Machine learning models, for example, are highly effective in approximating relationships between propeller geometry, operating conditions, and performance parameters such as efficiency, thrust, cavitation behavior, and noise generation.
Once trained, these models can provide rapid performance predictions that support informed decision-making during the early stages of design.
Large Language Models (LLMs), on the other hand, are particularly valuable for text-intensive tasks such as drafting technical reports, reviewing documentation, and ensuring consistency in engineering terminology.
Because these activities often consume substantial engineering resources, automation allows professionals to remain focused on analysis and decision-making.
AI can also facilitate the development of agent-based solutions that combine LLM capabilities with controlled access to internal organizational data.
Such systems can retrieve approved templates, historical project records, and technical documentation while maintaining compliance with governance policies.
Furthermore, they can answer internal technical inquiries, assist with regulatory interpretation, and support the preparation of customer-specific documentation.
For more advanced hydrodynamic applications, Graph Neural Networks (GNNs) present exciting opportunities.
By representing geometric configurations and flow behavior as interconnected structures, GNNs can capture complex spatial relationships that are difficult to model using conventional regression-based approaches.
Rather than replacing high-fidelity simulations, these tools complement them by providing faster insights and accelerating preliminary analyses.
Active learning models offer another promising approach for reducing the time required for propeller optimization studies.
Unlike traditional optimization techniques that rely on predefined algorithms and extensive simulation campaigns, active learning employs adaptive models that continuously improve as new information becomes available.
This enables engineers to explore design alternatives more efficiently and shorten development cycles.
Digital twins add another powerful layer of value. By integrating sensor data with simulation models, digital twins support performance monitoring, fault detection, and predictive maintenance strategies.
Moreover, operational data collected from vessels in service can be fed back into the design process, generating valuable insights that contribute to future product development and continuous improvement.