• Composites Research
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• v.36 no.3
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• pp.205-210
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• 2023

In this study, we developed a transfer learning framework based on homogenization data for efficient prediction of the effective mechanical properties and thermal conductivity of cellular foam structures. Mean-field homogenization (MFH) based on the Eshelby's tensor allows for efficient prediction of properties in porous structures including ellipsoidal inclusions, but accurately predicting the properties of cellular foam structures is challenging. On the other hand, finite element homogenization (FEH) is more accurate but comes with relatively high computational cost. In this paper, we propose a data-driven transfer learning framework that combines the advantages of mean-field homogenization and finite element homogenization. Specifically, we generate a large amount of mean-field homogenization data to build a pre-trained model, and then fine-tune it using a relatively small amount of finite element homogenization data. Numerical examples were conducted to validate the proposed framework and verify the accuracy of the analysis. The results of this study are expected to be applicable to the analysis of materials with various foam structures.

• Journal of Ocean Engineering and Technology
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• v.27 no.4
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• pp.14-21
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• 2013

This study examined the field application of a 3-D numerical model (LES-WASS-3D) to the estimation of the nearshore current at Songdo beach, Busan. The wave and tide conditions observed at Songdo beach during Typhoon Ewiniar (July 10, 2006) were used in a numerical simulation. The numerical wave heights were in good agreement with the field data. The spatial distributions of the wave heights, mean water levels, and mean flows obtained from the numerical simulation are discussed in relation to the bottom topographical change near Songdo beach before and after Typhoon Ewiniar. The results revealed that LES-WASS-3D is a powerful tool for estimating the nearshore current in the field.

• Smart Structures and Systems
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• v.15 no.2
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• pp.489-503
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• 2015

Response estimation at unmeasured locations using the limited number of measurements is an attractive topic in the field of structural health monitoring (SHM). Because of increasing complexity and size of civil engineering structures, measuring all structural responses from the entire body is intractable for the SHM purpose; the response estimation can be an effective and practical alternative. This paper investigates a response estimation technique based on the Kalman state estimator to combine multi-sensor data under non-zero mean input excitations. The Kalman state estimator, constructed based on the finite element (FE) model of a structure, can efficiently fuse different types of data of acceleration, strain, and tilt responses, minimizing the intrinsic measurement noise. This study focuses on the effects of (a) FE model error and (b) combinations of multi-sensor data on the estimation accuracy in the case of non-zero mean input excitations. The FE model error is purposefully introduced for more realistic performance evaluation of the response estimation using the Kalman state estimator. In addition, four types of measurement combinations are explored in the response estimation: strain only, acceleration only, acceleration and strain, and acceleration and tilt. The performance of the response estimation approach is verified by numerical and experimental tests on a simply-supported beam, showing that it can successfully estimate strain responses at unmeasured locations with the highest performance in the combination of acceleration and tilt.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.23 no.1
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• pp.49-62
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• 2020

This study was conducted as a basic study to develop the HSI(Habitat Suitability Index) model of Pelophylax chosenicus based on the research on the ecological and habitat status of Pelophylax chosenicus and the literature research on the HSI model. The habitat variables of Pelophylax chosenicus are the altitude of the spawning pond, the habitat area, the distance from wetland, the soil(aptitude grade for paddy field), the place for eating such as paddy field and wetlands(land cover) and the distance from Predator(Lithobates catesbeianus) distribution area. Based on the existing literature of Pelophylax chosenicus, the results of field surveys and expert opinions, the SI(Suitability Index) model and HSI model were developed and applied to the site to examine the applicability of the HSI model. As a result of application, SI 4 and SI 5 with varying SI values seem to have a major influence on the HSI. In addition, it is considered that the HSI model is an arithmetic mean of SI models, which has a major impact on HSI. The HSI model can be an important basis for the habitat evaluation and restoration model of Pelophylax chosenicus. In particular, it is highly applicable to the selection and evaluation of alternative habitats for Pelophylax chosenicus.

• Journal of computational fluids engineering
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• v.2 no.2
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• pp.9-17
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• 1997

The performance of several turbulence models in computing an axisymmetric supersonic base flow is investigated. A compressible Navier-Stokes code, which incorporates k-ε, k-ω model and Reynolds stress closure with three kinds of pressure-strain correlation model, has been developed using implicit LU-SGS algorithm with second-order upwind TVD scheme. Numerical computations have been carried out for Herrin and Dutton's base flow. It is observed that the two-equation models give large backward axial velocity approaching to the base and somewhat larger variation of base pressure distribution than the Reynolds stress model. It is also found that the Reynolds stress model with third order pressure-strain model in the anisotropy tensor predicts most accurate mean flow field.

