Pramudita Satria Palar

Comparative Study Between Different Strut’s Cross Section Shape on Minimizing Low Wall Shear Stress Along Stent Vicinity via Surrogate-Based Optimization

Endovascular stent has been employed to treat patients with intravascular diseases. Research on stent optimization is currently performed in order to find the best design in increasing the treatment efficacy. In this research, stent optimization is performed based on a finite element analysis method via Kriging surrogate model to observe the wall shear stress (WSS) conditions on the strut vicinity. Two configurations, rectangle and triangle are adopted as the cross section of a stent strut and compared to see the effects of the cross section on WSS condition. Strut gap in the range from 1 mm to 3 mm and the strut size length from 0.05 mm to 0.45 mm are considered as the design variables for each cross section. Structure contact simulation between stent and vessel wall is carried out to obtain the 5% vessel expansion. Afterward, computational fluid dynamics simulation is performed to analyze the hemodynamic effect of stent design along with wall deformation. Minimizing the percentage of low WSS area (WSS < 1 Pa) relative to the length of stent deployment area is set as the objective function of this optimization since low WSS is believed to promote some problems such as atherosclerosis. In total, 45 and 42 simulation iterations are conducted respectively for both cross sections to develop the Kriging surrogate models for efficient global optimization. Besides the prediction of the optimized configuration, broader observation on its behavior within the design range is also well predicted. The optimized configuration has 2.99 mm gap and 0.1 mm width for the rectangular strut, and 2.00 mm gap and 0.99 mm width for the triangular strut. The triangular strut has better performance in reducing the low WSS area with 14.6% of low WSS area on its optimized design, compared to 18.3% of the rectangular strut. Moreover, the triangular shape strut produces more stable performance; most design configuration with the strut width of less than 0.35 mm can keep low WSS area at the minimum value.

Global sensitivity analysis via multi-fidelity polynomial chaos expansion

The presence of uncertainties are inevitable in engineering design and analysis, where failure in understanding their effects might lead to the structural or functional failure of the systems. The role of global sensitivity analysis in this aspect is to quantify and rank the effects of input random variables and their combinations to the variance of the random output. In problems where the use of expensive computer simulations are required, metamodels are widely used to speed up the process of global sensitivity analysis. In this paper, a multi-fidelity framework for global sensitivity analysis using polynomial chaos expansion (PCE) is presented. The goal is to accelerate the computation of Sobol sensitivity indices when the deterministic simulation is expensive and simulations with multiple levels of fidelity are available. This is especially useful in cases where a partial differential equation solver computer code is utilized to solve engineering problems. The multi-fidelity PCE is constructed by combining the low-fidelity and correction PCE. Following this step, the Sobol indices are computed using this combined PCE. The PCE coefficients for both low-fidelity and correction PCE are computed with spectral projection technique and sparse grid integration. In order to demonstrate the capability of the proposed method for sensitivity analysis, several simulations are conducted. On the aerodynamic example, the multi-fidelity approach is able to obtain an accurate value of Sobol indices with 36.66% computational cost compared to the standard single-fidelity PCE for a nearly similar accuracy.

Stent design optimization based on kriging surrogate model under deformed vessel wall: Pulsatile inlet flow

The cardiovascular stent is one of the medical devices which has been commonly used for curing many vascular diseases. Nowadays, research on many aspects of stent development has been conducted to improve the devices efficacy. Mechanical and flow dynamics analysis on stent performance are useful to understand the impact of the device deployment. Many assumptions have been applied for constructing the stent simulation model including the blood vessel wall condition and its inlet flow conditions. Recently, common assumptions of the stent simulation model are mainly worked under the assumption of rigid wall condition and steady or pulsatile inlet flow. These different assumptions may lead to different simulation results. These differences may also affect the further analysis such as optimization process. This research tries to investigate the pulsatile effect on the stent optimization results based on computational simulation with wall deformation. Comparison with the previous optimization with a steady flow was conducted to find out about the differences between the two conditions. We found that the difference in optimization results from both inflow conditions is insignificant.

