to convert standard boundary representation (B-rep) CAD models into ODF representation. Using the ODF representation, we build a machine learning framework, similar to earlier approaches, to create a machine learning-based DFM system to provide manufacturability feedback. As proof of concept, we apply this framework to assess the manufacturability of drilled holes. The framework has an accuracy of more than 84% correctly classifying the manufacturable and non-manufacturable models using the new representation.

Optimization of Heave Motions for a Four Column Semi-Submersible Based on Genetic Algorithm

2018 ◽  
Author(s):  
Suji Zhu
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Computer Aided Design ◽  
Transfer Functions ◽  
Parametric Modelling ◽  
Optimal Solutions ◽  
Global Optimization Algorithm ◽  
The Past ◽  
Computer Aided ◽  
Aided Design

Semi-submersible is designed with low heave motions compared with conventional ships by utilizing the cancelation effects between pontoons and columns. During the past years, continuous efforts have been devoted to reducing the heave amplitudes considering utilizing dry-tree system. Different concepts of semi-submersible have been proposed with favorable heave response. Deep draft semi-submersibles have been proved to be efficient in reducing the heave motions, and the damper structures under the pontoon may also reduce the heave responses significantly. Those concepts are beyond the conventional semi-submersible design, which may bring high costs for fabrication and installations. During computer-aided design and analysis, optimization algorithms are used to search for the optimal hull configuration. However, due to the restrictions of computer capacities, the global optimization algorithm, in some cases, have difficulties in finding out the optimal solutions without the aid from engineering experience. In this paper, the geometry of a ring-pontoon four-column semi-submersible is generated by parametric modelling. The heave transfer functions at center of gravity are calculated using WADAM. Genetic algorithm is used to find the most favorable heave responses. In the end, the parameters that influence the heave motions are summarized and discussed.

Mixed Efficient Global Optimization for Time-Dependent Reliability Analysis

2015 ◽  
Vol 137 (5) ◽  
Author(s):  
Zhen Hu ◽  
Xiaoping Du
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Global Optimization ◽  
Reliability Analysis ◽  
Surrogate Model ◽  
Extreme Value ◽  
Time Dependent ◽  
Efficient Global Optimization ◽  
Highly Nonlinear ◽  
Adaptive Kriging

Time-dependent reliability analysis requires the use of the extreme value of a response. The extreme value function is usually highly nonlinear, and traditional reliability methods, such as the first order reliability method (FORM), may produce large errors. The solution to this problem is using a surrogate model of the extreme response. The objective of this work is to improve the efficiency of building such a surrogate model. A mixed efficient global optimization (m-EGO) method is proposed. Different from the current EGO method, which draws samples of random variables and time independently, the m-EGO method draws samples for the two types of samples simultaneously. The m-EGO method employs the adaptive Kriging–Monte Carlo simulation (AK–MCS) so that high accuracy is also achieved. Then, Monte Carlo simulation (MCS) is applied to calculate the time-dependent reliability based on the surrogate model. Good accuracy and efficiency of the m-EGO method are demonstrated by three examples.

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