The improved design of aquaculture systems is needed facing the demand for increased production as well as increased concern of fish wellbeing. Here, we make another step towards use of computational fluid dynamics (CFD) for design and operation of recirculating aquaculture systems (RAS), see e.g. [1]. The proposed CFD based methodology allows the modeler (the designer) to have an overall picture about hydrodynamic conditions within the fish tank and to manipulate (in silico ) all key phenomena involved, both physical (i.e., the tank geometry and operating conditions) and biological (fish density) . Further optimization of the tank hydrodynamics, mainly to ensure the optimal rearing conditions of fish larvae and fast biosolids removal, is being expected.
The scale effect on larvae performance, i.e., the effect of different tank volumes on European sea bass Dicentrarchus labrax growth, survival and stress variables, was investigated by Lika et al. [2] . The cornerstone in modeling this phenomenon is consisting in fact that certain level of t urbulence can enhance the feeding rate of the larvae, see [3]. However, the relation between feeding rate and the turbulence intensity is dome-shaped, see [4 ] and references within there. Consequently, it exists an optimal setting of the operating conditions (water recirculation rate or the liquid velocity in the inlet and all details concerning the boundary conditions) for a pre-established tank geometry, providing the optimal biological performance of larvae .
The big challenge resides in the optimization of both the biological performance of fish larvae and the self-cleaning capacity of RAS . Therefore, some assumptions must be undertaken. First, we can limit ourselves to maintain a sufficient water quality index (i.e., by defining a limit superior of the hydraulic retention time) while looking for an optimal biological performance. Second, the influence of fish larvae on the flow field can be neglected, i.e., we deal with the one liquid phase in CFD simulations only. Obviously , this is not the first work where t he CFD i s used for RAS hydrodynamics simulation. Nevertheless, to the best of our knowledge, there are only few works deali ng with the multicriterial optimization with the CFD model, based on ANSYS Fluent code [5], embedded . Let us remind that experiments at laboratory scale or at field are laborious and time consuming. In this context, it becomes of utmost importance to develop a reliable CFD based methodology being able to provide reliable description of the flow field within RAS tank and an ease of performing a large range of parametric studies for optimization of both RAS scale and operating conditions.
Acknowledgements
The work of Stepan Papacek was supported by the MEYS of the Czech Republic - projects CENAKVA (No. CZ.1.05/2.1.00/01.0024), CENAKVA II (No. LO1205 under the NPU I program) and The CENAKVA Centre Development (No. CZ.1.05/2.1.00/19.0380). The work of Karel Petera was supported by the long-term strategic development financing of the CTU Prague.
References
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[2] Lika , K.; Pavlidis , M.; Mitrizakis , N.; Samaras , A.; Papandroulakis , N. Do experimental units of different scale affect the biological performance of European sea bass Dicentrarchus labrax larvae? Journal of Fish Biology 2015 , 86 (4) , 1271–1285.
[3] Kristiansen, T.; Vollset , K.W.; Sundby , S.; Vikebø, F. Turbulence enhances feeding of larval cod at low prey densities. ICES J. Mar. Sci. 2014, 71, 2515–2529.
[4] Pécseli , H.L.; Trulsen , J.K.; Stiansen , J.E.; Sundby, S. Feeding of Plankton in a Turbulent Environment: A Comparison of Analytical and Observational Results Covering Also Strong Turbulence. Fluids 2020, 5, 37.
[5] ANSYS FLUENT product documentation, http://www.ansys.com/Products/Fluids/ANSYS-Fluent