• Ocean Systems Engineering
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• v.12 no.1
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• pp.63-86
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• 2022

A refined projection-based purely Lagrangian meshfree method is presented towards reliable numerical analysis of fluid flow interactions with saturated/unsaturated porous media of uniform/spatially-varying porosities. The governing equations are reformulated on the basis of two-phase mixture theory with incorporation of volume fraction. These principal equations of mixture are discretized in the context of Incompressible SPH (Smoothed Particle Hydrodynamics) method. Associated with the consideration of governing equations of mixture, a new term arises in the source term of PPE (Poisson Pressure Equation), resulting in modified source term. The linear and nonlinear force terms are included in momentum equation to represent the resistance from porous media. Volume increase of fluid particles are taken into consideration on account of the presence of porous media, and hence multi-resolution ISPH framework is also incorporated. The stability and accuracy of the proposed method are thoroughly examined by reproducing several numerical examples including the interactions between fluid flow and saturated/unsaturated porous media of uniform/spatially-varying porosities. The method shows continuous pressure field, smooth variations of particle volumes and regular distributions of particles at the interface between fluid and porous media.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.156-156
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• 2022

Explicitly spatially distributed and reliable data on industrial water demand is very much important for both policy makers and researchers in order to carry a region-specific analysis of water resources management. However, such type of data remains scarce particularly in underdeveloped and developing countries. Current research is limited in using different spatially available socio-economic, climate data and geographical data from different sources in accordance to predict industrial water demand at finer resolution. This study proposes a random forest regression (RFR) model to predict the industrial water demand at 0.50× 0.50 spatial resolution by combining various features extracted from multiple data sources. The dataset used here include National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) night-time light (NTL), Global Power Plant database, AQUASTAT country-wise industrial water use data, Elevation data, Gross Domestic Product (GDP), Road density, Crop land, Population, Precipitation, Temperature, and Aridity. Compared with traditional regression algorithms, RF shows the advantages of high prediction accuracy, not requiring assumptions of a prior probability distribution, and the capacity to analyses variable importance. The final RF model was fitted using the parameter settings of ntree = 300 and mtry = 2. As a result, determinate coefficients value of 0.547 is achieved. The variable importance of the independent variables e.g. night light data, elevation data, GDP and population data used in the training purpose of RF model plays the major role in predicting the industrial water demand.

• Geomechanics and Engineering
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• v.3 no.3
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• pp.169-188
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• 2011

Response of buried flexible pipe-soil system is studied, through numerical analysis, with respect to deflection and buckling in a spatially varying soil media. In numerical modeling procedure, soil parameters are modeled as two-dimensional non-Gaussian homogeneous random field using Cholesky decomposition technique. Numerical analysis is performed using random field theory combined with finite difference numerical code FLAC 5.0 (2D). Monte Carlo simulations are performed to obtain the statistics, i.e., mean and variance of deflection and circumferential (buckling) stresses of buried flexible pipe-soil system in a spatially varying soil media. Results are compared and discussed in the light of available analytical solutions as well as conventional numerical procedures in which soil parameters are considered as uniformly constant. The statistical information obtained from Monte Carlo simulations is further utilized for the reliability analysis of buried flexible pipe-soil system with respect to deflection and buckling. The results of the reliability analysis clearly demonstrate the influence of extent of variation and spatial correlation structure of soil parameters on the performance assessment of buried flexible pipe-soil systems, which is not well captured in conventional procedures.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.123-130
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• 2003

The estimation of key soil properties and subsequent quantitative assessment of the associated uncertainties has always been an important issue in geotechnical engineering. It is well recognized that soil properties vary spatially as a result of depositional and post-depositional processes. The stochastic nature of spatially varying soil properties can be treated as a random field. A practical statistical approach that can be used to systematically model various sources of uncertainty is presented in the context of reliability analysis of slope stability Newly developed expressions for probabilistic characterization of soil properties incorporate sampling and measurement errors, as well as spatial variability and its reduced variance due to spatial averaging. Reliability analyses of the probability of slope failure using the different statistical representations of soil properties show that the incorporation of spatial correlation and conditional simulation leads to significantly lower probability of failure than obtained using simple random variable approach.

