Cage based salmon aquaculture has grown substantially over the last decades, however it is still, to a large degree, relying on experience-based production regime today. Advances in the digital transformation of the aquaculture industry will improve the ability to monitor, control and document the production systems, and facilitate knowledge-based decision making. In this paper, a combined instrumentation and interpreting solution is proposed and tested for monitoring fish distributions in aquaculture net cage. Farmed salmon in a full-scale sea cage are simulated by an individual-based fish model. And real-time behavioural changes are introduced and determined by data-driven parameter identification, thereby reflecting observed fish distributions from a set of single-beam echosounders. This forms a hybrid approach to combine the interpretability of physics-based models with the automatic pattern-identification capabilities of advanced deep learning algorithms. The performance of a tentative instrumental and model setup is evaluated by comparing with measured fish density data, while providing more detailed information such as the number and swimming speed of the fish, which are notoriously difficult to quantify using conventional solutions. The proposed hybrid approach is considered to be suitable for developing a more comprehensive fish monitoring system to be used on a daily basis in marine aquaculture.

As the global population grows, ensuring sustainable food production has become critical. Marine aquaculture provides a sustainable and scalable source of protein; however, its continued expansion requires the development of novel technologies that enable remote management and autonomous operations. Digital twin technology emerges as a transformative tool for realizing this goal, yet its adoption remains limited. Fish net cages-flexible, floating structures-are critical but vulnerable components of aquaculture systems. Exposed to harsh and dynamic marine conditions, they experience substantial hydrodynamic loads that can cause structural damage leading to fish escapes, environmental impacts, and financial losses. We propose a multifidelity surrogate modeling framework for integration into a digital twin that enables real-time monitoring of net cage structural dynamics under stochastic marine conditions. At the core of the framework lies the nonlinear autoregressive Gaussian process method, which captures complex, nonlinear cross-correlations between models of varying fidelity. It combines low-fidelity simulation data with a limited set of high-fidelity field sensor measurements, which, although accurate, are costly and spatially sparse. The framework was validated at the SINTEF ACE fish farm in Norway, where the digital twin assimilates online metocean data to accurately predict net cage displacements and mooring line loads, closely matching field measurements. This approach is especially valuable in data-scarce environments, offering rapid predictions and real-time structural representation. Beyond monitoring, the developed digital twin enables proactive assessment of structural integrity and supports remote operations with unmanned underwater vehicles. Finally, we compare Gaussian processes and graph convolutional networks for predicting net cage deformation, demonstrating the superior ability of the latter to capture in complex structural behaviors.

Underwater vehicles and other mobile platforms are seeing increased use as tools within fish farming, particularly due to current trends towards Precision Farming practices, and more exposed farming sites. Although many of the applications of such tools (e.g., net cleaning and inspection) have become well established industrial practices, it is largely unknown how much such operations disturb the fish and the consequence of this disturbance. In this study, we explored this by exposing Atlantic salmon in commercial net cages to intrusive objects and monitoring the distribution of fish around these using on-board 360-degree sonars. Six different object designs were tested covering variations in size, shape, and colour, which are important static characteristics of underwater vehicles/platforms. The sonar data was first aggregated into images containing the Cumulative Fish Presence over 1-, 5- and 10-min periods to provide a more robust foundation for further analyses. By training a deep learning based method using UNet++ architecture to automatically segment the fish distribution patterns, the mean distance between the inner perimeter of the fish distribution and the object was assessed. Results from the study implied that fish keep greater distances to larger objects. There was, however, no clear impact of the shape. Regarding the effect of colour, fish kept greater distances to yellow than to white objects. When comparing results from tests on fish of different size, data indicate a positive linear relationship between fish weight (age) and distance to an object, that can be expressed as an avoidance distance of an average 3.8 body-lengths. Our findings provide new fundamental knowledge on the dynamics between the fish and objects such as vehicles or other mobile platforms in fish farms, and thus provides valuable insights that can be useful when designing such tools specifically for aquaculture.

