• Wind and Structures
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• 제25권2호
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• pp.101-129
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• 2017

Local transient extreme wind loads caused by group tower-related interference are among the major reasons that lead to wind-induced damage of super-large cooling towers. Four-tower arrangements are the most commonly seen patterns for super-large cooling towers. We considered five typical four-tower arrangements in engineering practice, namely, single row, rectangular, rhombic, L-shaped, and oblique L-shaped. Wind tunnel tests for rigid body were performed to determine the influence of different arrangements on static and dynamic wind loads and extreme interference effect. The most unfavorable working conditions (i.e., the largest overall wind loads) were determined based on the overall aerodynamic coefficient under different four-tower arrangements. Then we calculated the one-, two- and three-dimensional aerodynamic loads under different four-tower arrangements. Statistical analyses were performed on the wind pressure signals in the amplitude and time domains under the most unfavorable working conditions. On this basis, the non-Gaussian distribution characteristics of aerodynamic loads on the surface of the cooling towers under different four-tower arrangements were analyzed. We applied the Sadek-Simiu procedure to the calculation of two- and three-dimensional aerodynamic loads in the cooling towers under the four-tower arrangements, and the extreme wind load distribution patterns under the most unfavorable working conditions in each arrangement were compared. Finally, we proposed a uniform equation for fitting the extreme wind loads under the four-tower arrangements; the accuracy and reliability of the equation were verified. Our research findings will contribute to the optimization of the four-tower arrangements and the determination of extreme wind loads of super-large cooling towers.

• Smart Structures and Systems
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• 제29권3호
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• pp.445-455
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• 2022

Steel anchor bolts are installed in concrete using a variety of methods. One of the most common methods of anchor bolt installation is using epoxy resin as an infill material injected into the drilled hole to act as a bonding material between the steel bolt and the surrounding concrete. Typical design standards assume uniform stress distribution along the length of the anchor bolt accompanied with single crack leading to pull-out failure. Experimental evidence has shown that the steel anchor bolts fail owing to the multiple failure patterns, hence these design assumptions are not realistic. In this regard, the presented research work details the analytical model that takes into consideration multiple micro cracks in the infill material induced via impact loading. The impact loading from the Schmidt hammer is used to evaluate the bond condition bond condition of anchor bolt and the epoxy material. The added advantage of the presented analytical model is that it is able to take into account the various type of end conditions of the anchor bolts such as bent or U-shaped anchors. Through sensitivity analysis the optimum stiffness and shear strength properties of the epoxy infill material is achieved, which have shown to achieve lower displacement coupled with reduced damage to the surrounding concrete. The accuracy of the presented model is confirmed by comparing the simulated deformational responses with the experimental evidence. From the comparison it was found that the model was successful in simulating the experimental results. The proposed model can be adopted by professionals interested in predicting and controlling the deformational response of anchor bolts.

• 한국산업정보학회논문지
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• 제28권6호
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• pp.83-90
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• 2023

냉동컨테이너는 외부 환경에 의해 냉동기가 있는 적재부의 전면과 컨테이너 문이 있는 적재부의 후면부 사이에 온도 편차가 발생한다. 특히, 신선화물 운송에서 이러한 온도 편차는 화물의 신선도에 큰 영향을 미치게 된다. 본 연구에서는 온도 편차를 줄이기 위해 T-Floor를 부분적으로 차폐하고, T-Floor 차폐율이 냉동컨테이너 적재부 온도 변화에 미치는 영향을 평가하여 온도 편차를 최소화하는 방법을 제안한다. 실험 대상은 40 feet 스마트 냉동컨테이너로 T-Floor 차폐율은 0%, 50%, 60%, 70%, 80%로 설계 변수를 설정하였다. 실험 결과, 차폐율에 따라 냉동컨테이너 적재부의 온도 편차가 다르게 발생하였으며, 차폐율 60%인 경우 온도 편차가 가장 균일한 것을 확인했다. 이러한 적재부 온도 편차 최소화를 통해 스마트 냉동컨테이너를 이용하여 신선 화물의 운송 시 화물의 부패 및 냉해를 예방할 수 있다.

• 대기
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• 제33권5호
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• pp.519-530
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• 2023

Deep convection can make adverse effects on safe and efficient aviation operations by causing various weather hazards such as convectively-induced turbulence, icing, lightning, and downburst. To prevent such damage, it is necessary to accurately predict spatiotemporal distribution of deep convective area near the airport and airspace. This study developed a new index, the Aviation Convective Index (ACI), for deep convection, using the operational global Unified Model of the Korea Meteorological Administration. The ACI was computed from combination of three different variables: 3-hour maximum of Convective Available Potential Energy, averaged Outgoing Longwave Radiation, and accumulative precipitation using the fuzzy logic algorithm. In this algorithm, the individual membership function was newly developed following the cumulative distribution function for each variable in Korean Peninsula. This index was validated and optimized by using the 1-yr period of radar mosaic data. According to the Receiver Operating Characteristics curve (AUC) and True Skill Score (TSS), the yearly optimized ACI (ACI_YrOpt) based on the optimal weighting coefficients for 1-yr period shows a better skill than the no optimized one (ACI_NoOpt) with the uniform weights. In all forecast time from 6-hour to 48-hour, the AUC and TSS value of ACI_YrOpt were higher than those of ACI_NoOpt, showing the improvement of averaged value of AUC and TSS by 1.67% and 4.20%, respectively.

• 드라이브 ㆍ 컨트롤
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• 제21권2호
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• pp.53-64
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• 2024

Global population growth has resulted in an increased demand for food production. Simultaneously, aging rural communities have led to a decrease in the workforce, thereby increasing the demand for automation in agriculture. Drones are particularly useful for unmanned pest control fields. However, the current method of uniform spraying leads to environmental damage due to overuse of pesticides and drift by wind. To address this issue, it is necessary to enhance spraying performance through precise performance evaluation. Therefore, as a foundational study aimed at optimizing drone-based pest control technologies, this research evaluated water-sensitive paper (WSP) via density map estimation using convolutional neural networks (CNN) with a encoder-decoder structure. To achieve more accurate estimation, this study implemented multi-task learning, incorporating an additional classifier for image segmentation alongside the density map estimation classifier. The proposed model in this study resulted in a R-squared (R²) of 0.976 for coverage area in the evaluation data set, demonstrating satisfactory performance in evaluating WSP at various density levels. Further research is needed to improve the accuracy of spray result estimations and develop a real-time assessment technology in the field.