scholarly journals Design Optimization of Coronary Stent Based on Finite Element Models

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Hongxia Li ◽  
Tianshuang Qiu ◽  
Bao Zhu ◽  
Jinying Wu ◽  
Xicheng Wang
Keyword(s):  
Finite Element ◽  
Surrogate Model ◽  
Optimization Method ◽  
Expected Improvement ◽  
Finite Element Models ◽  
Rectangular Grid ◽  
Kriging Surrogate Model ◽  
Expansion Process ◽  
Grid Sampling ◽  
Expansion Behavior

This paper presents an effective optimization method using the Kriging surrogate model combing with modified rectangular grid sampling to reduce the stent dogboning effect in the expansion process. An infilling sampling criterion named expected improvement (EI) is used to balance local and global searches in the optimization iteration. Four commonly used finite element models of stent dilation were used to investigate stent dogboning rate. Thrombosis models of three typical shapes are built to test the effectiveness of optimization results. Numerical results show that two finite element models dilated by pressure applied inside the balloon are available, one of which with the artery and plaque can give an optimal stent with better expansion behavior, while the artery and plaque unincluded model is more efficient and takes a smaller amount of computation.

scholarly journals Applying models of spatial interpolation for the approximation of responses in the refinement of parameters of finite element models

2018 ◽  
Vol 212 ◽  
pp. 01021
Author(s):  
Anatoly Pikhalov ◽  
Anton Zabelin
Keyword(s):  
Finite Element ◽  
Radial Basis Functions ◽  
Element Model ◽  
Optimization Method ◽  
Latin Squares ◽  
Basis Functions ◽  
Finite Element Models ◽  
Radial Basis ◽  
High Level ◽  
Parameter Values

The numerical experiment on refining the parameters of the finite element model of the beam by the method of approximating the responses is presented in the article. As mathematical models of joint-stock companies are used: linear combinations of radial-basis functions, and Kriging-models. These models are generated in the work on the basis of Latin squares and depend on the parameters to be refined (the moduli of elasticity of finite element groups of the beam). To obtain optimal values of the parameters, a genetic optimization method was used. The results of solving the optimization problem showed a high level of coincidence of the parameter values with a combination of response models obtained from dynamic and static types of calculations. It was also shown that when solving the problems of finite element models, it is sufficient to use models constructed only on the basis of radial-basis functions.

scholarly journals Drug Release Analysis and Optimization for Drug-Eluting Stents

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Hongxia Li ◽  
Yihao Zhang ◽  
Bao Zhu ◽  
Jinying Wu ◽  
Xicheng Wang
Keyword(s):  
Drug Release ◽  
Surrogate Model ◽  
Expected Improvement ◽  
Drug Eluting Stents ◽  
Optimization Approach ◽  
Optimized Design ◽  
Kriging Surrogate Model ◽  
Adaptive Optimization ◽  
Release Analysis ◽  
Drug Eluting

The drug release analysis and optimization for drug-eluting stents in the arterial wall are studied, which involves mechanics, fluid dynamics, and mass transfer processes and design optimization. The Finite Element Method (FEM) is used to analyze the process of drug release in the vessels for drug-eluting stents (DES). Kriging surrogate model is used to build an approximate function relationship between the drug distribution and the coating parameters, replacing the expensive FEM reanalysis of drug release for DES in the optimization process. The diffusion coefficients and the coating thickness are selected as design variables. An adaptive optimization approach based on kriging surrogate model is proposed to optimize the lifetime of the drug in artery wall. The adaptive process is implemented by an infilling sampling criterion named Expected Improvement (EI), which is used to balance local and global search and tends to find the global optimal design. The effect of coating diffusivity and thickness on the drug release process for a typical DES is analyzed by means of FEM. An implementation of the optimization method for the drug release is then discussed. The results demonstrate that the optimized design can efficiently improve the efficacy of drug deposition and penetration into the arterial walls.

OPTIMUM EXPANSION AND RESIDUAL CONTACT PRESSURE LEVELS OF HYDRAULICALLY EXPANDED TUBE-TO-TUBESHEET JOINTS

1997 ◽  
Vol 21 (4) ◽  
pp. 415-434 ◽  
Author(s):  
M. Allam ◽  
A. Chaaban ◽  
A. Bazergui
Keyword(s):  
Finite Element ◽  
Residual Stresses ◽  
Contact Pressure ◽  
Heat Exchangers ◽  
Statistical Technique ◽  
Triangular Array ◽  
Finite Element Models ◽  
Steam Generators ◽  
Expansion Process ◽  
Expansion Pressure

Tube-to-tubesheet joints in steam generators and other heat exchangers are now often assembled by means of a hydraulic expansion process that plastically deforms the tubes against the tubesheet and thus creates an interference residual contact pressure between the tube and the tubesheet as well as tensile residual stresses in the tube. A good understanding of both the residual contact pressure and the residual stresses is important for establishing the integrity of the expanded joint. The propose of this paper is to investigate the effect of the level of the expansion pressure on both the residual contact pressure an the maximum tensile residual stresses. A comparison between the 3-D and the axisymmetric Finite Element models for a triangular array tube patterns is presented. An analytical equation is proposed for determining the optimum expansion pressure that provides an acceptable level of residual contact pressure and maximum tensile residual stresses. A statistical technique is also carried out to confirm the validity of the proposed approach.

