Environmental Engineering Reference
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Secondly, affording shelter from predation implies higher survival at artificial reefs than in
natural habitats, which is supported by some early experimental studies. Hixon & Beets,
(1989) tested two corollaries of the limited shelter hypothesis by conducting the experiments
in Perseverance Bay, St. Thomas, U.S. Virgin Islands. They suggested that artificial reefs
designed for persistent fisheries should include both small holes for small fishes (as refuges
from predation) and large holes for predatory species (as home sites). Leitao, et al (2008)
studied the effect of predation on artificial reef juvenile demersal fish species. The results
indicated that interspecific interactions (predator-prey) are important for conservation and
management and for the evaluation of the long-term effects of reef deployment. Third,
artificial reefs increase the production of natural reef environments by creating vacated
space. Einbinder, et al. (2006) experimentally tested whether artificial reefs change grazing
patterns in their surrounding environment. The results suggest that herbivorous fishes are
attracted to the artificial reefs, creating a zone of increased grazing. It is necessary to
consider their overall influence on their natural surroundings when planning deployment of
artificial reefs. Fourth, artificial reefs have been demonstrated a potential tool for the
restoration of marine habitats. The proper role of artificial habitats in aquatic systems
continues to be a topic of debate among ecological engineers. A detailed description of the
role of artificial reef habitats in the restoration of degraded marine systems has been given
by Seaman, (2007) . Finally, an important aspect of artificial reefs includes the flow pattern
effect. Recent studies have demonstrated the value of investigating the flow field effect in
and around artificial reefs as a means of identifying their ecological effect on the
proliferation of fishery resources (Lin & Zhang, 2006) . When an artificial reef is deployed at
the bottom of the sea, many kinds of flow patterns characterized by intense velocity
gradients, flow turbulence, vortices, and so on are aroused and develop depending on the
shapes, sizes and the different arrangements of artificial reefs. Zhang & Sun, (2001)
discovered that considerable local upwelling current fields and eddy current fields are
generated at the front and the back of artificial reefs, respectively. Upwelling enhances
biological productivity, which feeds fisheries, and it is richer in nutrients. Meanwhile, a
geometric shaded area is distributed within and around the reef. Consequently, algae and
plankton thrive, and therefore, the reefs not only provide a shelter from predators but also
an abundant food source.
In view of the above, flow field research is key to the research of artificial reef eco-efficiency.
Liu, et al. (2009) simulated the flow fields around solid artificial reefs models with different
shapes, including cubes, pyramids and triangular prisms, by mean of wind tunnel
experiments and numerical simulation methods. Su, et al. (2007) conducted particle image
velocimetry (PIV) measurements to study the flow patterns within and around an artificial
reef. The PIV results were then used to verify the numerical results obtained from finite
volume method (FVM) simulations. In engineering practice, the stability of artificial reefs is
an important issue in preventing the failure of reef units due to current action. The stability
of reefs and sediment erosion on the bottom of artificial reefs rely on the interactions among
the current-bottom material-reef system. In this study, the flow field patterns around a
hollow cube artificial reef were investigated by employing FVM simulation with
unstructured tetrahedral grids for solution of three-dimensional incompressible Reynolds-
averaged Navier-Stokes equations. A renormalization group (RNG) k-ε turbulence model
was embedded in the Navier-Stokes solver. The pressure and velocity coupling was solved
with the SIMPLEC algorithm at each time step. To validate simulation predictions, non-
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