Jaehyeok Doh

Reliability assessment on the degradation properties of polymers under operating temperature and vibration conditions

This study focuses on the design of polymer components considering their degradation under designed operating conditions in automobiles. We use stochastic and statistical methods to ensure that such components are reliable and robust. The behaviours of polymers are described using a viscoelastic model, and degradation properties of polymers are obtained from creep and tensile data that are acquired at various temperatures. Using the Maxwell fluid model, we calculate the Prony series, which estimates viscoelastic models based on creep data. By considering Prony coefficients that describe degradation characteristics, this approach generates stress data via a frequency-response analysis of polymer components in automobiles. These data are used to generate performance functions by the response surface method. We assess the reliability considering the variation of temperature-dependent degradation properties and the areas of the peak frequency. In this study, degraded properties and frequencies are assumed to have a normal distribution, and we evaluate the reliability and probability of failure under the yield strength criteria using a Monte Carlo simulation. We then compare the reliability and failure probabilities of the given polymers in an automotive component. Based on these comparisons, we suggest the most suitable polymeric materials for use in automotive applications.

Reliability-based robust design optimization of gap size of annular nuclear fuels using kriging and inverse distance weighting methods

ABSTRACT In this study, the design optimization of the gap size of annular nuclear fuels used in pressurized water reactors (PWRs) was performed. For this, thermoelastic–plasticity–creep (TEPC) analysis of PWR annular fuels was carried out using an in-house code to investigate the performance of nuclear fuels. Surrogate models based on the kriging and inverse distance weighting models were generated using computational performance data based on optimal Latin hypercube design. Using these surrogate models, the gap size of PWR annular fuel was deterministically optimized using the micro-genetic algorithm to improve the heat transfer efficiency and maintain a lower level of stress. Reliability-based design optimization and reliability-based robust design optimization were conducted to satisfy target reliability and secure the robustness of the PWRs’ performance. The optimal gap size was validated through TEPC analysis and the optimum solutions were compared according to the approximate method and reliability index.

Bayesian estimation of the lethargy coefficient for probabilistic fatigue life model

Abstract In this study, a model for probabilistic fatigue life that is based on the Zhurkov model is suggested using stochastically and statistically estimated lethargy coefficients. The fatigue life model was derived using the Zhurkov life model, and it was deterministically validated using real fatigue life data as a reference. For this process, firstly, a lethargy coefficient that is related to the failure of materials must be obtained with rupture time and stress from a quasi-static tensile test. These experiments are performed using HS40R steel. However, the lethargy coefficient has discrepancies due to the inherent uncertainty and the variation of material properties in the experiments. The Bayesian approach was employed for estimating the lethargy coefficient of the fatigue life model using the Markov Chain Monte Carlo (MCMC) sampling method and considering its uncertainties. Once the samples are obtained, one can proceed to the posterior predictive inference of the fatigue life. This life model was shown to be reasonable when compared with experimental fatigue life data. As a result, predicted fatigue life was observed to significantly decrease in accordance with increasing relative stress conditions.

Determination of the best distribution and effective interval using statistical characterization of uncertain variables

Abstract In this paper, an algorithm for estimating the best distribution about data containing uncertainties is proposed. The proposed algorithm combines sequence statistical modeling (SSM) and a method for determining the minimum experimental data. SSM is a method for selecting the best distribution about data using a goodness of fit (GoF) test and a comparison of the likelihood. The method used to determine the minimum experimental data determines the minimum data needed to an estimate the best distribution. The SSM presented herein is a method for selecting a suitable data distribution when considering only a parametric distribution. Thus, in this paper, the SSM was improved in order to select the correct distribution of both parametric and non-parametric distributions simultaneously. In addition, with the existing method for determining the minimum data, the data should be added based on actual experiments when the results data show an insufficient number, and there is a limitation in that the designers cannot broadly identify the data required. To overcome this limitation, SSM and random sampling are applied to the method to determine the minimum data, and thereby, ensure that the designer knows the approximate minimum data needed. To verify the validity of the proposed algorithm, it was applied to a real world case study on determining multiple statistical parameters in the bolt fastening problem. The sequence of verification methods used is as follows: First, the best distribution of the bearing surface and thread friction coefficient estimated by the proposed algorithm and based on a normal distribution are selected as comparison targets. Second, the bearing surface and thread friction coefficient data are sampled within the 95% confidence interval of the two distributions. Third, the reliability of the sampled friction coefficient data are compared using a Monte-Carlo simulation and an equation to calculate the bolt fastening force. In this study, the effectiveness of the proposed algorithm is validated.

Approximate Multi-Objective Optimization of A Wall-mounted Monitor Bracket Arm Considering Strength Design Conditions

In this study, an approximate multi-objective optimization of a wall-mounted monitor bracket arm was performed. The rotation angle of the bracket arm was determined considering the inplane degree of freedom. We then formulated an optimization problem on maximum stress and deflection. Analyses of mean and design parameters were conducted for sensitivity regarding performance with orthogonal array and response surface method (RSM). RSM models of objective and constraint functions were generated using central composite (CCD) and D-optimal design. The accuracy of approximate models was evaluated through value. The obtained optimal solutions by non-dominant sorting genetic algorithm (NSGA-II) were validated through the finite element analysis and we compared the obtained optimal solution by CCD and D-optimal design.

Approximate Multi-Objective Optimization of Gap Size of PWR Annular Nuclear Fuels

In this study, we conducted the approximate multi-objective optimization of gap sizes of pressurized-water reactor (PWR) annular fuels. To determine the contacting tendency of the inner-outer gaps between the annular fuel pellets and cladding, thermoelastic-plastic-creep (TEPC)analysis of PWR annular fuels was performed, using in-house FE code. For the efficient heat transfer at certain levels of stress, we investigated the tensile, compressive hoop stress and temperature, and optimized the gap sizes using the non-dominant sorting genetic algorithm (NSGA-II). For this, response surface models of objective and constraint functions were generated, using central composite (CCD) and D-optimal design. The accuracy of approximate models was evaluated through value. The obtained optimal solutions by NSGA-II were verified through the TEPC analysis, and we compared the obtained optimum solutions and generated errors from the CCD and D-optimal design. We observed that optimum solutions differ, according to design of experiments (DOE) method.