• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.37 no.6
• /
• pp.571-579
• /
• 2009

For the flow analysis of reactive compressible media involving energetic materials and metallic confinements, a Hydro-SCCM (Shock Compression of Condensed Matter) tool is developed for handling multi-physics shock analysis of energetics and inerts. The highly energetic flows give rise to the strong non-linear shock waves and the high strain rate deformation of compressible boundaries at high pressure and temperature. For handling the large gradients associated with these complex flows in the condensed phase as well as in the reactive gaseous phase, a new Eulerian multi-fluid method is formulated. Mathematical formulation of explosive dynamics involving condensed matter is explained with an emphasis on validating and application of hydro-SCCM to a series of problems of high speed multimaterial dynamics in nature.

• Journal of Welding and Joining
• /
• v.16 no.3
• /
• pp.102-110
• /
• 1998

The horizontal fillet welding is prevalently used in heavy and ship building industries to fabricate the large scale structures. A deep understanding of the horizontal fillet welding process is restricted, because the phenomena occurring in welding are very complex and highly non-linear characteristics. To achieve the satisfactory weld bead geometry in robot welding system, the seam tracking algorithm should be reliable. The number of seam tracker was developed for arc welding automation by now. Among these seam tracker, the arc sensor is prevalently used in industrial robot welding system because of its low cost and flexibility. However, the accuracy of arc sensor would be decreased due to the electrical noise and metal transfer. In this study, the signal processing algorithm based on the neural network was implemented to enhance the reliability of measured welding current signals. Moreover, the seam tracking algorithm in conjunction with the signal processing algorithm was implemented to trace the center of weld line. It was revealed that the neural network could be effectively used to predict the welding current signal at the end of weaving.

• Journal of the Korean institute of surface engineering
• /
• v.40 no.4
• /
• pp.165-169
• /
• 2007

Compared to conventional Indium Tin Oxide (ITO) film deposition methods, cesium (Cs) assisted sputtering offers higher film characteristics in terms of electrical, mechanical and optical properties. However, it showed highly non-linear characteristics between process input factors and equipment responses. Therefore, to maximize film quality, optimization of manufacturing process is essential and process characterization is the first step for process optimization. For this, we designed 2 level design of experiment (DOE) to analyze ITO film characteristics including film thickness, resistivity and transmittance. DC power, pressure, carrier flow, Cs temperature and substrate temperature were selected for process input variables. Through statistical effect analysis methods, relation between three types of ITO film characteristics and five kinds of process inputs are successfully characterized and eventually, it can be used to optimize Cs assisted sputtering processes for various types of film deposition.

• Advances in aircraft and spacecraft science
• /
• v.5 no.3
• /
• pp.349-362
• /
• 2018

A non-linear numerical simulation technique for predicting the unsteady performances of an airbreathing engine is developed. The study focuses on the simulation of integrated propulsion systems, where a closer coupling is needed between the airframe and the engine dynamics. In fact, the solution of the fully unsteady flow governing equations, rather than a lumped volume gas dynamics discretization, is essential for modeling the coupling between aero-servoelastic modes and engine dynamics in highly integrated propulsion systems. This consideration holds for any propulsion system when a full separation between the fluid dynamic time-scale and engine transient cannot be appreciated, as in the case of flow instabilities (e.g., rotating stall, surge, inlet unstart), or in case of sudden external perturbations (e.g., gas ingestion). Simulations of the coupling between external and internal flow are performed. The flow around the nacelle and inside the engine ducts (i.e., air intakes, nozzles) is solved by CFD computations, whereas the flow evolution through compressor and turbine bladings is simulated by actuator disks. Shaft work balance and rotor dynamics are deduced from the estimated torque on each turbine/compressor blade row.

• Journal of the Korean Society of Systems Engineering
• /
• v.16 no.2
• /
• pp.67-77
• /
• 2020

Traditionally nuclear thermal hydraulic and nuclear safety has relied on numerical simulations to predict the system response of a nuclear power plant either under normal operation or accident condition. However, this approach may sometimes be rather time consuming particularly for design and optimization problems. To expedite the decision-making process data-driven models can be used to deduce the statistical relationships between inputs and outputs rather than solving physics-based models. Compared to the traditional approach, data driven models can provide a fast and cost-effective framework to predict the behavior of highly complex and non-linear systems where otherwise great computational efforts would be required. The objective of this work is to develop an AI algorithm to predict the peak fuel cladding temperature as a metric for the successful implementation of FLEX strategies under extended station black out. To achieve this, the model requires to be conditioned using pre-existing database created using the thermal-hydraulic analysis code, MARS-KS. In the development stage, the model hyper-parameters are tuned and optimized using the talos tool.

