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.

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 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.

scholarly journals Design for Additive Manufacturing: Optimization of Piping Network in Compact System With Enhanced Path-Finding Approach

2018 ◽  
Vol 140 (8) ◽  
Author(s):  
Pei Cao ◽  
Zhaoyan Fan ◽  
Robert X. Gao ◽  
J. Tang
Keyword(s):  
Additive Manufacturing ◽  
Video Game ◽  
Optimal Path ◽  
Computational Cost ◽  
Design Tool ◽  
Computational Time ◽  
Path Finding ◽  
Network Systems ◽  
Efficient Manner ◽  
Visibility Graphs

This research aims at unleashing the potential of additive manufacturing technology in industrial design that can produce structures/devices with irregular component geometries to reduce sizes/weights. We explore, by means of path-finding, the length minimization of freeform hydraulic piping network in compact space under given constraints. Previous studies on path-finding have mainly focused on enhancing computational efficiency due to the need to produce rapid results in such as navigation and video-game applications. In this research, we develop a new Focal Any-Angle A* approach that combines the merits of grid-based method and visibility graph-based method. Specifically, we formulate pruned visibility graphs preserving only the useful portion of the vertices and then find the optimal path based on the candidate vertices using A*. The reduced visibility graphs enable us to outperform approximations and maintain the optimality of exact algorithms in a more efficient manner. The algorithm proposed is compared to the traditional A* on Grids, Theta* and A* on visibility graphs in terms of path length, number of nodes evaluated, as well as computational time. As demonstrated and validated through case studies, the proposed method is capable of finding the shortest path with tractable computational cost, which provides a viable design tool for the additive manufacturing of piping network systems.

A HIGH PERFORMANCE LARGE SPARSE SYMMETRIC SOLVER FOR THE MESHFREE GALERKIN METHOD

2008 ◽  
Vol 05 (04) ◽  
pp. 533-550 ◽  
Author(s):  
S. C. WU ◽  
H. O. ZHANG ◽  
C. ZHENG ◽  
J. H. ZHANG
Keyword(s):  
High Performance ◽  
Information Matrix ◽  
Computational Cost ◽  
Initial Boundary ◽  
Field Analysis ◽  
Iteration Step ◽  
Gradient Field ◽  
Computational Time ◽  
Preconditioned Conjugate Gradient ◽  
Memory Requirement

One main disadvantage of meshfree methods is that their memory requirement and computational cost are much higher than those of the usual finite element method (FEM). This paper presents an efficient and reliable solver for the large sparse symmetric positive definite (SPD) system resulting from the element-free Galerkin (EFG) approach. A compact mathematical model of heat transfer problems is first established using the EFG procedure. Based on the widely used Successive Over-Relaxation–Preconditioned Conjugate Gradient (SSOR–PCG) scheme, a novel solver named FastPCG is then proposed for solving the SPD linear system. To decrease the computational time in each iteration step, a new algorithm for realizing multiplication of the global stiffness matrix by a vector is presented for this solver. The global matrix and load vector are changed in accordance with a special rule and, in this way, a large account of calculation is avoided on the premise of not decreasing the solution's accuracy. In addition, a double data structure is designed to tackle frequent and unexpected operations of adding or removing nodes in problems of dynamic adaptive or moving high-gradient field analysis. An information matrix is also built to avoid drastic transformation of the coefficient matrix caused by the initial-boundary values. Numerical results show that the memory requirement of the FastPCG solver is only one-third of that of the well-developed AGGJE solver, and the computational cost is comparable with the traditional method with the increas of solution scale and order.