• Transactions of Materials Processing
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• v.22 no.8
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• pp.450-455
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• 2013

In the current study, a new dynamic recrystallization model for predicting high temperature flow stress is developed based on a physical model and the mean field theory. In the model, the grain aggregate is assumed as a representative volume element to describe dynamic recrystallization. The flow stress and microstructure during dynamic recrystallization were calculated using three sub-models for work hardening, for nucleation and for growth. In the case of work hardening, a single parameter dislocation density model was used to calculate change of dislocation density and stress in the grains. For modeling nucleation, the nucleation criterion developed was based on the grain boundary bulge mechanism and a constant nucleation rate was assumed. Conventional rate theory was used for describing growth. The flow stress behavior of pure copper was investigated using the model and compared with experimental findings. Simulated results by cellular automata were used for validating the model.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.8 no.3
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• pp.375-385
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• 1996

Turbulent flow and heat transfer characteristics around staggered tube banks were studied using the 3-D Navier-Stokes equations and energy equation governing a steady incompressible flow, which were reformulated in a non-orthogonal coordinate system with cartesian velocity components and discretized by the finite volume method with a non-staggered variable arrangement. The predicted turbulent kinetic energy using RNG $k-{\varepsilon}$ model was lower than that of standard $k-{\varepsilon}$ model but showed same result for mean flow field quantities. The prediction of the skin friction coefficient using RNG $k-{\varepsilon}$ model showed better trend with experimental data than standard $k-{\varepsilon}$ model result. The inclined flow showed higher velocity and skin friction coefficient than transverse flow because of extra strain rate ($\frac{{\partial}w}{{\partial}y}$). Also, this was why the inclined flow showed higher local heat transfer coefficient than the transverse flow.

• Wind and Structures
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• v.31 no.3
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• pp.269-285
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• 2020

The statistical characteristics of typhoon wind speed records tend to have a considerable time-varying trend; thus, the stationary wind model may not be appropriate to estimate the wind characteristics of typhoon events. Several nonstationary wind speed models have been proposed by pioneers to characterize wind characteristics more accurately, but comparative studies on the applicability of the different wind models are still lacking. In this study, three landfall typhoons, Ampil, Jongdari, and Rumbia, recorded by ultrasonic anemometers atop the Shanghai World Financial Center (SWFC), are used for the comparative analysis of stationary and nonstationary wind characteristics. The time-varying mean is extracted with the discrete wavelet transform (DWT) method, and the time-varying standard deviation is calculated by the autoregressive moving average generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model. After extracting the time-varying trend, the longitudinal wind characteristics, e.g., the probability distribution, power spectral density (PSD), turbulence integral scale, turbulence intensity, gust factor, and peak factor, are comparatively analyzed based on the stationary wind speed model, time-varying mean wind speed model and time-varying standard deviation wind speed model. The comparative analysis of the different wind models emphasizes the significance of the nonstationary considerations in typhoon events. The time-varying standard deviation model can better identify the similarities among the different typhoons and appropriately describe the nonstationary wind characteristics of the typhoons.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.249-259
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• 2008

The present paper studies the effect of the slope gradient of a fully permeable submerged breakwater using a newly developed numerical model that is able to consider the flow through a porous midium with inertial, laminar and turbulent resistance terms, i.e. simulate directly WAve-Structure (submerged breakwater)-Sand seabed interaction and can determine the eddy viscosity with LES turbulence model in 2-Dimensional wave field (LES-WASS-2D). The developed model was validated through the comparison with an existing experimental data, and further used for various numerical experiments in oder to investigate the complicated hydrodynamics on the varying slope gradient of permeable submerged breakwater. We found an acceptable phenomenon, as we expect intuitively, that reflection and transmission coefficients decrease simultaneously as slope gradient decrease. In addition, the breaking point, the circulation flow and mean vorticity around a submerged breakwater are throughly discussed.

• Structural Engineering and Mechanics
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• v.91 no.5
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• pp.443-457
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• 2024

At present, the traditional concrete surface inspection methods based on artificial vision have the problems of high cost and insecurity, while the computer vision methods rely on artificial selection features in the case of sensitive environmental changes and difficult promotion. In order to solve these problems, this paper introduces deep learning technology in the field of computer vision to achieve automatic feature extraction of structural damage, with excellent detection speed and strong generalization ability. The main contents of this study are as follows: (1) A method based on DeepLabV3+ convolutional neural network model is proposed for surface detection of post-earthquake structural damage, including surface damage such as concrete cracks, spaling and exposed steel bars. The key semantic information is extracted by different backbone networks, and the data sets containing various surface damage are trained, tested and evaluated. The intersection ratios of 54.4%, 44.2%, and 89.9% in the test set demonstrate the network's capability to accurately identify different types of structural surface damages in pixel-level segmentation, highlighting its effectiveness in varied testing scenarios. (2) A semantic segmentation model based on DeepLabV3+ convolutional neural network is proposed for the detection and evaluation of post-earthquake structural components. Using a dataset that includes building structural components and their damage degrees for training, testing, and evaluation, semantic segmentation detection accuracies were recorded at 98.5% and 56.9%. To provide a comprehensive assessment that considers both false positives and false negatives, the Mean Intersection over Union (Mean IoU) was employed as the primary evaluation metric. This choice ensures that the network's performance in detecting and evaluating pixel-level damage in post-earthquake structural components is evaluated uniformly across all experiments. By incorporating deep learning technology, this study not only offers an innovative solution for accurately identifying post-earthquake damage in civil engineering structures but also contributes significantly to empirical research in automated detection and evaluation within the field of structural health monitoring.