The potential future of open ocean aquaculture (OOA) lies in its ability to provide low-impact, sustainable seafood production offshore, offering a solution to meet the rising global seafood demand while addressing climate change and reducing the pressure on coastal areas. However, the development of OOA structures is costly, which emphasises the crucial importance of thorough concept evaluation during the design phase, where any risks associated with the new structure during its operation must be addressed. This involves not only ensuring structural reliability in challenging offshore environments but also ensuring that fish thrive in the enclosed space provided by the structure. A comprehensive, integrated evaluation of novel OOA concepts and design solutions may require special analysis tools and methods, which are found in neither today's offshore engineering nor traditional coastal aquaculture technology. This paper presents methods for a comprehensive analysis of a novel, submersed, flexible enclosure system developed for open-ocean finfish aquaculture in New Zealand. Herein, the enclosure structure is simplified and generalised by considering it as a horizontal fabric cylinder with ends covered by nets. The structure has outer ring structures to stiffen the enclosure and help maintain its cross-sectional shape. A single-point-mooring (SPM) system with either catenary-like or taut moorings allows the structure to freely rotate with the flow while being submerged at depths below which wave actions are reduced. The performed analyses consisted of three major steps needed to evaluate the OOA structure from different perspectives. In step (i), a computational fluid dynamics (CFD) modelling of steady flows through the structure with porous end covers was carried out to assess the internal flow reduction, which depends on the structure's shape and solidity of the net. Here, a CFD approach based on measured hydrodynamic properties of net material was employed. This modelling is necessary for ensuring optimal flow conditions for fish contained within the structure. It also provides water-velocity data needed for predicting the dissolved oxygen (DO) transport. In step (ii), time-domain simulations of the moored OOA structure in dynamic environments with waves and varying currents were conducted. Here, extreme conditions were modelled to evaluate the reliability of the structure and its mooring system under possible design loads. To model the flexible enclosure, a new finite element model based on so-called rotation-free shell elements was developed within the framework of FhSim, which is the simulation software at SINTEF Ocean for modelling flexible marine structures in waves and currents. In step (iii), the FhSim model was used to predict the motion of the structure in a harmonic tidal current. Building upon the results from the steady-state CFD analysis, these transient simulations provided the basis for predicting the DO dynamics within the enclosure depending on water temperature and respiration rates of fish (considering Atlantic salmon as an example). This helped identify when the dissolved oxygen levels within the enclosure may drop and how long it may take for them to recover during the tidal transitions, which is important for fish welfare. It was observed that the motion of the structure connected to an SPM mooring system may have a large impact on internaloxygen levels, offering insights for optimising the mooring design. Overall, the proposed methodology represents an integrated solution for concept evaluation during the design phase of OOA structures, potentially enabling the optimisation of new designs to handle offshore environmental loads while promoting fish welfare.

Digital Twin technology has emerged to become a key enabling technology in the ongoing transition into Industry 4.0. A Digital Twin is in essence a digital representation of an asset that provides better insight into its dynamics by combining a priori knowledge of the system through mathematical models with online data acquired from sensors and instruments deployed in or at the physical asset. While the technology is seeing increased use across several different industrial, governmental and research sectors, and across scientific disciplines, its application within aquaculture is still in its infancy. However, due to the rapid ongoing development in technological methods in aquaculture, an increasing number of the building blocks required to make a Digital Twin for aquaculture purposes are becoming available. We set out to explore these possibilities by first defining a Digital Twin — what components it should contain, how it should be constructed, and outlining the capability levels of a finished Digital Twin. Our next step was then to explore the state-of-the-art within the different required components and enabling technologies within aquaculture, thereby identifying the current foundation for developing Digital Twin technology in this sector. Following this, we developed concrete case studies that elaborate upon how we by combining existing and developing new technological tools could envision developing Digital Twins for three application areas of high industrial relevance, namely oxygen conditions in sea-cages, fish growth in sea-cages and in-cage robotics and vehicle operations. In conclusion, we present our thoughts on the potential of Digital Twin technology in being a key component in ushering in Industry 4.0 in aquaculture, and outline a pathway on the way onward towards achieving this goal.

