• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.118-121
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• 2023

Recently, green energy support policies have been announced around the world in accordance with environmental regulations, and asthe market grows rapidly, demand for batteries is also increasing. Therefore, various methodologies for battery diagnosis and recycling methods are being discussed, but current accurate life prediction of batteries has limitations due to the nonlinear form according to the internal structure or chemical change of the battery. In this paper, CS2 lithium-ion battery measurement data measured at the A. James Clark School of Engineering, University of Marylan was used to predict battery performance with high accuracy using a convolutional neural network (CNN) model among deep learning-based models. As a result, the battery performance was predicted with high accuracy. A data structure with a matrix of total data 3,931 ☓ 19 was designed as test data for the CS2 battery and checking the result values, the MAE was 0.8451, the RMSE was 1.3448, and the accuracy was 0.984, confirming excellent performance.

• Journal of the Society of Naval Architects of Korea
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• v.32 no.1
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• pp.58-69
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• 1995

The present paper is devoted to constructing a numerical algorithm for solving un steady problems on generation, propagation and interaction of nonlinear waves at a surface of ideal fluid, within the framework of the potential-flow model. The numerical scheme is implicit. with non-linearity iteration at every step of time. the finite-difference method with boundary-fitted coordinates are presented in favor for validity and high efficiency of the numerical model developed. Among these arguments, there are the results of calculations of two test problems-on stretching of a liquid ellipse and on wave generation by lifting a portion of a bottom.

• Journal of the Society of Naval Architects of Korea
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• v.55 no.5
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• pp.401-414
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• 2018

Planing hull type ships are often equipped with interceptor or trim tab to improve the excessive trim angle which leads to poor resistance and sea keeping performances. The purpose of this study is to design a controller to control the attitude of the ship by controllable stern interceptor and validate the effectiveness of the attitude control by the towing tank test. Embedded controller, servo motor and controllable stern interceptor system were equipped with planing hull type model ship. Prior to designing the control algorithm, a model test was performed to identify the system dynamic model of the planing hull type ship including the stern interceptor. The matrix components of model were optimized by Genetic Algorithm. Using the identified model, PID controller which is a classical controller and sliding mode controller which is a nonlinear robust controller were designed. Gain tuning of the controllers and running simulation was conducted before the towing tank test. Inserting the designed control algorithm into the embedded controller of the model ship, the effectiveness of the active control of the stern interceptor was validated by towing tank test. In still water test with small disturbance, the sliding mode controller showed better performance of canceling the disturbance and the steady-state control performance than the PID controller.

• Structural Engineering and Mechanics
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• v.27 no.6
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• pp.697-711
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• 2007

Though extensive research has been carried out for the ultimate strength of steel reinforced concrete (SRC) members under static and cyclic load, there was only limited information on the applied analysis models. Modeling of the inelastic response of SRC members can be accomplished by using a microcosmic model. However, generally used microcosmic model, which usually contains a group of parameters, is too complicated to apply in the nonlinear structural computation for large whole buildings. The intent of this paper is to develop an effective modeling approach for the reliable prediction of the inelastic response of SRC columns. Firstly, five SRC columns were tested under cyclic static load and constant axial force. Based on the experimental results, normalized trilinear skeleton curves were then put forward. Theoretical equation of normalizing point (ultimate strength point) was built up according to the load-bearing mechanism of RC columns and verified by the 5 specimens in this test and 14 SRC columns from parallel tests. Since no obvious strength deterioration and pinch effect were observed from the load-displacement curve, hysteresis rule considering only stiffness degradation was proposed through regression analysis. Compared with the experimental results, the applied analysis model is so reasonable to capture the overall cyclic response of SRC columns that it can be easily used in both static and dynamic analysis of the whole SRC structural systems.

• Steel and Composite Structures
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• v.19 no.5
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• pp.1145-1165
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• 2015

A behavior model for screw connections is developed to provide a better representation of the nonlinear response of thin steel plate shear walls (TSPSWs) with infill plates attached to the boundary frame members via self-drilling screws. This analytical representation is based on the load-bearing deformation relationship between the infill plate and the screw threads. The model can be easily implemented in strip models of TSPSWs where the tension field action of the infill plates is represented by a series of parallel discrete tension-only strips. Previously reported experimental results from tests of two different TSPSWs are used to provide experimental validation of the modeling approach. The beam-to-column connection behavior was also included in the analyses using a four parameter rotational spring model that was calibrated to a test of an identical frame as used for the TSPSW specimens but without the infill plates. The complete TSPSW models consisting of strips representing the infill plates, zero length elements representing the load-bearing deformation response of the screw connection at each end of the strips and the four parameter spring model at each beam-to-column connection are shown to have good agreement with the experimental results. The resulting models should enable design and analysis of TSPSWs for both new construction and retrofit of existing buildings.

