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.

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.

Using Kriging Surrogate Models to Predict the Vibration Responses of a Submerged Riser

2020 ◽  
Author(s):  
Marcelo Damasceno ◽  
Hélio Ribeiro Neto ◽  
Tatiane Costa ◽  
Aldemir Cavalini Júnior ◽  
Ludimar Aguiar ◽  
...  
Keyword(s):  
Surrogate Model ◽  
High Performance ◽  
Computational Cost ◽  
Offshore Structures ◽  
Surrogate Models ◽  
Computational Time ◽  
Kriging Surrogate Model ◽  
Efficient Manner ◽  
Modeling Tools ◽  
Fluid Structure

Abstract Fluid-structure interaction modeling tools based on computational fluid dynamics (CFD) produce interesting results that can be used in the design of submerged structures. However, the computational cost of simulations associated with the design of submerged offshore structures is high. There are no high-performance platforms devoted to the analysis and optimization of these structures using CFD techniques. In this context, this work aims to present a computational tool dedicated to the construction of Kriging surrogate models in order to represent the time domain force responses of submerged risers. The force responses obtained from high-cost computational simulations are used as outputs for training and validated the surrogate models. In this case, different excitations are applied in the riser aiming at evaluating the representativeness of the obtained Kriging surrogate model. A similar investigation is performed by changing the number of samples and the total time used for training purposes. The present methodology can be used to perform the dynamic analysis in different submerged structures with a low computational cost. Instead of solving the motion equation associated with the fluid-structure system, a Kriging surrogate model is used. A significant reduction in computational time is expected, which allows the realization of different analyses and optimization procedures in a fast and efficient manner for the design of this type of structure.

Experimental study of kriging-based crack identification in plate structure

2016 ◽  
Vol 231 (17) ◽  
pp. 3118-3129 ◽  
Author(s):  
Haiyang Gao ◽  
Xiaofei Hu ◽  
Fang Han ◽  
Xinming Li ◽  
Jungang Zhang
Keyword(s):  
Surrogate Model ◽  
Crack Detection ◽  
Model Updating ◽  
Computational Cost ◽  
Measurement Noise ◽  
Dynamic Responses ◽  
Detection Methods ◽  
Crack Identification ◽  
Kriging Surrogate Model ◽  
Plate Structure

One of the major issues that existing crack identification methods utilizing dynamic responses are facing is the limitation of engineering feasibility. How to suppress the effect of measurement noise and improve the identification accuracy is still challenging. In this work, an effective method is proposed to identify the size of an arbitrary internal crack in plate structure based on a Kriging surrogate model, and a series of laboratory tests are designed to verify the practicability of this strategy. The initial Kriging surrogate model is constructed by samples of crack parameters (tip locations) and corresponding root mean square (RMS) of random responses as the inputs and outputs, respectively. To further improve the surrogate accuracy and reduce computational cost during the inverse problem, an optimal point-adding process for Kriging model updating is then carried out. Experimental results of crack identification in a cantilever plate indicate that the proposed method can be an alternative to conventional crack detection methods even in the presence of measurement noise and modeling errors.

A Computational Efficient Method to Assess the Sensitivity of Finite-Element Models: An Illustration With the Hemipelvis

2016 ◽  
Vol 138 (12) ◽  
Author(s):  
Dermot O'Rourke ◽  
Saulo Martelli ◽  
Murk Bottema ◽  
Mark Taylor
Keyword(s):  
Finite Element ◽  
Factorial Design ◽  
Surrogate Model ◽  
Computational Cost ◽  
Bone Geometry ◽  
Surrogate Models ◽  
Full Factorial Design ◽  
Fe Model ◽  
Kriging Surrogate Model ◽  
Full Factorial

Assessing the sensitivity of a finite-element (FE) model to uncertainties in geometric parameters and material properties is a fundamental step in understanding the reliability of model predictions. However, the computational cost of individual simulations and the large number of required models limits comprehensive quantification of model sensitivity. To quickly assess the sensitivity of an FE model, we built linear and Kriging surrogate models of an FE model of the intact hemipelvis. The percentage of the total sum of squares (%TSS) was used to determine the most influential input parameters and their possible interactions on the median, 95th percentile and maximum equivalent strains. We assessed the surrogate models by comparing their predictions to those of a full factorial design of FE simulations. The Kriging surrogate model accurately predicted all output metrics based on a training set of 30 analyses (R2 = 0.99). There was good agreement between the Kriging surrogate model and the full factorial design in determining the most influential input parameters and interactions. For the median, 95th percentile and maximum equivalent strain, the bone geometry (60%, 52%, and 76%, respectively) was the most influential input parameter. The interactions between bone geometry and cancellous bone modulus (13%) and bone geometry and cortical bone thickness (7%) were also influential terms on the output metrics. This study demonstrates a method with a low time and computational cost to quantify the sensitivity of an FE model. It can be applied to FE models in computational orthopaedic biomechanics in order to understand the reliability of predictions.

