• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.70-70
• /
• 2020

기후변화 대응을 위해 파리기후협약에서는 온실가스 배출량 감축을 위한 구체적인 목표를 제시하였다. 에너지 분야는 온실가스가 가장 많이 배출되는 분야 중 하나이며, 온실가스 감축 방안으로 신재생 에너지에 대한 관심이 증가하고 있다. 특히 수력에너지는 신재생 에너지 중 가장 현실적이고 많이 활용되는 에너지원으로 각광받고 있다. 아시아 지역은 개발도상국이 다수 위치하고 있고 미개발된 잠재 수력에너지가 풍부한 지역으로 국내 기업의 진출 가능성이 높은 지역이다. 수력에너지 개발을 위해서는 수력 발전 가능량 평가가 필수적이며, 수력 발전 가능량은 기후, 수문조건에 민감하게 반응하기 때문에 기후변화에 따른 수력 발전 가능량의 변동 가능성이 있다. 본 연구에서는 아시아 지역에 대한 AR5 기후변화 시나리오 기반 수력 발전 가능량을 전망하고 분석하고자 하였다. 수력 발전 가능량 산정을 위한 수문 자료 생성은 지표수문해석 모형 VIC (Variable Infiltration Capacity)를 이용하였으며, 모형 입력 자료로 APHRODITE (Asian Precipitation -Highly Resolved Observational Data Integration Towards Evaluation of water resources) 기상 자료, USGS (U.S. Geological Survey) 수치지형도, FAO (Food and Agriculture Organization) 토양도, NCC (Norwegian Climate Centre) NorESM 기후변화 시나리오를 활용하였다. 분석결과 수력 발전 가능량은 과거 및 미래 기간에 동남아시아, 남아시아 지역에 풍부한 것으로 나타났다. 동남아시아는 유출량이 풍부하며, 남아시아는 유역별 낙차가 크기 때문에 수력 발전 가능량이 풍부한 것으로 나타났다. 따라서 동남아시아 지역의 수력 발전 가능량이 남아시아에 비해 기후변화의 영향을 크게 받는 것으로 나타났다. 또한 미래 기후변화로 인해 유출량의 변동 폭이 더욱 넓어져 발전 효율이 감소하는 것으로 나타나 수력발전의 안정성이 감소하였다. 본 연구는 아시아 지역의 수력 발전 가능량을 산정하고 특징을 분석하였다는 점에서 의미가 있다.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.3
• /
• pp.727-730
• /
• 2023

The operation of electric automatic windows is used in harsh environments, and the energy density decreases as charging and discharging are repeated, and as soundness deteriorates due to damage to the internal separator, the vehicle's mileage decreases and the charging speed slows down, so about 5 to 10 Batteries that have been used for about a year are classified as waste batteries, and for this reason, as the risk of battery fire and explosion increases, it is essential to diagnose batteries and estimate SOH. Estimation of current battery SOH is a very important content, and it evaluates the state of the battery by measuring the time, temperature, and voltage required while repeatedly charging and discharging the battery. There are disadvantages. In this paper, measurement of discharge capacity (C-rate) using a waste battery of a Tesla car in order to predict SOH estimation of a lithium-ion battery. A Support Vector Machine (SVM), one of the machine models, was applied using the data measured from the waste battery.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.3D
• /
• pp.215-222
• /
• 2010

Average speed denotes a travel speed based on the average travel time of vehicles to traverse a segment of roadway, and average travel speed is used as a measure of effectiveness (MOE) suggested in the highway capacity manual (HCM) for evaluating the level of service (LOS) of roadway. Most of the urban freeways in our country are having congestion problem regardless of the rush hours as a high-speed highway with a speed limit of 80km/h or less. Especially traffic congestion within the ramp junction areas is becoming worse by the increased traffic and lack of links with the arterials around the urban freeway. So, the purpose in this study is to identify the traffic characteristics within the ramp junction areas of urban freeway, predict the average speed within the ramp junction areas based on the traffic characteristics identified, and finally prove the validity of the average speed predicted.

