Introduction
One third of the global salmon production is currently contributing by Norway.
To improve the focus on the environmental performance of fish farms in
Norwegian aquaculture, new businesses and innovative technologies
are frequently introduced and tested across the whole country [1, 2].
One of the
innovative production systems
is Semi Closed-C ontainment Systems (S-CCS), where the fish are
separated from the outside natural environment with a physical barrier . With a proper enhanced control
on tank hydrodynamics and self-cleaning , production cycle, disease
transmission
and environmental impact which makes S- CCS a favorable
replacement to open-cage
production systems [1, 3].
The overall hydrodynamic performance of the system is influenced by inflow characteristics, i.e. turbulence produced by inlet orientations but experimentally it is not feasible to study velocity, uniformity, vorticity and swirl number, respectively [5,6]. Therefore, computational fluid dynamics modeling (CFD) is considered as a most appropriate tool to investigate the hydrodynamics of such a large system.
The aim of current CFD investigation was to monitor the effect of inlet column nozzles orientation angle and water dividers placements on the hydrodynamics of S- CCS with rigid walls and establish the optimal inlet column nozzles orientation set-up for use during Atlantic salmon production in this system.
Materials and Methods
Circular shaped floating tank with rigid walls and a total volume of 3500 m3 received water from two inlet column with nozzles (possible to operate 6 inlets columns simultaneously) . The water is discharged through central outlets located at a depth from top ( between -1.5m to -5.5m) (sieved region of 4m around the central outlet pipe).
In order to design and find the most optimal inlet nozzles and water divider (V-shaped
closed structure p laced behind each column to split water stream)
orientation angle for optimal water distribution,
a set-up of four cases were developed in CFD (Table 1) . The hydrodynamics of the system was evaluated using different flow field indicators, such as flow velocity, distribution of vortices, turbulence in the system and vorticity.
Where initially pressure and velocity values are estimated
by algorithm and later pressure-correction equation ∇2p′=1/∆t
(∇·V),
is solved to obtain a corrected value of pressure and velocity field and at the solution convergence is checked . A k-omega SST turbulence model with first order accuracy in space and time is used to solve Turbulence Kinetic Energy (k)
and Specific Dissipation Rate (ω)
[4] . In present study, one assumption in selection of boundary conditions is that no external force factors are included in the system (sea waves effect).
Results
In all 4 CFD simulations cases are compared at different depths (-1, -3, -6, -9), respectively. Velocity and mixing
pattern show hydrodynamic difference in flow field across the tank.
Out of 4 analyzed cases, the best hydrodynamics in the system (Figure 1a and 1b) was achieved by case 1 set-up (Table I), while the large variation in hydrodynamics was observed between cases . Observed differences in velocities between CFD models and empirical measurements were further investigated.
Discussion and conclusion
To investigate the hydrodynamics of large systems, we compared different design and Computational models , respectively. The understanding of two
inlets flow patterns
plus extra water dividers installments behind all 6 pipe columns could be performed more efficiently and relatively cheaper with the help of CFD modeling and simulations. In order to achieve a good qualitive and more reliable results, it is very important to develop a Solid CFD bench model. In this study, initially
we developed a reliable CFD bench model and then examined the complete flow patterns
for
4 cases with different inlet nozzles and water dividers orientation angles
(Table I) . Large differences between the flow patterns are observed among various cases, based on their inlet orientation angle change. This factor has large impact on mixing and velocity factor across the system, which in turn effect water quality for optimal fish growth/welfare, health performance and particle removal.
This study shows that further optimization of the system set-up is achievable with the help of CFD modeling.
Acknowledgements
This study was funded by CtrlAQUA SFI ( Research Council of Norway , project nr. 237856/O30).
References
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[2]
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