scholarly journals Crack Identification of Cantilever Plates Based on a Kriging Surrogate Model

2013 ◽  
Vol 135 (5) ◽  
Author(s):  
Haiyang Gao ◽  
Xinglin Guo ◽  
Huajiang Ouyang ◽  
Fang Han
Keyword(s):  
Surrogate Model ◽  
Computational Cost ◽  
Kriging Model ◽  
Crack Identification ◽  
Internal Crack ◽  
Kriging Surrogate Model ◽  
Optimal Point ◽  
Initial Sampling ◽  
Searching Ability ◽  
Random Responses

This work presents an effective method to identify the tip locations of an internal crack in cantilever plates based on a Kriging surrogate model. Samples of varying crack parameters (tip locations) and their corresponding root mean square (RMS) of random responses are used to construct the initial Kriging surrogate model. Moreover, the pseudo excitation method (PEM) is employed to speed up the spectral analysis. For identifying crack parameters based on the constructed Kriging model, a robust stochastic particle swarm optimization (SPSO) algorithm is adopted for enhancing the global searching ability. To improve the accuracy of the surrogate model without using extensive samples, a small number of samples are first used. Then an optimal point-adding process is carried out to reduce computational cost. Numerical studies of a cantilever plate with an internal crack are performed. The effectiveness and efficiency of this method are demonstrated by the identified results. The effect of initial sampling size on the precision of the identified results is also investigated.

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 Shape Optimization of Single-Curvature Arch Dam Based on Sequential Kriging-Genetic Algorithm

2019 ◽  
Vol 9 (20) ◽  
pp. 4366
Author(s):  
Yong-Qiang Wang ◽  
Rong-Heng Zhao ◽  
Ye Liu ◽  
Yi-Zheng Chen ◽  
Xiao-Yi Ma
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Optimization Method ◽  
Small Sample ◽  
Arch Dam ◽  
Genetic Optimization ◽  
The Finite Element Method ◽  
Kriging Surrogate Model ◽  
Arch Dams ◽  
Sample Set

Shape optimization of single-curvature arch dams using the finite element method (FEM) is often computationally expensive. To reduce the computational burden, this study introduces a new optimization method, combining a genetic algorithm with a sequential Kriging surrogate model (GA-SKSM), for determining the optimal shape of a single-curvature arch dam. At the start of genetic optimization, a KSM was constructed using a small sample set. In each iteration of optimization, the minimizing predictor criterion and low confidence bound criterion were used to collect samples from the domain of interest and accumulate them into a small sample set to update the KSM until the optimization process converged. A practical problem involving the optimization of a single-curvature arch dam was solved using the introduced GA-SKSM, and the performance of the method was compared with that of GA-KSM and GA-FEM methods. The results revealed that the GA-SKSM method required only 5.40% and 12.40% of the number of simulations required by the GA-FEM and GA-KSM methods, respectively. The GA-SKSM method can significantly improve computational efficiency and can serve as a reference for effective optimization of the design of single-curvature arch dams.

scholarly journals Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems

Vibration ◽  
2020 ◽  
Vol 4 (1) ◽  
pp. 49-63
Author(s):  
Waad Subber ◽  
Sayan Ghosh ◽  
Piyush Pandita ◽  
Yiming Zhang ◽  
Liping Wang
Keyword(s):  
Dynamical Systems ◽  
Computational Cost ◽  
Three Dimensional ◽  
Region Of Interest ◽  
System Response ◽  
Computational Domain ◽  
User Interest ◽  
Numerical Resolution ◽  
Multi Scale ◽  
Operation Conditions

Industrial dynamical systems often exhibit multi-scale responses due to material heterogeneity and complex operation conditions. The smallest length-scale of the systems dynamics controls the numerical resolution required to resolve the embedded physics. In practice however, high numerical resolution is only required in a confined region of the domain where fast dynamics or localized material variability is exhibited, whereas a coarser discretization can be sufficient in the rest majority of the domain. Partitioning the complex dynamical system into smaller easier-to-solve problems based on the localized dynamics and material variability can reduce the overall computational cost. The region of interest can be specified based on the localized features of the solution, user interest, and correlation length of the material properties. For problems where a region of interest is not evident, Bayesian inference can provide a feasible solution. In this work, we employ a Bayesian framework to update the prior knowledge of the localized region of interest using measurements of the system response. Once, the region of interest is identified, the localized uncertainty is propagate forward through the computational domain. We demonstrate our framework using numerical experiments on a three-dimensional elastodynamic problem.

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