Digital Twin Ocean Engineering & Equipment

Aims & Scope

The rapid advancement of Digital Twin technology is transforming the field of ocean engineering, enabling smarter, more efficient, and safer marine operations. As the challenges such as extreme weather conditions, complex offshore activities, and the increasing demand for sustainable energy solutions, Digital Twins offer a powerful tool for simulation, monitoring, and predictive analytics. 

From marine equipment and offshore renewable energy systems to disaster prevention and automated exploration, Digital Twin technology is reshaping the way we design, operate, and maintain critical ocean infrastructure. The integration of data-driven learning, real-time simulations, and intelligent risk assessment is paving the way for smarter and more resilient marine systems. This session is organized to bring together researchers, engineers, and industry leaders to explore the latest advancements in Digital Twins for ocean engineering. With discussions on cutting-edge research, platform architecture, and real-world applications, this session will provide valuable insights that will shape the future of digital and intelligent ocean. 

 

The conference will focus on the following aspects:

▪Digital twin in oncean renewable energy

▪Digital twin in maritime operations, and disaster prevention

▪Digital twin in automated ocean exploration and offshore platform equipment

▪Digital twin in intelligent marine equipment design, real-time monitoring, operational simulation, smart decision-making, and risk control
▪Digital twin in architecture for ocean engineering: data-driven online learning and standardization of data exchange


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For session presentation applications, please contact the session chairs below:

Jinfeng Liu

Email: liujinfeng#just.edu.cn (replace # with @)

Yanjun Liu

Email: lyj111#sdu.edu.cn(replace # with @)


Session Chairs

Presentations

  • CIO
    China Merchants Industrial Group Co., Ltd.
    Exploration of the Application of Digital Twin Technology in Shipbuilding
    Abstract Research and apply digital twin technology to address various management pain points in shipbuilding enterprises. These pain points include insufficient multi - departmental collaborative control during the sectional pre - assembly and final block erection processes, poor information flow, significant task connection deviations between upstream and downstream processes, as well as irrational scheduling of core equipment and excessive waiting times during the production process.
  • Professor
    Dalian University of Technology (China)
    Digital twin technology in the shipbuilding industry: research based on platform load inversion
    Abstract The importance and necessity of digital twin technology in the shipbuilding industry are increasingly prominent. By simulating key factors such as the power system, fuel consumption, navigation route, and load distribution of ships, digital twin technology can improve the performance and safety of ships. With the help of digital twin technology, various indicators of ships can be monitored in real time, equipment failures and maintenance needs can be predicted, enabling ship owners and maintenance personnel to better plan and execute maintenance plans, reduce maintenance costs and downtime, and improve the reliability and availability of ships. Taking the offshore self elevating measurement and testing platform as a demonstration project, based on platform load inversion research, a structural digital platform is constructed to achieve digital twin and intelligent application of the exploration platform.
  • Associate Professor
    Tongji University (China)
    Optimal Design of Fixture Layout for Compliant Part with Application in Ship Hull Assembly Process
    Abstract In the ship assembly process, a large number of compliant parts are involved. The ratio of the part thickness to the length or the width is very small. Fixture design is a critical task in the ship assembly process due to its impact on the deformation and dimensional variation of the compliant parts. The current practice in the ship industry, the fixtures are uniformly distributed, which is non-optimal and large deformation will occur. This speech will talk about a series of studies of Prof. Liu for optimal design of fixture layout in the ship assembly process by integrating direct stiffness method and Metaheuristic algorithm, which significantly reduced the deformation of the compliant part in the ship assembly process.
  • Associate Professor
    Dalian Maritime University (China)
    Numerical calculation research on high frequency induction heating suitable for curved hull plate
    Abstract Greenhouse gas emissions in ship engineering are not only concentrated in the process of ship navigation, but also account for a large proportion of greenhouse gas emissions during ship construction. Therefore, the hull plate in the shipyard is taken as the research object which has more direct application value than the small size plate used in previous domestic and foreign researches. The existing numerical calculation model only considers the temperature field and local deformation, and does not consider the overall deformation. In order to improve the accuracy of the simulation calculation of the complex curved hull plate, a numerical computation model for high frequency induction heating is proposed considering the actual forming situation of the hull plate in shipyard. The constraint conditions of the numerical calculation model are improved in combination with the forming of the hull plate in shipyard. The findings of the research lay a foundation for the establishment of subsequent induction heating deformation prediction model. The research can provide theoretical and data support for the high frequency induction heating automation technology of ships. It can also provide reference for the curved plate forming in aerospace, automotive engineering, construction and other fields. 
  • Associate Professor
    Jimei University （China）
    Data-Model Hybrid Driven Low Carbon Dynamic Optimization in Laser Welding Processes
    Abstract Taking laser welding as the research object, we design and build a multi-source carbon emission monitoring system based on digital twins, and construct a digital twin-driven multi-source carbon emission model for laser welding; we analyze the carbon emission characteristic curve of the whole processing cycle, and calculate the percentage of carbon emission of each system in different processing stages. Comparative analysis of weld performance, organization, and carbon efficiency is carried out with typical welding processes as an example, and universal recommendations for energy saving and emission reduction in welding are given; the anomaly data are prescreened by using the dimensionless analysis method, and a physically-assisted migration learning model for welding carbon emission is proposed and constructed, which fully utilizes the migration mechanism, energy consumption mechanism, and energy consumption data of multiple working conditions, significantly improves the accuracy of the model prediction, and relieves the model's deep dependence on the data as well as its negative response to the noise; based on the real-time data, the model is developed and implemented. The negative response to noise; according to real-time monitoring data, the welding carbon emission off/online model is dynamically corrected and adjusted to achieve high-fidelity transient simulation of carbon emission characteristics of the laser welding process, based on which dynamic optimization and decision-making of the process parameters can be carried out, which can provide a reference idea for the research of low-carbon twinning digitization of laser welding. Hybrid data and model-driven low-carbon dynamic optimization of laser welding process.
  • Associate Professor
    Bauman Moscow State Technical University （Russia）
    Digital Twins for Modeling Flight Environment Processes in Aerospace Spacecraft Design
    Abstract Currently, the aerospace industry is developing in almost all areas: these include near-Earth constellations of spacecraft, and the development of deep space exploration. Designing spacecraft that move in deep space is impossible without the use of digital twins. The process is complicated by the fact that it is often very difficult and expensive or impossible to create full-fledged experimental setups that simulate the process of spacecraft movement in space. The processes that occur when spacecraft move in space are sometimes very fleeting and important. In the direction of designing spacecraft, the use of digital twins is very relevant and necessary.
  • Ph.D. Candidate
    Curtin University WASM （Australia）
    Risk-Sensitive Mining Truck Scheduling: A Bayesian DQN and Thompson
    Abstract This article combines Bayesian Neural Networks (BNN) with Deep-Q-Networks (DQN), using BNN as a replacement for the neural network in the DQN algorithm to address the truck dispatch problem in open-pit mining site. We compare the BNN-DQN algorithm with traditional DQN, PER-Dueling-DDQN, PPO, and AC algorithms. These algorithms are implemented in a simulated open pit mining transportation environment, reflecting near-realistic loading, unloading, and travelling/hauling conditions. The implementation results indicate that the BNN-DQN algorithm significantly reduces truck waiting times at loading and unloading points, improves fuel consumption, and enhances truck dispatching and loading distribution. It outperforms other algorithms in terms of scheduling efficiency and decision-making accuracy. This study demonstrates the potential of combining Bayesian Neural Networks with reinforcement learning to address complex logistical transportation problems in industrial environments.