TY - JOUR
T1 - A digital twin for ship structures—R&D project in Japan
AU - Fujikubo, Masahiko
AU - Okada, Tetsuo
AU - Murayama, Hideaki
AU - Houtani, Hidetaka
AU - Osawa, Naoki
AU - Iijima, Kazuhiro
AU - Hamada, Kunihiro
AU - Toh, Kimihiro
AU - Oka, Masayoshi
AU - Hirakawa, Shinichi
AU - Shibata, Kenichi
AU - Ashida, Tetsuro
AU - Arima, Toshiro
AU - Tanaka, Yoshiteru
AU - Tatsumi, Akira
AU - Takeuchi, Takaaki
AU - Mitsuyuki, Taiga
AU - Mikami, Kohei
AU - Kobayashi, Makito
AU - Komoriyama, Yusuke
AU - Ma, Chong
AU - Chen, Xi
AU - Ochi, Hiroshi
AU - Miratsu, Rei
N1 - Publisher Copyright:
© The Author(s), 2024.
PY - 2024/3/27
Y1 - 2024/3/27
N2 - In order to clarify and visualize the real state of the structural performances of ships in operation and establish a more optimal, data-driven framework for ship design, construction and operation, an industry-academia joint R&D project on the digital twin for ship structures (DTSS) was conducted in Japan. This paper presents the major achievements of the project. The DTSS aims to grasp the stress responses over the whole ship structure in waves by data assimilation that merges hull monitoring and numerical simulation. Three data assimilation methods, namely, the wave spectrum method, Kalman filter method, and inverse finite element method were used, and their effectiveness was examined through model and full-scale ship measurements. Methods for predicting short-term extreme responses and long-term cumulative fatigue damage were developed for navigation and maintenance support using statistical approaches. In comparison with conventional approaches, response predictions were significantly improved by DTSS using real response data in encountered waves. Utilization scenarios for DTSS in the maritime industry were presented from the viewpoints of navigation support, maintenance support, rule improvement, and product value improvement, together with future research needs for implementation in the maritime industry.
AB - In order to clarify and visualize the real state of the structural performances of ships in operation and establish a more optimal, data-driven framework for ship design, construction and operation, an industry-academia joint R&D project on the digital twin for ship structures (DTSS) was conducted in Japan. This paper presents the major achievements of the project. The DTSS aims to grasp the stress responses over the whole ship structure in waves by data assimilation that merges hull monitoring and numerical simulation. Three data assimilation methods, namely, the wave spectrum method, Kalman filter method, and inverse finite element method were used, and their effectiveness was examined through model and full-scale ship measurements. Methods for predicting short-term extreme responses and long-term cumulative fatigue damage were developed for navigation and maintenance support using statistical approaches. In comparison with conventional approaches, response predictions were significantly improved by DTSS using real response data in encountered waves. Utilization scenarios for DTSS in the maritime industry were presented from the viewpoints of navigation support, maintenance support, rule improvement, and product value improvement, together with future research needs for implementation in the maritime industry.
UR - http://www.scopus.com/inward/record.url?scp=85189081396&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85189081396&partnerID=8YFLogxK
U2 - 10.1017/dce.2024.3
DO - 10.1017/dce.2024.3
M3 - Article
AN - SCOPUS:85189081396
SN - 2632-6736
VL - 5
JO - Data-Centric Engineering
JF - Data-Centric Engineering
ER -