Numerical Simulation of UV Disinfection Reators: Impact of Fluence Rate Distribution and Turbulence Modeling

UV disinfection;turbulence model;Fluence rate model;CFD

Liu, Dong ; David Ollis, Committee Member,Francis de los Reyes III, Committee Member,Detlef Knappe, Committee Member,Joel. J. Ducoste, Committee Chair,Liu, Dong ; David Ollis ; Committee Member ; Francis de los Reyes III ; Committee Member ; Detlef Knappe ; Committee Member ; Joel. J. Ducoste ; Committee Chair

University:North Carolina State University

关键词: UV disinfection;    turbulence model;    Fluence rate model;    CFD;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/3523/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

This study investigated two most important parts for numerical simulation of UV disinfection reactors: fluence rate distribution and turbulence modeling. In the first part, the evaluation of alternative fluence rate distribution models, including Multiple Point Source Summation (MPSS), Multiple Segment Source Summation (MSSS), Line Source Integration (LSI), Modified LSI (RAD-LSI), Discrete Ordinate (DO) and View Factor models, has been performed. As part of the evaluation, a complete MSSS model, which accounts for the quartz sleeve thickness while calculating refraction angles, was developed. In addition, a simple attenuation factor approach was introduced to integrate the physics of reflection, refraction, and absorption effects into the LSI model. In the second part, six turbulence models, including standard k-e, k-e RNG, k-w(88), revised k-w(98), Reynolds Stress Transport (RST), and Two-Fluid (TF), were investigated and applied to the flow field simulation of a closed conduit polychromatic UV reactor. Predicted flow field, kinetic turbulence energy, and turbulence energy dissipation rate were compared with the experimental data from a Particle Image Velocimetry (PIV) analysis at four locations close to the UV lamps. All of the predicted flow fields were combined with a MSSS fluence rate model and three different microbial inactivation kinetic models to simulate the disinfection process at two UV lamp power conditions. Microbial transport was simulated using the Lagrangian based particle tracking method. As part of the turbulence model analysis, the fluence distribution and the effluent microbial inactivation predicted by all turbulence models were reported. In the fluence rate distribution study, the results showed that models, neglecting the effects of refraction, deviated significantly from the experimental data. In addition, the MSSS approach or models that incorporated the MSSS concept were found to agree well with the experimentally measured fluence rate distribution. Moreover, little difference was found between the results of the MSSS model with quartz sleeve thickness and UVCalc_3D, which does not model the quartz sleeve thickness in the refraction angle calculation but uses a factor to account for the effects of the quartz sleeve on the fluence rate. The attenuation factor combined with LSI model was found to match the MSSS model predictions, while reducing the computational cost. The results show that the fluence distributions and the effluent inactivation level were sensitive to the turbulence model selection. The level of sensitivity was a function of the operating conditions and the UV response kinetics of the microorganisms. In the turbulence model study, the results show that operating conditions that produce higher log inactivation or microorganisms with higher sensitivity to UV will show greater sensitivity to the turbulence model selection. In addition, a broader fluence distribution was found with turbulence models that predicted a larger wake region behind the lamps and greater turbulent mixing characteristics. Overall, the results of this study suggest that numerical simulations of UV processes using CFD should be initially validated with experimental bioassay tests prior to its use as a tool for understanding and evaluating the UV disinfection performance for a specific reactor design and target microorganism.