• Nuclear Engineering and Technology
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• v.46 no.5
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• pp.619-632
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• 2014

The Best Estimate Plus Uncertainty (BEPU) method has been widely used to evaluate the uncertainty of a best-estimate thermal hydraulic system code against a figure of merit. This uncertainty is typically evaluated based on the physical model's uncertainties determined by expert judgment. This paper introduces the application of data assimilation methodology to determine the uncertainty bands of the physical models, e.g., the mean value and standard deviation of the parameters, based upon the statistical approach rather than expert judgment. Data assimilation suggests a mathematical methodology for the best estimate bias and the uncertainties of the physical models which optimize the system response following the calibration of model parameters and responses. The mathematical approaches include deterministic and probabilistic methods of data assimilation to solve both linear and nonlinear problems with the a posteriori distribution of parameters derived based on Bayes' theorem. The inverse problem was solved analytically to obtain the mean value and standard deviation of the parameters assuming Gaussian distributions for the parameters and responses, and a sampling method was utilized to illustrate the non-Gaussian a posteriori distributions of parameters. SPACE is used to demonstrate the data assimilation method by determining the bias and the uncertainty bands of the physical models employing Bennett's heated tube test data and Becker's post critical heat flux experimental data. Based on the results of the data assimilation process, the major sources of the modeling uncertainties were identified for further model development.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.12
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• pp.638-644
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• 2017

Optimal cooling operation algorithm was developed based on a simulation case of a single family house model equipped with renewable energy facility. EnergyPlus simulation results were used as virtual test data. The model contained three energy storage elements: thermal heat capacity of the living room, chilled water storage tank, and battery. Their charging and discharging schedules were optimized so that daily electricity bill became minimal. As an optimization tool, linear programming was considered because it was possible to obtain results in real time. For its adoption, EnergyPlus-based house model had to be linearly approximated. Results of this study revealed that dynamic cooling load of the living room could be approximated by a linear RC model. Scheduling based on the linear programming was then compared to that by a nonlinear optimization algorithm which was made using GenOpt developed by a national lab in USA. They showed quite similar performances. Therefore, linear programming can be a practical solution to optimal operation scheduling if linear dynamic models are tuned to simulate their real equivalents with reasonable accuracy.

• Steel and Composite Structures
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• v.6 no.4
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• pp.285-302
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• 2006

This paper describes the design and behaviour of cold-formed stainless steel slender circular hollow section columns. The columns were compressed between fixed ends at different column lengths. The investigation focused on large diameter-to-plate thickness (D/t) ratio ranged from 100 to 200. An accurate finite element model has been developed. The initial local and overall geometric imperfections have been included in the finite element model. The material nonlinearity of the cold-formed stainless steel sections was incorporated in the model. The column strengths, load-shortening curves as well as failure modes were predicted using the finite element model. The nonlinear finite element model was verified against test results. An extensive parametric study was carried out to study the effects of cross-section geometries on the strength and behaviour of stainless steel slender circular hollow section columns with large D/t ratio. The column strengths predicted from the parametric study were compared with the design strengths calculated using the American Specification, Australian/New Zealand Standard and European Code for cold-formed stainless steel structures. It is shown that the design strengths obtained using the Australian/New Zealand and European specifications are generally unconservative for the cold-formed stainless steel slender circular hollow section columns, while the American Specification is generally quite conservative. Therefore, design equation was proposed in this study.

• Nuclear Engineering and Technology
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• v.40 no.1
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• pp.77-86
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• 2008

This paper describes the nonlinear analyses of a 1:4 scale model of a prestressed concrete containment vessel (PCCV) using an axisymmetric model and a three-dimensional model. These two models are refined by comparison of the analysis results and with testing results. This paper is especially focused on the analysis of behavior under pressure and the temperature effects revealed using an axisymmetric model. The temperature-dependent degradation properties of concrete and steel are considered. Both geometric and material nonlinearities, including thermal effects, are also addressed in the analyses. The Menetrey and Willam (1995) concrete constitutive model with non-associated flow potential is adopted for this study. This study includes the results of the predicted thermal and mechanical behaviors of the PCCV subject to high temperature loading and internal pressure at the same time. To find the effect of high temperature accident conditions on the ultimate capacity of the liner plate, reinforcement, prestressing tendon and concrete, two kinds of analyses are performed: one for pressure only and the other for pressure with temperature. The results from the test on pressurization, analysis for pressure only, and analyses considering pressure with temperatures are compared with one another. The analysis results show that the temperature directly affects the behavior of the liner plate, but has little impact on the ultimate pressure capacity of the PCCV.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4022-4032
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• 2021

In this study, machine learning (ML) techniques were used to model surveillance test data of nuclear power plants from an international database of the ASTM E10.02 committee. Regression modeling was conducted using various techniques, including Cubist, XGBoost, and a support vector machine. The root mean square deviation of each ML model for the baseline dataset was less than that of the ASTM E900-15 nonlinear regression model. With respect to the interpolation, the ML methods provided excellent predictions with relatively few computations when applied to the given data range. The effect of the explanatory variables on the transition temperature shift (TTS) for the ML methods was analyzed, and the trends were slightly different from those for the ASTM E900-15 model. ML methods showed some weakness in the extrapolation of the fluence in comparison to the ASTM E900-15, while the Cubist method achieved an extrapolation to a certain extent. To achieve a more reliable prediction of the TTS, it was confirmed that advanced techniques should be considered for extrapolation when applying ML modeling.