Digital Twins for Ship Design and Optimization

Detailed overview of innovation with sample startups and prominent university research

What it is

Digital twins in the marine industry are dynamic, virtual representations of a ship and its various systems. These digital replicas leverage real-time data from sensors onboard the vessel to mirror the actual ship’s performance, behavior, and condition. This allows for simulations, analysis, and optimization that would be difficult or impossible to conduct on the physical ship itself.

Impact on climate action

Digital Twins for Ship Design and Optimization revolutionize maritime industries, cutting emissions by enhancing vessel efficiency. Real-time data and simulations optimize ship performance, reducing fuel consumption and greenhouse gas emissions. By advancing low-carbon marine technology, this innovation accelerates climate action, mitigating environmental impact and promoting sustainable shipping practices.


  • Internet of Things (IoT): Sensors onboard the ship collect a wide range of data, including engine performance, fuel consumption, emissions, hull stress, and environmental conditions.
  • Cloud Computing: Cloud platforms provide the computing power and storage capacity necessary to handle the massive amounts of data generated by shipboard sensors.
  • Data Analytics: Advanced data analytics techniques are used to process and interpret sensor data, providing insights into the ship’s performance and identifying areas for improvement.
  • 3D Modeling and Simulation: Digital twins often incorporate detailed 3D models of the ship and its systems, allowing for virtual simulations of different operational scenarios.
  • Artificial Intelligence (AI) and Machine Learning: AI and machine learning algorithms can be used to analyze data patterns, predict maintenance needs, and optimize system performance.

TRL : 7-8

Prominent Innovation themes

  • Real-Time Performance Optimization: Using digital twins to simulate and optimize ship operations in real-time, adjusting parameters such as engine speed, route, and trim to minimize fuel consumption and emissions.
  • Predictive Maintenance: Leveraging data analytics and machine learning to predict maintenance needs for onboard systems, enabling proactive maintenance scheduling, reducing downtime, and minimizing costly repairs.
  • Hull Performance Monitoring: Analyzing data from hull sensors to monitor stress, vibration, and other factors that impact hull integrity and efficiency, allowing for proactive maintenance and optimization.
  • Emissions Monitoring and Reduction: Utilizing digital twins to simulate and optimize emissions scrubbing systems, ensuring optimal performance and compliance with environmental regulations.
  • Virtual Commissioning and Testing: Using digital twins to test and validate new ship designs and onboard systems before physical construction, reducing development time and costs.

Other Innovation Subthemes

  • Dynamic Ship Simulation
  • IoT-enabled Ship Monitoring
  • Cloud-Based Data Analytics
  • 3D Modeling for Virtual Ships
  • AI-driven Performance Analysis
  • Real-Time Operational Optimization
  • Predictive Maintenance Forecasting
  • Hull Integrity Monitoring
  • Vibration Analysis for Efficiency
  • Emissions Reduction Strategies
  • Scrubbing System Optimization
  • Virtual Ship Commissioning
  • Digital Twin-based Testing
  • Fuel Efficiency Enhancement
  • Route Optimization Strategies
  • Trim Adjustment Simulations
  • Proactive Maintenance Scheduling
  • Cost-Effective Repair Strategies
  • Sustainable Shipping Solutions

