• Journal of Korean Society for Atmospheric Environment
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• v.18 no.1
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• pp.59-65
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• 2002

The application of local air circulation models in the field of air pollution research has become more and more popular with increasing demands of detailed wind data for obtaining precise information on spatial and temporal variations. However the prediction of air circulation near the surface is generally not a simple task because of intricate interactions between surface and air. Particularly in Korea, many areas are mountainous with a complicated shoreline. Because considerable errors could be introduced into the model predictions, it is necessary to confirm their feasibility by comparing model predictions with observations. In this paper, the results from the evaluation of model predictions in selected publications in Korea as well as their procedures were reviewed. Various aspects of errors in the model predictions. such as possible sources, vulnerable conditions, and reduction methods, were discussed.

• Steel and Composite Structures
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• v.49 no.1
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• pp.109-123
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• 2023

Headed studs welded to steel beams and embedded within the concrete of deck slabs are vital components of modern composite floor systems, where safety and economy depend on the accurate predictions of the stud shear resistance. The multitude of existing deck profiles and the complex behavior of studs in deck slab ribs makes developing accurate and reliable mechanical or empirical design models challenging. The paper addresses this issue by presenting a machine learning (ML) model developed from the natural gradient boosting (NGBoost) algorithm capable of producing probabilistic predictions and a database of 464 push-out tests, which is considerably larger than the databases used for developing existing design models. The proposed model outperforms models based on other ML algorithms and existing descriptive equations, including those in EC4 and AISC 360, while offering probabilistic predictions unavailable from other models and producing higher shear resistances for many cases. The present study also showed that the stud shear resistance is insensitive to the concrete elastic modulus, stud welding type, location of slab reinforcement, and other parameters considered important by existing models. The NGBoost model was interpreted by evaluating the feature importance and dependence determined with the SHapley Additive exPlanations (SHAP) method. The model was calibrated via reliability analyses in accordance with the Eurocodes to ensure that its predictions meet the required reliability level and facilitate its use in design. An interactive open-source web application was created and deployed to the cloud to allow for convenient and rapid stud shear resistance predictions with the developed model.

• Transactions of the Korean Society of Mechanical Engineers
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• v.7 no.3
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• pp.328-334
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• 1983

The turbulent hydrogen-air diffussion flame was studied experimentally and theoretically. Laser Doppler anemometer was used to measure the velocity field in the flame. Two mathematical models for the combustion reaction term, which are infinite rate model and finite rate to be derived eddy break-up model, were tested by comparing predictions with experimental data for coaxial turbulent diffusion flame. The agreement between the predictions and the data is, on the whole, very good in the case of employing the finite rate model rather than the infinite rate model. But, it was shown that the finite rate model was practically applicable to the predictions of the turbulent diffussion flame structure.

• Wind and Structures
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• v.4 no.1
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• pp.31-44
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• 2001

Mean concentrations of ammonia gas released as a tracer from an isolated low-rise building have been measured and predicted. Predictions were calculated using computational fluid dynamics (CFD) and two dispersion models: a diffusion model and a Lagrangian particle tracking technique. Explicit account was taken of the natural variation of wind direction by a technique based on the weighted summation of individual steady state wind direction results according to the probability density function of the wind direction. The results indicated that at distances >3 building heights downstream the weighted predictions from either model are satisfactory but that in the near wake the diffusion model is less successful. Weighted solutions give significantly improved predictions over unweighted results. Lack of plume spread is identified as the main cause of inaccuracies in predictions and this is linked to inadequate resolution of flow features and mixing in the CFD model. Further work on non-steady state simulation of wake flows for dispersion studies is recommended.

• Wind and Structures
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• v.15 no.1
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• pp.43-64
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• 2012

Many potential small wind turbine locations are near obstacles such as buildings and shelterbelts, which can have a significant, detrimental effect on the local wind climate. A neural network-based model has been developed which predicts mean wind speed and turbulence intensity at points in an obstacle's region of influence, relative to unsheltered conditions. The neural network was trained using measurements collected in the wakes of 18 scale building models exposed to a simulated rural atmospheric boundary layer in a wind tunnel. The model obstacles covered a range of heights, widths, depths, and roof pitches typical of rural buildings. A field experiment was conducted using three unique full scale obstacles to validate model predictions and wind tunnel measurements. The accuracy of the neural network model varies with the quantity predicted and position in the obstacle wake. In general, predictions of mean velocity deficit in the far wake region are most accurate. The overall estimated mean uncertainties associated with model predictions of normalized mean wind speed and turbulence intensity are 4.9% and 12.8%, respectively.