• Advances in concrete construction
• /
• v.14 no.3
• /
• pp.153-167
• /
• 2022

The interaction between blast load and structures, as well as the interaction among structural members may well affect the structural response and damages. Therefore, it is necessary to analyse more realistic reinforced concrete structures in order to gain an extensive knowledge on the possible structural response under blast load effect. Among all the civilian structures, columns are considered to be the most vulnerable to terrorist threat and hence detailed investigation in the dynamic response of these structures is essential. Therefore, current research examines the effect of blast loads on the reinforced concrete columns via development of Pressure- Impulse (P-I) diagrams. In the finite element analysis, the level of damage on each of the aforementioned RC column will be assessed and the response of the RC columns when subjected to explosive loads will also be identified. Numerical models carried out using LS-DYNA were compared with experimental results. It was shown that the model yields a reliable prediction of damage on all RC columns. Validation study is conducted based on the experimental test to investigate the accuracy of finite element models to represent the behaviour of the models. The blast load application in the current research is determined based on the Lagrangian approach. To develop the designated P-I curves, damage assessment criteria are used based on the residual capacity of column. Intensive investigations are implemented to assess the effect of column dimension, concrete and steel properties and reinforcement ratio on the P-I diagram of RC columns. The produced P-I models can be applied by designers to predict the damage of new columns and to assess existing columns subjected to different blast load conditions.

• International conference on construction engineering and project management
• /
• 2020.12a
• /
• pp.510-519
• /
• 2020

Within an intelligent automated cyber-physical system, the realization of the autonomous mechanism for data collection, data integration, and data analysis plays a critical role in the design, development, operation, and maintenance of such a system. This construct is particularly vital for fault-tolerant route-finding systems that rely on the imprecise GPS location of the vehicles to properly operate, timely plan, and continuously produce informative feedback to the user. More essentially, the integration of digital twins with cyber-physical route-finding systems has been overlooked in intelligent transportation services with the capacity to construct the network routes solely from the locations of the operating vehicles. To address this limitation, the present study proposes a conceptual architecture that employs digital twin to autonomously maintain, update, and manage intelligent transportation systems. This virtual management simulation can improve the accuracy of time-of-arrival prediction based on auto-generated routes on which the vehicle's real-time location is mapped. To that end, first, an intelligent transportation system was developed based on two primary mechanisms: 1) an automated route finding process in which predictive data-driven models (i.e., regularized least-squares regression) can elicit the geometry and direction of the routes of the transportation network from the cloud of geotagged data points of the operating vehicles and 2) an intelligent mapping process capable of accurately locating the vehicles on the map whereby their arrival times to any point on the route can be estimated. Afterward, the digital representations of the physical entities (i.e., vehicles and routes) were simulated based on the auto-generated routes and the vehicles' locations in near-real-time. Finally, the feasibility and usability of the presented conceptual framework were evaluated through the comparison between the primary characteristics of the physical entities with their digital representations. The proposed architecture can be used by the vehicle-tracking applications dependent on geotagged data for digital mapping and location tracking of vehicles under a systematic comparison and simulation cyber-physical system.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2023.05a
• /
• pp.151-151
• /
• 2023

An accurate prediction of the hydrodynamic maneuvering darivatives is essential to desing a robust control system of a UUV(unmanned underwater vehicle). Typically, these derivatives were estimated by either the towing tank experiment or semi-empirical methods. With the enhancement of high performance computing capacity, a numerical analysis using computational fluid dynamics has reach the level of experiment. Therefore, the aims of the present research are to numerically develop a computational model for the vertical planar motion mechanism of a UUV and to estimate the hydrodynamics loads in 6-DOF. The target structure of the present study was manta type UUV (12meter length). The numerical model was developed in 1/ 6 model scale. Numerical results were compared with the results of the towing tank experiment for validation. In the present study, a commercial RANS-based viscous solver STARCCM+ (ver 17.06) was used.

• Wind and Structures
• /
• v.37 no.4
• /
• pp.303-314
• /
• 2023

In this study, two steady RANS turbulence models (SST k-ω and Realizable k-ε) and four unsteady turbulence models (URANS SST k-ω and Realizable k-ε, SST-SAS, and SST-IDDES) are evaluated with respect to their capacity to predict crosswind characteristics on high-speed trains (HSTs). All of the numerical simulations are compared with the wind tunnel values and LES results to ensure the accuracy of each turbulence model. Specifically, the surface pressure distributions, time-averaged aerodynamic coefficients, flow fields, and computational cost are studied to determine the suitability of different models. Results suggest that the predictions of the pressure distributions and aerodynamic forces obtained from the steady and transient RANS models are almost the same. In particular, both SAS and IDDES exhibits similar predictions with wind tunnel test and LES, therefore, the SAS model is considered an attractive alternative for IDDES or LES in the crosswind study of trains. In addition, if the computational cost needs to be significantly reduced, the RANS SST k-ω model is shown to provide relatively reasonable results for the surface pressures and aerodynamic forces. As a result, the RANS SST k-ω model might be the most appropriate option for the expensive aerodynamic optimizations of trains using machine learning (ML) techniques because it balances solution accuracy and resource consumption.