Multi-objective aerodynamic design with user preference using truncated expected hypervolume improvement

Multi-objective optimization in aerodynamic design plays an important role in capturing the trade-off and useful knowledge that would be useful for real-world design processes. In the preliminary design phase, aerodynamic designers usually have an interest in focusing the optimization process in a certain direction of interest. To this end, we propose the use of user preference multi-objective Bayesian global optimization (MOBGO) for aerodynamic design using truncated expected hypervolume improvement (TEHVI). Taking into account the apriori knowledge of objective functions, TEHVI acts as an infill criterion to search for the optimal solutions based on the Kriging models in MOBGO. In TEHVI-MOBGO, the first step is to obtain a coarse approximation of the Pareto front in order to capture the general trend and trade off using standard EHVI; following this step, TEHVI is then applied to focus the search on a defined region of interest. We demonstrate the capabilities and usefulness of TEHVI method on the design optimization of an inviscid transonic wing and a viscous transonic airfoil in order to minimize the drag coefficient and absolute value of pitching moment, which leads to a reduced fuel burn and easier control characteristic.

On efficient global optimization via universal Kriging surrogate models

In this paper, we investigate the capability of the universal Kriging (UK) model for single-objective global optimization applied within an efficient global optimization (EGO) framework. We implemented this combined UK-EGO framework and studied four variants of the UK methods, that is, a UK with a first-order polynomial, a UK with a second-order polynomial, a blind Kriging (BK) implementation from the ooDACE toolbox, and a polynomial-chaos Kriging (PCK) implementation. The UK-EGO framework with automatic trend function selection derived from the BK and PCK models works by building a UK surrogate model and then performing optimizations via expected improvement criteria on the Kriging model with the lowest leave-one-out cross-validation error. Next, we studied and compared the UK-EGO variants and standard EGO using five synthetic test functions and one aerodynamic problem. Our results show that the proper choice for the trend function through automatic feature selection can improve the optimization performance of UK-EGO relative to EGO. From our results, we found that PCK-EGO was the best variant, as it had more robust performance as compared to the rest of the UK-EGO schemes; however, total-order expansion should be used to generate the candidate trend function set for high-dimensional problems. Note that, for some test functions, the UK with predetermined polynomial trend functions performed better than that of BK and PCK, indicating that the use of automatic trend function selection does not always lead to the best quality solutions. We also found that although some variants of UK are not as globally accurate as the ordinary Kriging (OK), they can still identify better-optimized solutions due to the addition of the trend function, which helps the optimizer locate the global optimum.

Ensemble of Kriging with Multiple Kernel Functions for Engineering Design Optimization

We introduce the ensemble of Kriging with multiple kernel functions guided by cross-validation error for creating a robust and accurate surrogate model to handle engineering design problems. By using the ensemble of Kriging models, the resulting ensemble model preserves the uncertainty structure of Kriging, thus, can be further exploited for Bayesian optimization. The objective of this paper is to develop a Kriging methodology that eliminates the needs for manual kernel selection which might not be optimal for a specific application. Kriging models with three kernel functions, that is, Gaussian, Matern-3/2, and Matern-5/2 are combined through a global and a local ensemble technique where their approximation quality are investigated on a set of aerodynamic problems. Results show that the ensemble approaches are more robust in terms of accuracy and able to perform similarly to the best performing individual kernel function or avoiding misspecification of kernel.

Multiple Metamodels for Robustness Estimation in Multi-objective Robust Optimization

Due to the excessive cost of Monte Carlo simulation, metamodel is now frequently used to accelerate the process of robustness estimation. In this paper, we explore the use of multiple metamodels for robustness evaluation in multi-objective evolutionary robust optimization under parametric uncertainty. The concept is to build several different metamodel types, and employ cross-validation to pick the best metamodel or to create an ensemble of metamodels. Three types of metamodel were investigated: sparse polynomial chaos expansion PCE, Kriging, and 2

Exploiting active subspaces in global optimization: how complex is your problem?

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