The objective of this research is to develop models that represent the effects of light and algae and incorporate these effects within a computational fluid dynamics (CFD) model of a photobioreactor (PBR). Several factors, including nutrient availability, carbon dioxide concentration, light intensity, and frequency of high and low light intensity periods, affect the efficiency of biomass yield within a photobioreactor. However, even with a general understanding of the affecting factors, scaling up of photobioreactors from a laboratory to a commercial level exist and provide a challenge concerning efficiency. The development and execution of an integrated light, algae, and CFD model can provide insight into more cost and time efficient configurations of PBRs. In depth CFD studies have been used to predict thermal-fluid effects, including bubble-liquid interaction and temperature profiles; however, studies concerning algae-liquid interactions appear sparsely. In order to better understand up-scaling issues, new modifications of previous CFD methods incorporate an algae particle tracking method, as well as light modeling. The particle tracking method considers the individual algae cell as a volume-less and mass-less particle that follows the liquid velocity profiles within the PBR. The light model takes into account algal concentration as well as bubble location and bubble concentration. The integration of the models allows for the average intensity of light experienced by an algae cell to be numerically estimated, alongside the frequency of light and dark periods the particle experiences. The long term goal of this research is to develop an algae growth model that incorporates light intensity and the flashing light effect. The present research is a continuum of previous work aimed at pursuing the optimum design of a column PBR which is commercially viable and effective at producing algal biofuels and bioproducts.

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