Sample Global Startups and Companies

  • Akselos:
    • Technology Focus: Akselos specializes in structural engineering simulations and digital twin technology. Their focus likely revolves around creating highly accurate digital replicas of ship structures and systems, enabling real-time monitoring, predictive maintenance, and performance optimization.
    • Uniqueness: Akselos stands out for its advanced algorithms and computational techniques, which allow for rapid and accurate simulations of complex structural behaviors. Their digital twin solutions might offer unparalleled insights into the structural integrity and performance of ships.
    • End-User Segments: Their target segments could include shipbuilders, naval architects, ship operators, and maritime regulatory bodies seeking to improve safety, efficiency, and sustainability in the maritime industry.
  • ShipIn Systems:
    • Technology Focus: ShipIn Systems likely specializes in developing comprehensive digital twin platforms tailored specifically for ship design and optimization. Their solutions may integrate data from various onboard sensors, equipment, and operational systems to create holistic digital representations of vessels.
    • Uniqueness: ShipIn Systems could differentiate itself through its user-friendly interfaces, customizable analytics modules, and robust integration capabilities. Their digital twin platforms might offer end-to-end support for ship lifecycle management, from design and construction to operation and maintenance.
    • End-User Segments: Their target segments may include shipyards, shipowners, fleet managers, maritime engineering firms, and classification societies looking to leverage digitalization for improved ship performance and operational efficiency.
  • DNV GL:
    • Technology Focus: DNV GL is a leading classification society and consultancy firm with a strong focus on digitalization and innovation in the maritime industry. Their digital twin solutions likely encompass a wide range of services, including ship design optimization, performance monitoring, and risk management.
    • Uniqueness: DNV GL’s expertise in maritime safety and regulatory compliance, coupled with their deep understanding of digital technologies, positions them as a trusted partner for shipowners and operators navigating the complexities of digital transformation.
    • End-User Segments: DNV GL’s digital twin solutions cater to a diverse range of stakeholders in the maritime sector, including shipyards, ship operators, insurers, regulators, and port authorities. Their services are designed to enhance safety, reliability, and sustainability throughout the maritime ecosystem.

Sample Research At Top-Tier Universities

  • Massachusetts Institute of Technology (MIT):
    • Technology Enhancements: MIT researchers are pioneering the use of digital twin technology for ship design and optimization. They are integrating advanced computational models with real-time data from sensors and IoT devices to create virtual replicas of marine vessels. These digital twins enable engineers to simulate various operating conditions and optimize the design for fuel efficiency and emissions reduction.
    • Uniqueness of Research: MIT’s approach involves a holistic view of ship performance, considering factors such as hull design, propulsion systems, and operational strategies. By leveraging data-driven insights, they can identify opportunities for improving energy efficiency and reducing carbon emissions throughout the vessel’s lifecycle.
    • End-use Applications: The research at MIT has implications for the maritime industry, including shipping companies, shipbuilders, and regulatory agencies. By adopting digital twins for ship design and optimization, stakeholders can design and operate vessels that comply with increasingly stringent environmental regulations while maximizing economic performance.
  • Technical University of Delft:
    • Technology Enhancements: Researchers at the Technical University of Delft are developing advanced simulation tools and optimization algorithms for digital twins of marine systems. They are focusing on modeling complex fluid dynamics, structural mechanics, and environmental interactions to accurately predict the performance of low-carbon marine vessels.
    • Uniqueness of Research: Delft’s research emphasizes the integration of digital twins into the entire lifecycle of marine systems, from conceptual design to operation and maintenance. By incorporating feedback loops between simulation models and real-world data, they can continuously improve the accuracy and reliability of digital twins over time.
    • End-use Applications: The research at Delft has practical applications for ship designers, naval architects, and marine engineers. By using digital twins for ship design and optimization, stakeholders can explore innovative design concepts, evaluate performance trade-offs, and minimize environmental impact while ensuring safety and reliability.
  • University College London (UCL):
    • Technology Enhancements: UCL researchers are exploring the use of artificial intelligence and machine learning techniques to enhance the capabilities of digital twins for ship design and optimization. They are developing algorithms that can analyze vast amounts of data to identify optimal design configurations and operational strategies for low-carbon marine vessels.
    • Uniqueness of Research: UCL’s research focuses on the integration of digital twins with emerging technologies such as blockchain and Internet of Things (IoT) for enhanced transparency and traceability in the maritime supply chain. By leveraging distributed ledger technology, they aim to improve collaboration and information sharing among stakeholders while ensuring data security and privacy.
    • End-use Applications: The research at UCL has implications for the entire maritime ecosystem, including shipping companies, port operators, and regulatory authorities. By harnessing the power of digital twins and advanced analytics, stakeholders can optimize resource utilization, reduce emissions, and mitigate the environmental impact of maritime activities.

commercial_img Commercial Implementation

Digital twin technology is gaining traction in the marine industry, with several shipping companies and shipyards adopting this approach to improve design, optimize performance, and predict maintenance needs. For example, Maersk has implemented digital twins for several of its container ships, using the technology to monitor hull performance, optimize fuel consumption, and predict maintenance schedules.