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Mechanical Property of Hierarchically Porous PDMS according to Mixing Conditions
S. Lee(이상민),J. Lim(임종경),T. Yoon(윤태운),J. H. Bae(배재현) Korean Society for Precision Engineering 2021 한국정밀공학회 학술발표대회 논문집 Vol.2021 No.11월
A PDMS (Polydimethylsiloxane) material has been widely used in a lab-on-a-chip, an optical device, a wearable device, etc., because of chemical resistance, flexibility, high biocompatibility, and good optical transparency. When such a PDMS material is manufactured in a porous form, it can have a low density and a large surface area to volume ratio, so many studies are being actively conducted to apply it to an oil absorbent, a flexible pressure sensor with high sensitivity, and a scaffold in bio and tissue engineering field. In this study, a hierarchically porous PDMS using a sieved commercial salt (Sodium Chloride) was fabricated, and changes in mechanical properties of porous PDMS according to the size of salt particle (Adjusted according to Sieve Size) were analyzed. A test sample in the form of a cylinder was prepared according to the size of salt particle, the compressive modulus was measured, and the change of the elastic modulus was comparatively analyzed.
압축성 기저 유동에 대한 GEKO 모델 계수의 불확실성 정량화 연구
정영기(Y.K. Jung),장경식(K. Chang),배재현(J.H. Bae) 한국전산유체공학회 2021 한국전산유체공학회지 Vol.26 No.1
The present work, supersonic flow over an axisymmetric base is simulated using Generalized k-ω (GEKO) model which is proposed by Menter. GEKO two-equation model based on the k-ω formulation provides free model coefficients which can be adjusted by user depending on the specified flow type. Uncertainty Quantification analysis (UQ) is adopted to quantify the uncertainty of the model coefficients and to calibrate the coefficients for the flow. Latin Hypercube Sampling (LHS) method is used for sampling input parameters which are independent as a uniform distribution. Surrogate model is constructed by using ordinary least-squares (OLS). In order to characterize the posterior via Markov Chain Monte Carlo sampling, Affine Invariant Ensemble Algorithm (AIES) is selected. Through Forward problem, the most effective coefficient among the coefficients of GEKO model is figured out (Sensitivity analysis). Calibrated model coefficients are obtained through Bayesian inference. The results obtained using the calibrated coefficients by UQ to the base flow shows better agreement with available experimental measurements than the results obtained using default model coefficients.