• International Journal of Ocean System Engineering
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• v.3 no.3
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• pp.116-130
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• 2013

The prediction of ocean currents in real time over the warning times of a few hours or days is required in planning many operation-related activities in the ocean. Traditionally this is done through numerical models which are targeted toward producing spatially distributed information. This paper discusses a complementary method to do so when site-specific predictions are desired. It is based on the use of a recurrent type of neural network as well as the statistical tool of model tree. The measurements made at a site in Indian Ocean over a period of 4 years were used. The predictions were made over 72 time steps in advance. The models developed were found to be fairly accurate in terms of the selected error statistics. Among the two modeling techniques the model tree performed better showing the necessity of using distributed models for different sub-domains of data rather than a unique one over the entire input domain. Typically such predictions were associated with average errors of less than 2.0 cm/s. Although the prediction accuracy declined over longer intervals, it was still very satisfactory in terms of theselected error criteria. Similarly prediction of extreme values matched with that of the rest of predictions. Unlike past studies both east-west and north-south current components were predicted fairly well.

• Water Engineering Research
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• v.1 no.1
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• pp.25-37
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• 2000

This study presents some results of a preliminary study for the coupled precipitation and river flow prediction system. The model system in based on three numerical models, Mesoscale Atmospheric Simulation model for generating atmospheric variables. Soil-Plant-Snow model for computing interactions within soil-canopy-snow system as well as the energy and water exchange between the atmosphere and underlying surfaces, and TOPMODEL for simulating stream flow, subsurface flow, and water tabled depth in an watershed. The selected study area is the 2,703 $\alpha_4$ $\km_2$ Soyang River basin with outlet at Soyang dam site. In addition to providing the results of rainfall and stream flow predictions, some results of DEM and GIS application are presented. It is obvious that the accurate river flow predictions are highly dependant on the accurate predictation predictions.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2003.10a
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• pp.191-194
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• 2003

The results from a systematic study of the response of a Ti-6Al-4V alloy under quasi-static and dynamic loading at different strain rates and temperatures are presented. It has been shown that the work-hardening rate decreased as the strain rate and the strain increased. The correlations and predictions using modified KHL (Khan-Huang-Liang) viscoplastic constitutive model are compared with those from JC (Johnson-Cook) model and experimental observations. Overall, KHL model correlations and predictions compared much more favorably than the corresponding JC model predictions and correlations.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.39-42
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• 1996

Modeling techniques such as linear regression have been used to predict hurricane activity many months in advance of the start of the hurricane season with some success. In this paper, we construct feedforward neural networks to model Atlantic basin hurricane activity and compare the predictions of our neural network models to the predictions produced by statistical models found in the weather forecasting literature. We find that our neural network models produce reasonably accurate predictions that, for the most part, compare favorably to the predictions of statistical models.

• Computers and Concrete
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• v.25 no.5
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• pp.383-400
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• 2020

Due to higher stiffness to weight, higher corrosion resistance, higher strength to weight ratios and good durability, concrete composite structures provide many advantages as compared with conventional materials. Thus, they have wide applications in the field of concrete construction. This research focuses on the structural behavior of steel-tube CFRP confined concrete (STCCC) columns under axial concentric loading. A nonlinear finite element analysis (NLFEA) model of STCCC columns was simulated using ABAQUS which was then, calibrated for different material and geometric models of concrete, steel tube and CFRP material using the experimental results from the literature. The comparative study of the NLFEA predictions and the experimental results indicated that the proposed constitutive NLFEA model can accurately predict the structural performance of STCCC columns. After the calibration of NLFEA model, an extensive parametric study was performed to examine the effects of different critical parameters of composite columns such as; (i) unconfined concrete strength, (ii) number of CFRP layers, (iii) thickness of steel tube and (iv) concrete core diameter, on the axial load capacity. Furthermore, a large database of axial strength of 700 confined concrete compression members was developed from the previous researches to give an analytical model that predicts the ultimate axial strength of composite columns accurately. The comparison of the predictions of the proposed analytical model was done with the predictions of 216 NLFEA models from the parametric study. A close agreement was represented by the predictions of the proposed constitutive NLFEA model and the analytical model.