Clean energy captured by offshore wind turbines has been widely used for supporting onshore activities. In the near future, facilities such as offshore wind turbines can also play an important role in the energy transition of offshore activities. Offshore wind energy can be employed for electrifying operations in offshore fish farms, which are traditionally supplied by diesel-engine barges/generators. Based on this motivation, this study focuses on the design of a shared mooring system between a semi-submersible offshore fish cage and a spar-type floating wind turbine. A numerical model of the proposed shared mooring system is implemented in a global response analysis software sima for performing fully coupled time-domain simulations. The configuration of the shared mooring line is determined using an engineering approach, which comprises Irvine’s formulation, system eigenvalue analysis, and cost estimation. Moreover, relevant case studies by altering the environmental conditions are performed. Extreme operational conditions that may give large relative motions are investigated thoroughly. The dynamic performance of the integrated system is compared with that of individual structures. The global motion of the floating wind turbine and its mooring line’s tension behavior are obviously influenced by the existence of the shared line. In general, the present work investigates the feasibility of a shared mooring system for these types of offshore structures and further gives insights into the engineering design procedure.

This paper proposes an extensive model for unmanned underwater vehicle operations in aquaculture and includes a dynamic 6 degrees of freedom (DOF) quaternion-based model of a remotely operated vehicle (ROV) and a model of a net cage structure. The proposed vehicle and net cage models are subjected to waves and a dynamic current flow, which itself is affected by the wake effects from the net cage. Furthermore, two ROV platforms with complete sets of dynamic parameters are presented, the Argus Mini ROV and the BlueROV2 underwater vehicle. The suggested models are tested for aquaculture operations in simulations using the Argus Mini ROV as the test case in the vehicle model. The simulations demonstrate a net following procedure where the vehicle is exposed to various degrees of environmental effects from waves and currents.

This paper proposes a low-cost solution for localizing a remotely operated vehicle (ROV) inside a fish net pen. The solution consists of a kinematic Kalman Filter capable of estimating the absolute ROV position and orientation in a fish net pen using primarily the onboard compass, laser-camera triangulation, and a model of the cylindrical net pen. The solution is demonstrated in a real fish net pen, under realistic operating conditions, and the performance is comparable to that of specialized positioning sensor systems such as ultra short baseline systems and Doppler velocity loggers.

Digital twins and relevant concepts are being applied in a wide variety of ways, and they are of most use when an actual real-world physical system or process (a physical twin) is changing over time and when measurement data correlated with this change can be captured. In this work, a digital twin model was implemented for real-time monitoring of aquaculture net cage systems, which is notoriously challenging because of several difficult-to-measure properties, such as forces on and deformation of the flexible netting structures, waves and flow field alterations around the cage and complex stiffness behaviour of the mooring elements made by fibre ropes. These properties were set to be adaptable according to the resultant outputs, such as cage responses and mooring loads that were continuously compared with the measurement data obtained from remote monitoring sensors. In this way, real-time sensor data were assimilated into the numerical simulation model for representing the actual net cage system. No dedicated sensors were used for fish monitoring, but the fish behavioural responses to current, wave and cage deformation were modelled according to relevant field observational data. A wireless sensor network has also been tested for the digital twin implementation, which was found to be suitable for practical uses in fish farms.

Clean energy captured by offshore wind turbines have been widely used for supporting onshore activities. In the near future, facilities such as offshore wind turbines can also play an important role in energy transition of offshore activities. Offshore wind energy can be employed for electrifying the operations in offshore fish farms, which are traditionally supplied by diesel-engine barges/generators. Based on this motivation, this study focuses on a design of shared mooring system between a semi-submersible offshore fish cage and a spar-type floating wind turbine. A numerical model of the proposed shared-mooring system is implemented in a global response analysis software SIMA for performing fully coupled time domain simulations. The configuration of the shared mooring line is determined using an engineering approach which comprises Irvine’s formulation, system eigenvalue analysis and cost estimation. Moreover, relevant case studies by altering the environmental conditions are performed. Extreme operational conditions that may give large relative motions are investigated thoroughly. The dynamic performance of the integrated system is compared with that of individual structures. The global motion of the floating wind turbine and its mooring line’s tension behavior are obviously influenced by the existence of the shared line. In general, the present work investigates the feasibility of a shared-mooring system for these types of offshore structures and further gives insights about the engineering design procedure.