A Kriging surrogate model assisted Tabu search method for electromagnetic inverse problems

2020 ◽  
Vol 64 (1-4) ◽  
pp. 351-358
Author(s):  
Siguang An ◽  
Qiang Deng ◽  
Tianwei Wu ◽  
Shiyou Yang ◽  
Nanying Shentu
Keyword(s):  
Inverse Problems ◽  
Tabu Search ◽  
Surrogate Model ◽  
Optimal Solution ◽  
Search Method ◽  
Computational Time ◽  
Mathematical Function ◽  
Kriging Surrogate Model ◽  
Variable Space ◽  
Tabu Search Method

To balance the efficiency and accuracy of a global optimization algorithm in solving electromagnetic inverse problems, a Tabu search method assisted by using a Kriging surrogate model is proposed. To reduce the computational time and speed up the algorithm, the Kriging surrogate model is used to predict the objective space. To ensure the accuracy of the final optimal solution, a united trigger is developed to realize dynamically switching between the prediction and the direct objective computation. To utilize the variable space efficiently and provide proper sampling points to update the Kriging surrogate model, an evaluation list is used to evaluate the variable space. A typical mathematical function and electromagnetic inverse problems in low and high frequency are solved to testify the correctness and effectiveness of the proposed method.

An Advanced and Robust Ensemble Surrogate Model: Extended Adaptive Hybrid Functions

2018 ◽  
Vol 140 (4) ◽  
Author(s):  
Xueguan Song ◽  
Liye Lv ◽  
Jieling Li ◽  
Wei Sun ◽  
Jie Zhang
Keyword(s):  
Surrogate Model ◽  
Surrogate Models ◽  
Computational Time ◽  
Test Problems ◽  
Large Set ◽  
Hybrid Functions ◽  
Adaptive Weight ◽  
Process Prediction ◽  
The Individual ◽  
Leave One Out

Hybrid or ensemble surrogate models developed in recent years have shown a better accuracy compared to individual surrogate models. However, it is still challenging for hybrid surrogate models to always meet the accuracy, robustness, and efficiency requirements for many specific problems. In this paper, an advanced hybrid surrogate model, namely, extended adaptive hybrid functions (E-AHF), is developed, which consists of two major components. The first part automatically filters out the poorly performing individual models and remains the appropriate ones based on the leave-one-out (LOO) cross-validation (CV) error. The second part calculates the adaptive weight factors for each individual surrogate model based on the baseline model and the estimated mean square error in a Gaussian process prediction. A large set of numerical experiments consisting of up to 40 test problems from one dimension to 16 dimensions are used to verify the accuracy and robustness of the proposed model. The results show that both the accuracy and the robustness of E-AHF have been remarkably improved compared with the individual surrogate models and multiple benchmark hybrid surrogate models. The computational time of E-AHF has also been considerately reduced compared with other hybrid models.

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.

Integrated HPC OGV and Pre-Diffuser Design Optimization Using Kriging Surrogate Model

2013 ◽  
Author(s):  
Hongtao Wang ◽  
Weiliang Xie ◽  
Meining Chen
Keyword(s):  
Design Optimization ◽  
Boundary Layers ◽  
Surrogate Model ◽  
High Performance ◽  
Guide Vane ◽  
Thickness Distribution ◽  
Adverse Pressure Gradient ◽  
Optimization Process ◽  
Expected Improvement ◽  
Kriging Surrogate Model

The integration of high compressor outlet guide vane (OGV) and combustor pre-diffuser requires some radial turning to be performed within the OGV passage. However, the enhanced loading of OGV leads to the increase in adverse pressure gradient within the OGV passage. Consequently, both the end-wall and blade boundary layers are thickened which could lead to boundary layers separation. In this work, an adaptive global optimization process is applied for the OGV/pre-diffuser system, which combines design of experiment (DOE), Kriging surrogate model and micro genetic algorithm. The meridional flow passage of OGV/pre-diffuser system is parameterized using Bezier curves with the combination of mean line and thickness distribution. In order to prevent the OGV corner separation, the bowed design is applied to the OGV to help delay flow separation. A composite curve combined with two straight lines and a conic Bezier curve is used to represent the OGV stacking line along circumference so that the bowed blades could be parameterized. Aerodynamic performance evaluations of the compressor are performed using a three dimensional Reynolds-averaged Navier-stokes computational fluid dynamics solver — NUMECA. In the optimization process, expected improvement sample criteria is adopted for balancing the exploration and exploitation with Kriging surrogate model. Reasonably high performance is confirmed by comparing the baseline and optimal designs. This study gives some insights into design optimization of an integrated OGV/Pre-diffuser for axial compressor.

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.

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