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.

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 Kernel Parameter Optimization for Kriging Based on Structural Risk Minimization Principle

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Hua Su ◽  
Chunlin Gong ◽  
Liangxian Gu
Keyword(s):  
Parameter Optimization ◽  
Surrogate Model ◽  
Optimization Method ◽  
Nonuniform Distribution ◽  
Risk Minimization ◽  
Structural Risk Minimization ◽  
Kriging Surrogate Model ◽  
Generalization Ability ◽  
Kernel Parameter ◽  
Structural Risk

An improved kernel parameter optimization method based on Structural Risk Minimization (SRM) principle is proposed to enhance the generalization ability of traditional Kriging surrogate model. This article first analyses the importance of the generalization ability as an assessment criteria of surrogate model from the perspective of statistics and proves the applicability to Kriging. Kernel parameter optimization method is used to improve the fitting precision of Kriging model. With the smoothness measure of the generalization ability and the anisotropy kernel function, the modified Kriging surrogate model and its analysis process are established. Several benchmarks are tested to verify the effectiveness of the modified method under two different sampling states: uniform distribution and nonuniform distribution. The results show that the proposed Kriging has better generalization ability and adaptability, especially for nonuniform distribution sampling.

Uncertainty Updating of Finite Element Models Using Interval Analysis

2020 ◽  
Vol 20 (13) ◽  
pp. 2041012
Author(s):  
Deshan Shan ◽  
Y. H. Chai ◽  
Hao Dong ◽  
Zhonghui Li
Keyword(s):  
Finite Element ◽  
Response Surface ◽  
Interval Analysis ◽  
Surrogate Model ◽  
Structural Parameters ◽  
Natural Extension ◽  
Model Parameters ◽  
Finite Element Models ◽  
Interaction Terms ◽  
Response Surface Function

Uncertainties in structural parameters and measurements can be accounted for by incorporating interval analysis into the updating scheme of finite element models using a response-surface function. To facilitate the interval arithmetic operation, two different strategies are proposed in this paper to transform the response-surface function into a corresponding interval response-surface function. These strategies minimize the inherent interval overestimation that can arise from the variable dependency of the surrogate model. In the first strategy, the natural extension and centered-form extension methods are used to mitigate the interval overestimation of the surrogate model, which may or may not contain interaction terms. In the second strategy, the natural extensión method is also adopted to realize the interval transformation of the surrogate model containing interaction terms but an affine arithmetic is further introduced to minimize the interval overestimation. To demonstrate the efficacy of the proposed method, model parameters are determined from an instrumented model of a cable-stayed bridge tested on a shaking table. Results show that the proposed updating method is feasible and effective for applications to finite element models of complex bridge structures.

scholarly journals A Kriging Surrogate Model for Uncertainty Analysis of Graphene Based on a Finite Element Method

2019 ◽  
Vol 20 (9) ◽  
pp. 2355 ◽  
Author(s):  
Jiajia Shi ◽  
Liu Chu ◽  
Robin Braun
Keyword(s):  
Finite Element ◽  
Uncertainty Analysis ◽  
Surrogate Model ◽  
Sampling Method ◽  
Latin Hypercube Sampling ◽  
Element Model ◽  
Graphene Sheets ◽  
Kriging Surrogate Model ◽  
Random Defects ◽  
The Finite Element Model

Due to the inevitable presence of random defects, unpredictable grain boundaries in macroscopic samples, stress concentration at clamping points, and unknown load distribution in the investigation of graphene sheets, uncertainties are crucial and challenging issues that require more exploration. The application of the Kriging surrogate model in vibration analysis of graphene sheets is proposed in this study. The Latin hypercube sampling method effectively propagates the uncertainties in geometrical and material properties of the finite element model. The accuracy and convergence of the Kriging surrogate model are confirmed by a comparison with the reported references. The uncertainty analysis for both Zigzag and Armchair graphene sheets are compared and discussed.

Optimization of Balloon-Stent System Based on Nonlinear Material Using Kriging Surrogate Model

2012 ◽  
Vol 502 ◽  
pp. 463-468
Author(s):  
Hong Xia Li ◽  
Xi Cheng Wang
Keyword(s):  
Surrogate Model ◽  
Design Space ◽  
Optimization Method ◽  
Nonlinear Material ◽  
Kriging Surrogate Model ◽  
Adaptive Optimization ◽  
Model Combining ◽  
Stent System ◽  
Global Optimal ◽  
The Relationship

Computer-aided technology was used for balloon-stent system design. Nonlinear material was used to simulate the dilation of balloon-stent system. Based on finite element results, an adaptive optimization method based on the kriging surrogate model combining with LHS approach and EI function was employed for the optimization of balloon length to reduce stent dogboning effect during its dilation. The kriging surrogate model can approximate the relationship between dogboning rate and balloon length, replacing the expensive reanalysis of the stent dilation. Sample points from LHS can represent the information of all parts on the design space. EI function is used to balance local and global search, and tends to find the global optimal design. Numerical results demonstrate that this adaptive optimization methed based on kriging surrogate model can be used for the optimization of balloon length of balloon-stent system.

Sign in / Sign up

Export Citation Format

Share Document