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The fluid flow environment associated with high Glossosoma abundance is predicted by large-eddy simulation of a natural turbulent open-channel flow. The spatial distribution of Glossosoma was depicted by high resolution physical variables described by fluid flow and streambed topography. Variogram analysis of the streambed topography revealed a characteristic length scale of the streambed of the order 0.2 m over which bed roughness height was correlated. Flow simulation output was spatially and temporally averaged over the streambed characteristic length scale and linked to Glossosoma spatial density. A dimensionless scaling relationship between Glossosoma spatial distribution and streamwise velocity averaged in the longitudinal and transverse direction, spatial velocity fluctuation, and spanwise vorticity from the computational fluid dynamics simulation output explained 79% of the variation in observed dimensionless Glossosoma spatial density. The analysis demonstrated that computational fluid mechanics and high resolution bed topography could be instrumental in predicting benthic macroinvertebrate spatial distribution in streams and rivers. [ABSTRACT FROM AUTHOR]

Low levels of kinetic energy dissipation were successfully generated in a reactor using two submersible speakers. A software programme controlled the amplitude and frequency of the signal fed to each speaker and achieved good repeatability of flow conditions. The flow reactor had a near isotropic flow regime with a low mean flow, values were calculated from particle image velocimetry measurements. The flow characteristics compared well with grid turbulence reactors, though as no moving parts are present in this reactor design the strain rates were lower compared to oscillating grid set-ups. The low range of Reynolds numbers based on Taylor microscales ( Re λ~0.5–5.9) covered both turbulent and non-turbulent flow regimes. The small-scale fluid motion produced over the entire volume of this reactor makes it suitable for experiments examining the physiological responses of fluid motion on microorganisms. [ABSTRACT FROM AUTHOR]

1. A fluid-flow reactor using submersible speakers was constructed to generate small-scale fluid motion similar to conditions measured in open water environments; flow was quantified by particle image velocimetry. Additionally a Couette-type rotating cylinder was used to generate shear flows; flow was quantified using an optical hotwire probe and torque measurements. Growth rates of the green alga Selenastrum capricornutum were determined from changes in cell counts and viability was tested using the fluorogenic probe fluoresceine diacetate. 2. Evidence that fluid motion directly affects growth rates was obtained as a significant difference between growth in a moving versus non-moving fluid. A near 2-fold increase in growth rate was achieved for an energy dissipation rate of ℇ = 10−7 m2 s−3; a rate common in lakes and oceans. The onset of the viability equilibrium, identified as the day of the test period when the number of active cells equalled non-active cells, was delayed by 2 days for moving fluid conditions as compared with a non-moving fluid. 3. Nutrient uptake was determined by a decrease in the bulk fluid concentration and cellular phosphorus concentration was also estimated. The thickness of the diffusive sublayer surrounding a cell, a zone dominated by molecular diffusion, was estimated. Increasing fluid motion was found to decrease the thickness of this layer. The Sherwood number (ratio of total mass flux to molecular mass flux) showed that advective flux surrounding cells dominated molecular diffusion flux with regard to Péclet numbers (ratio of advective transport to molecular diffusion transport). Fluid motion facilitated uptake rates and resulted in increased growth rates, compared with no-flow conditions. The rate-of-rotation and the rate-of-strain in a moving fluid equally mediated the diffusive sublayer thickness surrounding the cells. Our study demonstrates that small-scale fluid motion mediates algal growth kinetics and therefore should be included in predictive models for algal blooms. [ABSTRACT FROM AUTHOR]