• Steel and Composite Structures
• /
• v.49 no.5
• /
• pp.533-546
• /
• 2023

Cost-effective solutions provided by composite construction are gaining popularity which, in turn, promotes the appearance on the market of new types of composite sections that allow not only to take advantage of the synergy of steel and concrete working together at room temperature, but also to improve their behaviour at high temperatures. When combined with high performance materials, significant load-bearing capacities can be achieved even with reduced cross-sectional dimensions. Steel-reinforced concrete-filled steel tubular (SR-CFST) columns are one of these innovative composite sections, where an open steel profile is embedded into a CFST section. Besides the renowned benefits of these typologies at room temperature, the fire protection offered by the surrounding concrete to the inner steel profile, gives them an enhanced fire performance which delays its loss of mechanical capacity in a fire scenario. The experimental evidence on the fire behaviour of SR-CFST columns is still scarce, particularly when combined with high performance materials. However, it is being much needed for the development of specific design provisions that consider the use of the inner steel profile in CFST columns. In this work, a new experimental program on the thermo-mechanical behaviour of SR-CFST columns is presented to extend the available experimental database. Ten SR-CFST stub columns, with circular and square geometries, combining high strength steel and concrete were tested. It was seen that the circular specimens reached higher failure times than the square columns, with the failure time increasing both when high strength steel was used at the embedded steel profile and high strength concrete was used as infill. Finally, different proposals for the reduction coefficients of high performance materials were assessed in the prediction of the cross-sectional fire resistance of the SR-CFST columns.

• Earthquakes and Structures
• /
• v.26 no.1
• /
• pp.31-48
• /
• 2024

In order to get a better understanding of seismic performance of exterior beam-column joint, reciprocating loading tests with variable loading speeds or axial forces were carried out. The main findings indicate that only few cracks exist on the surface of the joint core area, while the plastic hinge region at the beam end is seriously damaged. The damage of the specimen is more serious with the increase of the upper limit of variable axial force. The deflection ductility coefficient of specimen decreases to various degrees after the upper limit of variable axial force increases. In addition, the higher the loading speed is, the lower the deflection ductility coefficient of the specimen is. The stiffness of the specimen decreases as the upper limit of variable axial force or the loading speed increase. Compared to the influence of variable axial force, the influence of the loading speed on the stiffness degradation of the specimen is more obvious. The cumulative energy dissipation and the equivalent viscous damping coefficient of specimen decrease with the increase of loading speed. The influence of variable axial force on the energy dissipation of specimen varies under different loading speeds. Based on the truss model, the biaxial stress criterion, the Rankine criterion, the Kent-Scott-Park model, the equivalent theorem of shearing stress, the softened strut-and-tie model, the controlled slip theory and the proposed equations, a calculation method for the shear capacity is proposed with satisfactory prediction results.

• Computers and Concrete
• /
• v.34 no.1
• /
• pp.93-122
• /
• 2024

One well-known reason for using Fiber Reinforced Polymer (FRP) composites is to improve concrete strength and strain capacity via external confinement. Hence, various studies have been undertaken to offer a good illustration of the response of FRP-wrapped concrete for practical design intents. However, in such studies, the strength and strain of the confined concrete were predicted using regression analysis based on a limited number of test data. This study presents an approach based on artificial neural networks (ANNs) to develop models to predict the strength and strain at maximum stress enhancement of circular concrete cross-sections confined with different FRP types (Carbone, Glass, Aramid). To achieve this goal, a large test database comprising 493 axial compression experiments on FRP-confined concrete samples was compiled based on an extensive review of the published literature and used to validate the predicted artificial intelligence techniques. The ANN approach is currently thought to be the preferred learning technique because of its strong prediction effectiveness, interpretability, adaptability, and generalization. The accuracy of the developed ANN model for predicting the behavior of FRP-confined concrete is commensurate with the experimental database compiled from published literature. Statistical measures values, which indicate a better fit, were observed in all of the ANN models. Therefore, compared to existing models, it should be highlighted that the newly developed models based on FRP type are remarkably accurate.