Department of Civil and Environmental Engineering, University of California, Los Angeles, Los Angeles, California.
Water Environ Res. 2019 Feb;91(2):101-110. doi: 10.1002/wer.1003. Epub 2019 Jan 18.
Secondary settling tanks (SSTs), also known as secondary sedimentation tanks or secondary clarifiers, are a basic yet complicated process in a biological water resource recovery facility. In order to understand and improve SST performance, computational fluid dynamics methods have been employed over the last 30 years. In the present investigation, a Fluent-based two-dimensional axisymmetric numerical model is applied to understand the effects of the buoyancy term (G ) in the turbulent kinetic energy (TKE) equation and two model parameters (the coefficient of buoyancy term (C ) in the turbulent dissipation rate equation and the turbulent Schmidt number (σ ) in the sludge transport equation) on the performance of an SST. The results show that the hydrodynamics can only be correctly predicted by buoyancy-coupled TKE equation, unless the mixed liquor suspended solids is low and sludge settling velocity is extremely high. When the field observations show the SST is operating well, the buoyancy-decoupled TKE equation predicts the correct result, but the buoyancy-decoupled TKE equation may predict failure. Care is required in selecting the correct modeling technique for various conditions. This study provides guidance on how to avoid modeling problems and increase rates of convergence. PRACTITIONER POINTS: C3 can be set to zero to improve rate of convergence and reduce computing time. σc can be used to adjust SBH, when ESS and RAS concentrations are well calibrated to the field data, but the SBH does not fit field observation.
二沉池(SST),又称二次沉淀池或二次澄清池,是生物水资源回收设施中的一个基本而复杂的过程。为了理解和改善 SST 的性能,在过去的 30 年中,已经采用了计算流体动力学方法。在本研究中,应用基于 Fluent 的二维轴对称数值模型来理解在湍流动能(TKE)方程中的浮力项(G)和两个模型参数(在湍流动能耗散率方程中的浮力项系数(C)和在污泥输送方程中的湍流通量数(σ))对 SST 性能的影响。结果表明,除非混合液悬浮固体浓度低且污泥沉降速度极高,否则只有通过浮力耦合的 TKE 方程才能正确预测水动力。当现场观察表明 SST 运行良好时,浮力解耦的 TKE 方程预测了正确的结果,但浮力解耦的 TKE 方程可能会预测失败。在各种条件下选择正确的建模技术时需要谨慎。本研究提供了如何避免建模问题和提高收敛率的指导。