CFD Simulations of Mixing Dynamics and Photobioreaction Kinetics in Miniature Bioreactors under Transitional Flow Regimes

Miniaturised stirred bioreactors are crucial in high-throughput bioprocesses for their simplicity and cost-effectiveness. To accelerate process optimisation in chemical and bioprocess industries, models that integrate CFD-predicted flow fields with (bio)reaction kinetics are needed. However, conventional two-step coupling methods, which freeze flow fields after solving hydrodynamics and then address (bio)reaction transport, face numerical challenges in miniaturised systems due to unsteady radial flows, recirculation zones, and secondary vortices. These flow fluctuations prevent steady-state hydrodynamic convergence. This study addresses these challenges by time-averaging the RANS solutions of the transitional SST model to achieve statistical hydrodynamic convergence. This method is particularly effective for internal flow problems at low to midrange Reynolds numbers (<10,000), typical of transitional regimes. Following this, photo-bioreaction transport models were solved using these converged fields, considering the bioreactor�s directional illumination and curvature. Applied to a 0.7 L Schott bottle photobioreactor mechanically mixed by a magnetic stirrer (100-500 rpm), the model predictions were thoroughly validated against tracer dye experiments and Rhodopseudomonas palustris biomass growth data. It accurately predicted swirly vortex fields at 500 rpm with a 7% error margin and the biomass growth profiles align to literature datasets. However, parallel computing efficiency did not scale linearly from 16 to 32 processor cores (4G of RAM/core), making time-averaging computationally expensive for simulating scale-up bioreactors. Improved bioreactor mixing influenced cell light/dark cycles and enhanced biomass productivity but stirring speeds above 300 rpm required increased light intensity (>100 W/m�) due to light limitation. This model provides a framework for optimising stirring speeds and refining operational parameters, aiding in the scale-up and scale-down of bioprocesses.