We propose a web-based platform of integrated intelligent agents that incorporates multiple Functional Mock-up Units (FMUs) and surrogate modelling techniques. In this platform, each FMU envelops a stand-alone simulation component that represents an aquaculture system, such as the fish growth model, water quality model, and fish behavior model. Some FMUs may be computationally expensive to simulate or have different time step intervals, making integration with other FMUs difficult. To address these challenges, we employed surrogate models to substitute the more computationally expensive models. In this work, surrogate models are trained using simulation data and selected based on robustness analysis to ensure the overall system input-output reliability. The platform also includes a Chatbot component utilizing natural language processing and decision-making techniques to interpret user requests and provide tailored FMU configurations, enhancing the user simulation experience. Overall, the proposed platform provides a comprehensive and Efficient approach to modelling and simulating complex systems such as fish farms. Robustness analysis ensures the platform's accuracy and reliability, while the user-friendly interface enables easy tailored experimentation. By providing a framework for exploring the potential of aquaculture as a key source of food and income, the proposed platform represents a valuable interactive simulation tool for researchers, policymakers, and industry professionals seeking to improve the sustainability, efficiency, and economic viability of the aquaculture industry.

The shift towards salmon farming in more exposed locations has been an industry-wide trend for the last decade. Moving fish farms to locations with high water currents and waves can improve production by providing more stable temperatures and water quality, as well as reducing the negative environmental impacts of fish farming. This study investigates how waves affect the behavior of salmon from the same group, reared at different locations within a fish farm in standard circular sea cages. Using echosounders, DO (dissolved oxygen), temperature sensors and ADCP (Acoustic Doppler Current Profiler), we show that salmon avoid waves, swim below them and maintain their normal behavior. We also show that salmon behavior is related to the exposure of the cage, in the farm layout, to waves and currents. An integrated numerical model of fish and flexible sea cages is used to simulate the fish behavior under waves and currents and it able to reproduce the observed fish distributions in general.

In this study, a general control framework for autonomous operations in highly complex and dynamically changing environments such as fish farms is proposed and experimentally validated. Since fish farms feature an environment that includes fish, deformable flexible structures and highly variable environmental disturbances, the framework is designed to interact with these. The proposed control approach integrates estimates of the cage structure dynamics and fish behavior, adaptive path planning and path following control concepts in one unified and compact framework that could be used to implement and demonstrate different concept studies in dynamically changing environments. The performance of the control framework is investigated though field trials using a remotely operated vehicle (ROV) in a commercial fish farm. Experimental results show that the proposed framework can be applied to challenging operations in fish farms.

The majority of present marine finfish production is conducted in flexible net cages which can deform when they are subjected to water movements generated by currents. The ability to monitor net deformation is important for performing cage operations and evaluation of fish health and welfare under changing environment. This paper presents a new method for real-time monitoring of net cage deformations that is based on an integrated approach where positioning sensor data is incorporated into a numerical model. An underwater positioning system was deployed at a full-scale fish farm site, with three acoustic sensors mounted on a cage measuring positions of the net at different depths. A novel numerical model with an adaptive current field was used to simulate net cage deformations, where the magnitude and direction of the current could be adapted by continuously assessing deviations between the simulated and the measured positions of the net. This method was found to accurately predict the pre-defined current velocity profiles in a set of simulated experiments. In the field experiment, a good agreement was also obtained between the simulated positions of the net and the acoustic sensor data. The integrated approach was shown to be well suited for in-situ real-time monitoring of net cage deformations by using a significantly reduced number of sensors.

