https://www.kriso.re.kr/sciwatch/handle/2021.sw.kriso/9806 2026-05-02T11:50:58Z 2026-05-02T11:50:58Z https://www.kriso.re.kr/sciwatch/handle/2021.sw.kriso/11201 2026-01-09T00:30:07Z Kim, Hyeon Ju Moon, Jung hyun Lim, Seung Taek KIM, SEGYU Lee, Ho Saeng Sung, Hong Gun Kwak, Hyun Uk https://www.kriso.re.kr/sciwatch/handle/2021.sw.kriso/11469 2026-01-12T01:30:23Z 2025-12-05T00:00:00Z

Title: 해수온도차발전 및 데이터센터 (OTEC-AIDC) 복합단지 개발 구상 Authors: Kim, Hyeon Ju; Moon, Jung hyun; Lim, Seung Taek; KIM, SEGYU; Lee, Ho Saeng; Sung, Hong Gun; Kwak, Hyun Uk Abstract: The surge in data center demand driven by AI transformation (AX) is rapidly increasing power consumption. Consequently, solutions to reduce electricity usage and minimize fossil fuel reliance are urgently needed. Data center solutions utilizing seawater heat represent one such approach, with facilities installed underwater, offshore, and onshore demonstrating excellent performance with a PUE of 1.2 or lower. Ocean Thermal Energy Conversion (OTEC) utilizes cold deep seawater as its heat source. The discharge water temperature remains sufficiently cold for data center cooling, and the discharge flow rate is also considered adequate. Applying the cold source from OTEC power plant discharge water to data center cooling not only allows for the distribution of intake facility costs but also enables the achievement of excellent performance with a PUE of 1.1 or lower by reducing intake power consumption. This approach involves adding a data center to an existing multi-stage model utilizing OTEC and DOWA (Discharged Ocean Water Application). In regions where deep seawater development is feasible in low-latitude waters, it could become a highly effective model for utilizing marine resources.

2025-12-05T00:00:00Z 김수빈 유영준 강민우 정성준 https://www.kriso.re.kr/sciwatch/handle/2021.sw.kriso/11166 2025-12-29T21:31:49Z 2025-12-01T00:00:00Z

Title: 지도학습을 이용한 전기추진 스마트 선박의 분당회전수 및 축 동력 추정 연구 Authors: 김수빈; 유영준; 강민우; 정성준 Abstract: IMO has been progressively strengthening regulations aimed at reducing greenhouse gas emissions and improving energy efficiency. As a result, there is a growing demand for methods that can quantitatively evaluate the full-scale performance of ships. Although various approaches have been proposed for estimating the performance, there are limitations in determining the relationships between speed？RPM and speed？power. Moreover, it is necessary to assess the effects of various factors on full-scale performance. In this paper, it is aimed to predict RPM and shaft power of an electric-propulsion smart ship from data-driven analysis using supervised learning. First, post-processing procedure for full-scale measurements is revised to improve the feasibility of deriving speed-RPM and speed-power relationships from the proposed model. Second, design variables for analyzing full-scale measurements based on supervised learning are established. Finally, it is possible to predict RPM and power with improved accuracy.

2025-12-01T00:00:00Z https://www.kriso.re.kr/sciwatch/handle/2021.sw.kriso/11094 2025-12-29T21:31:06Z