The organization of training of engineering personnel working with artificial intelligence is one of the most important areas of study in higher education institutions. Here are some of them:
Designing and modelling facilities:
creating 3D models and digital twins of offshore energy installations;
simulating the impact of sea conditions (waves, currents, storms) on structures;
optimising the design of installations to improve their efficiency and durability;
predicting the behaviour of structures under various operational conditions.
Selecting construction sites:
analysing geographical and hydrometeorological data to identify optimal locations for facilities;
assessing the impact of the chosen location on the marine ecosystem and shipping;
identifying potentially hazardous areas (e.g., areas with strong currents or underwater obstacles).
Managing construction projects:
scheduling work taking into account weather conditions and other factors;
optimising logistics for the delivery of materials and equipment;
monitoring compliance with project deadlines and budget;
analysing risks and developing measures to mitigate them.
Monitoring the condition of facilities:
analysing real-time sensor data to detect anomalies and potential faults;
predicting the service life of components and the need for replacement;
early detection of corrosion, damage, or other defects;
tracking environmental changes that may affect the operation of installations.
5. Predicting the performance of energy installations:
analysing historical data to predict energy generation;
assessing the impact of weather conditions and sea currents on the operation of installations;
forecasting peaks and troughs in generation based on meteorological data.
Optimising equipment operation:
calculating optimal operating modes for turbines and generators;
determining the most efficient positions for wind turbines or panels;
optimising the location of offshore energy installations, taking into account hydrodynamic and climatic factors.
Early detection of faults and preventive maintenance:
analysing real-time sensor data to identify anomalies;
predicting potential failures based on the analysis of equipment condition;
developing preventive maintenance schedules to minimise downtime.
Energy supply management and integration with power grids:
balancing the load in the power system, taking into account the instability of energy generation from offshore sources;
synchronising the operation of offshore energy installations with onshore energy sources;
optimising energy transmission to the shore, taking into account losses and grid load.
Modelling and designing new installations:
creating virtual models to test new designs and technologies;
simulating the impact of sea conditions (waves, currents, storms) on installations;
optimising the design of installations to improve their efficiency and durability.
Environmental impact analysis:
assessing the ecological impact of energy installations on the marine ecosystem;
modelling the spread of pollutants and their impact;
developing measures to minimise negative impacts on the marine environment.
11.Optimising logistics and maintenance of installations:
planning routes for service vessels, taking into account weather conditions and currents;
calculating the optimal time for maintenance work;
managing inventories and logistics for delivering components for repairs.
Processing large volumes of data:
analysing data from satellites, buoys and underwater sensors to study sea conditions;
identifying long-term climate change trends and their impact on offshore energy;
integrating data from various sources for a comprehensive analysis of the state of the marine environment and energy installations.
Developing adaptive control systems:
creating algorithms for automatically regulating the operation of installations under changing conditions;
developing systems for automatically responding to emergency situations (storms, technical malfunctions);
implementing intelligent control systems to improve the reliability and safety of installations.
Optimising investments and planning industry development:
analysing economic indicators and risks for investment projects;
forecasting the demand for offshore energy in different regions;
developing strategies for the development of offshore energy, taking into account technological and economic factors.
The view that engineers’ training in working with artificial intelligence (AI) should be conducted in parallel with the classical teaching of relevant subjects at universities has a number of compelling arguments:
Fundamental knowledge as the basis for working with AI:
classical education provides an understanding of the laws of physics, mathematical principles, and engineering foundations, without which the effective use of AI in professional activities is impossible;
the knowledge gained from studying traditional disciplines helps engineers critically evaluate the results of AI work, identify potential errors, and correct processes.
Controlling and adjusting the work of AI:
an engineer with classical knowledge can better understand the limitations and capabilities of the technologies on which AI is based;
the creator or specialist working with AI systems must be able to analyse their solutions in terms of compliance with real physical and technical conditions.
Integration of traditional methods and new technologies:
studying classical disciplines and AI technologies in parallel allows engineers to learn how to combine time-tested approaches with innovative methods;
such training prepares specialists who are able to work in interdisciplinary teams and solve complex engineering problems using modern tools.
Preparation for professional challenges:
in modern engineering practice, AI often acts as a tool that requires competent application;
engineers with a classical education and AI skills will be more competitive in the labour market.
However, there are some aspects to consider:
The need for specialised AI courses: along with classical disciplines, it is important to develop separate programmes devoted to algorithms, machine learning methods, and the specifics of working with AI technologies.
Developing critical thinking: engineers must be able not only to apply AI but also to assess the boundaries of its applicability, which requires a deep understanding of both classical theories and the principles of AI operation.
Updating curriculum programmes: universities must regularly revise their courses to include up-to-date knowledge about modern technologies without losing the fundamentality of education.
Thus, combining classical education and training in working with AI can be the optimal solution for training a new generation of engineers who are able to work effectively in a rapidly changing technological landscape.