Closed fish cages have gained increased interest in marine aquaculture. However, knowledge on the seakeeping behaviour of a floating closed cage and the influence of the contained water inside the cage are still limited. In this paper, a coupled numerical model is developed for the simulation of closed rigid cages in waves. Numerical studies are conducted both in the frequency domain and in the time domain, and compared with scaled physical experiments. Special attention has been drawn to the coupling effects of sloshing on cage response and the resulting mooring line forces. The comparative analyses show that sloshing of the contained water has large influence on the coupled surge and pitch motions of the cage. Sloshing is also found to have significant effect on the mean-drift forces in regular waves. In the tested/simulated irregular waves, the mooring forces are found to be dominated by the slow-drift motions, which indicates that the slowly-varying wave drift forces need to be considered in the design of the mooring system for a floating closed cage.

With the continuous growing of the aquaculture industry and increasingly limited fish farming sites at close to shore areas both in Norway and worldwide, there is a need to develop fish farms suitable for aquaculture production in typical offshore environments. For this purpose, SALMAR has developed and deployed the Ocean Farm 1 facility for offshore fish farming. The main purpose of this paper is to develop a reliable numerical model and investigate the motion responses of the Ocean Farm 1 structure in waves and current. The established numerical model consists of the Ocean Farm 1's frame structure (with rigidly-connected circular column components), the net and the mooring system. The hydrodynamic external loads and coefficients of the frame structure are obtained by using potential flow theory. The quadratic drag load on the individual circular columns of the frame structure is formulated by a given drag coefficient. The loads on the net are formulated by using the screen model, where the Reynold number dependent lift and drag forces are formulated as a function of the solidity ratio Sn of the net, relative inflow angle and velocity. The hydrodynamic loads on the mooring lines are formulated using the Morison's equation and the structural responses of the mooring lines are obtained using a nonlinear FE model. With the developed numerical model, time domain simulations are performed. The simulation results are firstly validated against measured data from the decay tests, current tests, and regular wave tests. After the validation, numerical simulations are performed in different irregular wave and current combined weather conditions and the obtained motion response of Ocean Farm 1 are discussed and compared with available measurement data.

Finite Element Method (FEM) and Smoothed Particle Hydrodynamics (SPH) method are effective methods to study the interaction between marine structures and sea ice. However, both FEM and SPH methods have their own shortcomings in numerical simulations. There are mesh distortions and disappearance in FEM, and tensile instability, difficulty in applying boundary conditions, and low computational efficiency in SPH. Thus, to make up those problems, it is essential to develop a new numerical method. In the present study, the FEM–SPH adaptive method was applied in the numerical simulation of icebreaking, which could convert finite elements into SPH particles based on given conditions. Numerical models of cone and icebreaker interactions with level ice were established. Then, numerical simulation results were compared with empirical formula and model test results. In addition, the effects of cone angle and ice strength were analyzed. It was demonstrated that this algorithm could accurately predict the icebreaking resistance, which is the most concerned parameter in practice. Simultaneously, it could effectively simulate the accumulation process of ice rubbles. Thus, the FEM–SPH adaptive method is considered to be an effective way to simulate the interaction between marine structures and sea ice, and has great potential in the numerical simulation of icebreaking.

The effect of long-term use on the catch efficiency of biodegradable gillnets was investigated during commercial fishing trials and in controlled lab aging tests. The relative catch efficiency between biodegradable and nylon gillnets was evaluated over three consecutive fishing seasons for Atlantic cod (Gadus morhua) in Norway. The biodegradable gillnets progressively lost catch efficiency over time, as they caught 18.4%, 40.2%, and 47.4% fewer fish than the nylon gillnets during the first, second, and third season, respectively. A 1000-hour aging test revealed that both materials began to degrade after just 200 h and that biodegradable gillnets degraded faster than the nylon gillnets. Infrared spectroscopy revealed that the chemical structure of the biodegradable polymer changed more than the nylon. Although less catch efficient than nylon gillnets, biodegradable gillnets have great potential for reducing both capture in lost fishing gear and plastic pollution at sea, which are major problems in fisheries worldwide.