• Journal of KIBIM
• /
• v.11 no.4
• /
• pp.42-52
• /
• 2021

Paneling building facades is one of the essential procedures in building construction. Traditionally, it has been an easy task of simply projecting paneling patterns drawn in drawing boards onto 3d building facades. However, as many organic or curved building shapes are designed and constructed in modern architectural practices, the traditional one-to-one projection is becoming obsolete for the building types of the kind. That is primarily because of the geometrical discrepancies between 2d drawing boards and 3d curved building surfaces. In addition, curved compound surfaces are often utilized to accommodate the complicated spatial programs, building codes, and zoning regulations or to achieve harmonious geometrical relationships with neighboring buildings in highly developed urban contexts. The use of the compound surface apparently makes the traditional paneling pattern projection more challenging. Various mapping technics have been introduced to deal with the inabilities of the projection methods for curved facades. The mapping methods translate geometries on a 2d surface into a 3d building façade at the same topological locations rather than relying on Euclidean or Affine projection. However, due to the intrinsic differences of the planar 2d and curved 3d surfaces, the mapping often comes with noticeable distortions of the paneling patterns. Thus, this paper proposes a practical method of drawing paneling patterns directly on a curved compound surface utilizing Geodesic, which is faithful to any curved surface, to minimize unnecessary distortions.

• Kyungpook Mathematical Journal
• /
• v.61 no.4
• /
• pp.831-843
• /
• 2021

In this paper we look at the three dimensional stagnation point flow problem over a rough rotating disk. We study the theoretical behaviour of the stagnation point flow, or forced flow, in the presence of a slip factor in which convective instability stationary modes appear. We make a numerical investigation of the effects of slip on the behaviour of the flow components of the stagnation point flow where the disk is rough. We provide, for the first time in the literature, a complete convective instability analysis and an energy analysis. Suitable similarity transformations are used to reduce the Navier-Stokes equations and the continuity equation into a system of highly non-linear coupled ordinary differential equations, and these are solved numerically subject to suitable boundary conditions using the bvp4c function of MATLAB. The convective instability analysis and the energy analysis are performed using the Chebyshev spectral method in order to obtain the neutral curves and the energy bars. We observe that the roughness of the disk has a destabilising effect on both Type-I and Type-II instability modes. The results obtained will be prominently treated as benchmarks for our future studies on stagnation flow.

• Geomechanics and Engineering
• /
• v.30 no.2
• /
• pp.107-121
• /
• 2022

Coal pillar assessment is of broad importance to underground engineering structure, as the pillar failure can lead to enormous disasters. Because of the highly non-linear correlation between the pillar failure and its influential attributes, conventional forecasting techniques cannot generate accurate outcomes. To approximate the complex behavior of coal pillar, this paper elucidates a new idea to forecast the underground coal pillar stability using combined unsupervised-supervised learning. In order to build a database of the study, a total of 90 patterns of pillar cases were collected from authentic engineering structures. A state-of-the art feature depletion method, t-distribution symmetric neighbor embedding (t-SNE) has been employed to reduce significance of actual data features. Consequently, an unsupervised machine learning technique K-mean clustering was followed to reassign the t-SNE dimensionality reduced data in order to compute the relative class of coal pillar cases. Following that, the reassign dataset was divided into two parts: 70 percent for training dataset and 30 percent for testing dataset, respectively. The accuracy of the predicted data was then examined using support vector classifier (SVC) model performance measures such as precision, recall, and f₁-score. As a result, the proposed model can be employed for properly predicting the pillar failure class in a variety of underground rock engineering projects.

• Textile Coloration and Finishing
• /
• v.34 no.4
• /
• pp.250-260
• /
• 2022

In this research, a high-elasticity acrylic emulsion binder with core-shell polymerization and self-crosslinking system is mixed with a flame-retardant water-dispersed polyurethane (PUD) binder. In addition, finite element analysis was conducted through virtual engineering software ANSYS by applying three representative nonlinear material models. The most suitable nonlinear material model was selected after the relative comparison between the actual experimental values and the predicted values of the properties derived from simulations. The selected nonlinear material model is intended to be used as a nonlinear material model for computational simulation analysis that simulates the experimental environment of the vibration test (ASTM E1399) and the actual fire safety test (ASTM E1966). When the mass fraction of thermally expandable graphite was 0.7%, the thermal and physical properties were the best. Among the nonlinear material models, the simulation result of the Ogden model showed the closest value to the actual result.

• Journal of Forest and Environmental Science
• /
• v.39 no.4
• /
• pp.195-202
• /
• 2023

In this study, we sought to compare and evaluate the accuracy and predictive performance of machine learning algorithms for estimating the growth of individual Larix kaempferi trees in Gangwon Province, Korea. We employed linear regression, random forest, XGBoost, and LightGBM algorithms to predict tree growth using monitoring data organized based on different thinning intensities. Furthermore, we compared and evaluated the goodness-of-fit of these models using metrics such as the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). The results revealed that XGBoost provided the highest goodness-of-fit, with an R² value of 0.62 across all thinning intensities, while also yielding the lowest values for MAE and RMSE, thereby indicating the best model fit. When predicting the growth volume of individual trees after 3 years using the XGBoost model, the agreement was exceptionally high, reaching approximately 97% for all stand sites in accordance with the different thinning intensities. Notably, in non-thinned plots, the predicted volumes were approximately 2.1 m³ lower than the actual volumes; however, the agreement remained highly accurate at approximately 99.5%. These findings will contribute to the development of growth prediction models for individual trees using machine learning algorithms.