• Earthquakes and Structures
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• v.4 no.5
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• pp.509-525
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• 2013

In this paper, learning capabilities of two types of Arterial Neural Networks, namely hierarchical neural networks and Generalized Regression Neural Network were used in a two-stage approach to develop a method for generating spatial varying accelerograms from acceleration response spectra and a distance parameter in which generated accelerogram is desired. Data collected from closely spaced arrays of seismographs in SMART-1 array were used to train neural networks. The generated accelerograms from the proposed method can be used for multiple support excitations analysis of structures that their supports undergo different motions during an earthquake.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.4
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• pp.89-97
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• 2003

Using inference models developed for estimation of the parameters necessary to implement the Runoff Block of the Stormwater Management Model (SWMM), a number of alternative inference scenarios were developed to assess the influence of inference model complexity and structure on the calibration of the catchment modelling system. These inference models varied from the assumption of a spatially invariant value (catchment average) to spatially variable with each subcatchment having its own unique values. Fur-thermore, the influence of different measures of deviation between the recorded information and simulation predictions were considered. The results of these investigations indicate that the model performance is more influenced by model structure than complexity, and control parameter values are very much dependent on objective function selected as this factor was the most influential for both the initial estimates and the final results.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.4
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• pp.247-255
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• 2005

ice yield and plant growth response to nitrogen (N) fertilizer may vary within a field, probably due to spatially variable soil conditions. An experiment designed for studying the response of rice yield to different rates of N in combination with variable soil conditions was carried out at a field where spatial variation in soil properties, plant growth, and yield across the field was documented from our previous studies for two years. The field with area of 6,600 m2 was divided into six strips running east-west so that variable soil conditions could be included in each strip. Each strip was subjected to different N application level (six levels from 0 to 165kg/ha), and schematically divided into 12 grids $(10m \times10m\;for\;each\;grid)$ for sampling and measurement of plant growth and rice grain yield. Most of plant growth parameters and rice yield showed high variations even at the same N fertilizer level due to the spatially variable soil condition. However, the maximum plant growth and yield response to N fertilizer rate that was analyzed using boundary line analysis followed the Mitcherlich equation (negative exponential function), approaching a maximum value with increasing N fertilizer rate. Assuming the obtainable maximum rice yield is constrained by a limiting soil property, the following model to predict rice grain yield was obtained: $Y=10765{1-0.4704^*EXP(-0.0117^*FN)}^*MIN(I-{clay},\;I_{om},\;I_{cec},\;I_{TN},\; I_{Si})$ where FN is N fertilizer rate (kg/ha), I is index for subscripted soil properties, and MIN is an operator for selecting the minimum value. The observed and predicted yield was well fitted to 1:1 line (Y=X) with determination coefficient of 0.564. As this result was obtained in a very limited condition and did not explain the yield variability so high, this result may not be applied to practical N management. However, this approach has potential for quantifying the grain yield response to N fertilizer rate under variable soil conditions and formulating the site-specific N prescription for the management of spatial yield variability in a field if sufficient data set is acquired for boundary line analysis.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.423-432
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• 2003

The data observed at different positions are called the estimate of interested variable at new observation point on the Kriging utilize the space estimate technique, in which case there is correlation spatially. In this paper we provide the estimate for Variogram and Kriging methods as a field of kriging theory and dealt with actually measured data. And at the same time we forecast the amount of ozone that was not measured at this point by Kriging method and compared Ordinary Kriging method with Inverse Distance Kriging method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.278-281
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• 2004

Multiple sound sources are controlled to enhance sound power within a zone of interest. The problem of enhancing acoustic variable can be regarded as an optimization problem, which seeks an optimal control input that maximizes the acoustic variable. It should be noted that enhancing sound power of a selected region requires both the magnitude and direction to be controlled. For this reason, two kinds of cost functions that can represent the spatially distributed intensity are defined. Theoretical formulation shows the possibility of sound power control in a zone, and the detailed procedures of the proposed method are validated by numerical simulations.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.3
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• pp.129-138
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• 2003

A new methodology for selecting spatially variable model control parameter values through consideration of inference models within a Hydroinformatic system has been developed to overcome problems associated with determination of spatially variable control parameter values for both ungauged and gauged catchment. The adopted Hydroinformatic tools for determination of control parameter values were a GIS(Arc/Info) to handle spatial and non-spatial attribute information, the SWMM(stormwater management model) to simulate catchment response to hydrologic events, and lastly, L_BFGS_B(a limited memory quasi-Newton algorithm) to assist in the calibration process. As a result, high accuracy of control parameter estimation was obtained by considering the spatial variations of the control parameters based on landuse characteristics. Also, considerable time and effort necessary for estimating a large number of control parameters were reduced from the new calibration approach.